Cell cycle population effects in perturbation studies

Growth condition perturbation or gene function disruption are commonly used strategies to study cellular systems. Although it is widely appreciated that such experiments may involve indirect effects, these frequently remain uncharacterized. Here, analysis of functionally unrelated Saccharyomyces cerevisiae deletion strains reveals a common gene expression signature. One property shared by these strains is slower growth, with increased presence of the signature in more slowly growing strains. The slow growth signature is highly similar to the environmental stress response (ESR), an expression response common to diverse environmental perturbations. Both environmental and genetic perturbations result in growth rate changes. These are accompanied by a change in the distribution of cells over different cell cycle phases. Rather than representing a direct expression response in single cells, both the slow growth signature and ESR mainly reflect a redistribution of cells over different cell cycle phases, primarily characterized by an increase in the G1 population. The findings have implications for any study of perturbation that is accompanied by growth rate changes. Strategies to counter these effects are presented and discussed.

Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases

BACKGROUND AND OBJECTIVES

This study was designed to identify and validate gene signatures that can predict disease free survival (DFS) in patients undergoing a radical resection for their colorectal liver metastases (CRLM).

METHODS

Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France) and the University Medical Center Utrecht (The Netherlands). Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort.

RESULT

No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort.

CONCLUSIONS

No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

Functional overlap and regulatory links shape genetic interactions between signaling pathways

To understand relationships between phosphorylation-based signaling pathways, we analyzed 150 deletion mutants of protein kinases and phosphatases in S. cerevisiae using DNA microarrays. Downstream changes in gene expression were treated as a phenotypic readout. Double mutants with synthetic genetic interactions were included to investigate genetic buffering relationships such as redundancy. Three types of genetic buffering relationships are identified: mixed epistasis, complete redundancy, and quantitative redundancy. In mixed epistasis, the most common buffering relationship, different gene sets respond in different epistatic ways. Mixed epistasis arises from pairs of regulators that have only partial overlap in function and that are coupled by additional regulatory links such as repression of one by the other. Such regulatory modules confer the ability to control different combinations of processes depending on condition or context. These properties likely contribute to the evolutionary maintenance of paralogs and indicate a way in which signaling pathways connect for multiprocess control.

Improved genome-wide localization by ChIP-chip using double-round T7 RNA polymerase-based amplification

Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a powerful technique to detect in vivo protein–DNA interactions. Due to low yields, ChIP assays of transcription factors generally require amplification of immunoprecipitated genomic DNA. Here, we present an adapted linear amplification method that involves two rounds of T7 RNA polymerase amplification (double-T7). Using this we could successfully amplify as little as 0.4 ng of ChIP DNA to sufficient amounts for microarray analysis. In addition, we compared the double-T7 method to the ligation-mediated polymerase chain reaction (LM-PCR) method in a ChIP-chip of the yeast transcription factor Gsm1p. The double-T7 protocol showed lower noise levels and stronger binding signals compared to LM-PCR. Both LM-PCR and double-T7 identified strongly bound genomic regions, but the double-T7 method increased sensitivity and specificity to allow detection of weaker binding sites.

Deletion of chromosomal region 6q14-16 in prostate cancer

A detailed analysis of chromosome 6 in DNAs from prostate cancers was performed, to define a region for subsequent search for cancer genes. DNA from 4 prostate cancer cell lines and 11 xenografts was used for CGH and whole-chromosome allelotyping with polymorphic microsatellite markers. Loss of proximal 6q was studied in more detail by high-density allelotyping of xenografts, cell lines and 19 prostate tumour specimens from TURP. Seven of 15 xenografts and cell lines showed deletion of proximal 6q by CGH. Gain of 6q was found in 2 samples. Six samples showed 6p gain, and 1 had 6p loss. Allelotyping results were consistent with CGH data in 11 of 15 DNAs. In LNCaP and DU145 cells, CGH showed 6p loss and 6q loss, respectively, but 2 allelic bands were detected for many polymorphic markers on these chromosome arms. These apparent discrepancies might be explained by aneuploidy. In cell line TSU, allelotyping demonstrated chromosome 6 deletion, which was not clearly detected by CGH, indicating loss of 1 copy of chromosome 6 followed by gain of the retained copy during progressive tumour growth. Loss of heterozygosity was detected in 9 of 19 TURP specimens. Combining all data, we found a common minimal region of loss at 6q14-16 with a length of 8.6 Mbp flanked by markers D6S1609 and D6S417. One hundred and twenty-three STSs, ESTs, genes and candidate genes mapping in this interval were used to screen xenografts and cell lines for HDs, but none was detected. In summary, chromosome region 6q14-16 was deleted in approximately 50% of the prostate cancer specimens analysed. The high percentage of loss underscores the importance of genes within this region in prostate cancer growth.