De novo expression of gastrokines in pancreatic precursor lesions impede the development of pancreatic cancer.

Molecular events occurring in stepwise progression from pre-malignant lesions (pancreatic intraepithelial neoplasia; PanIN) to the development of pancreatic ductal adenocarcinoma (PDAC) are poorly understood. Thus, characterization of early PanIN lesions may reveal markers that can help in diagnosing PDAC at an early stage and allow understanding the pathology of the disease. We performed the molecular and histological assessment of patient-derived PanINs, tumor tissues and pancreas from mouse models with PDAC (KC mice that harbor K-RAS mutation in pancreatic tissue), where we noted marked upregulation of gastrokine (GKN) proteins. To further understand the role of gastrokine proteins in PDAC development, GKN-deficient KC mice were developed by intercrossing gastrokine-deficient mice with KC mice. Panc-02 (pancreatic cancer cells of mouse origin) were genetically modified to express GKN1 for further in vitro and in vivo analysis. Our results show that gastrokine proteins were absent in healthy pancreas and invasive cancer, while its expression was prominent in low-grade PanINs. We could detect these proteins in pancreatic juice and serum of KC mice. Furthermore, accelerated PanIN and tumor development were noted in gastrokine deficient KC mice. Loss of gastrokine 1 protein delayed apoptosis during carcinogenesis leading to the development of desmoplastic stroma while loss of gastrokine 2 increased the proliferation rate in precursor lesions. In summary, we identified gastrokine proteins in early pancreatic precursor lesions, where gastrokine proteins delay pancreatic carcinogenesis.

Manual microdissection combined with antisense RNA-longSAGE for the analysis of limited cell numbers.

Establishing a gene expression profile of defined subtypes of cells within an organ is still challenging because it frequently requires microdissection and subsequent amplification of the limited amount of messenger RNA (mRNA) isolated from the microdissected tissue in order to be able to proceed with comprehensive gene expression analyses via microarray or serial analysis of gene expression (SAGE) technology. Here we describe a manual microdissection strategy for the isolation of high-quality RNA. Furthermore, a strategy for combining linear amplification of RNA with longSAGE is described that allows the use of antisense RNA (aRNA) generated via the well-established linear amplification of RNA procedure together with the conventional SAGE or longSAGE technology.

aRNA-LongSAGE: SAGE with antisense RNA.

In order to generate serial analysis of gene expression (SAGE) libraries from very small samples such as microdissected cells, the starting material must first be amplified via PCR or linear amplification of RNA. In microarray experiments, it has been shown that linear amplification of RNA can be used to generate reliable gene expression profiles and leads to the detection of expression differences that are not seen with nonamplified starting material. As the product of the amplification is amplified antisense RNA (aRNA), linear amplification of RNA cannot be used in combination with the conventional SAGE protocol. The aRNA-LongSAGE protocol described herein is an adaptation of the MicroSAGE protocol to the use of aRNA as starting material.

Discarding duplicate ditags in LongSAGE analysis may introduce significant error.

BACKGROUND: During gene expression analysis by Serial Analysis of Gene Expression (SAGE), duplicate ditags are routinely removed from the data analysis, because they are suspected to stem from artifacts during SAGE library construction. As a consequence, naturally occurring duplicate ditags are also removed from the analysis leading to an error of measurement. RESULTS: An algorithm was developed to analyze the differential occurrence of SAGE tags in different ditag combinations. Analysis of a pancreatic acinar cell LongSAGE library showed no sign of a general amplification bias that justified the removal of all duplicate ditags. Extending the analysis to 10 additional LongSAGE libraries showed no justification for removal of all duplicate ditags either. On the contrary, while the error introduced in original SAGE by removal of naturally occurring duplicate ditags is insignificant, it leads to an error of up to 3 fold in LongSAGE. However, the algorithm developed for the analysis of duplicate ditags was able to identify individual artifact ditags that originated from rare nucleotide variations of tags and vector contamination. CONCLUSION: The removal of all duplicate ditags was unfounded for the datasets analyzed and led to large errors. This may also be the case for other LongSAGE datasets already present in databases. Analysis of the ditag population, however, can identify artifact tags that should be removed from analysis or have their tag count adjusted.

aRNA-longSAGE: a new approach to generate SAGE libraries from microdissected cells.

Large-scale gene expression analyses of microdissected primary tissue are still difficult because generally only a limited amount of mRNA can be obtained from microdissected cells. The introduction of the T7-based RNA amplification technique was an important step to reduce the amount of RNA needed for such analyses. This amplification technique produces amplified antisense RNA (aRNA), which so far has precluded its direct use for serial analysis of gene expression (SAGE) library production. We describe a method, termed 'aRNA-longSAGE', which is the first to allow the direct use of aRNA for standard longSAGE library production. The aRNA-longSAGE protocol was validated by comparing two aRNA-longSAGE libraries with two Micro-longSAGE libraries that were generated from the same RNA preparations of two different cell lines. Using a conservative validation approach, we were able to verify 68% of the differentially expressed genes identified by aRNA-longSAGE. Furthermore, the identification rate of differentially expressed genes was roughly twice as high in our aRNA-longSAGE libraries as in the standard Micro-longSAGE libraries. Using our validated aRNA-longSAGE protocol, we were able to successfully generate longSAGE libraries from as little as 40 ng of total RNA isolated from 2000-3000 microdissected pancreatic ductal epithelial cells or cells from pancreatic intraepithelial neoplasias.