Sander R. van Hooff

The specificity and topology of chromatin interaction pathways in yeast

Packaging of DNA into chromatin has a profound impact on gene expression. To understand how changes in chromatin influence transcription, we analyzed 165 mutants of chromatin machinery components in Saccharomyces cerevisiae. mRNA expression patterns change in 80% of mutants, always with specific effects, even for loss of widespread histone marks. The data are assembled into a network of chromatin interaction pathways. The network is function based, has a branched, interconnected topology, and lacks strict one-to-one relationships between complexes. Chromatin pathways are not separate entities for different gene sets, but share many components. The study evaluates which interactions are important for which genes and predicts additional interactions, for example between Paf1C and Set3C, as well as a role for Mediator in subtelomeric silencing. The results indicate the presence of gene-dependent effects that go beyond context-dependent binding of chromatin factors and provide a framework for understanding how specificity is achieved through regulating chromatin.

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.

Large-Scale Genetic Perturbations Reveal Regulatory Networks and an Abundance of Gene-Specific Repressors

To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.

Two distinct repressive mechanisms for histone 3 lysine 4 methylation through promoting 3'-end antisense transcription.

Histone H3 di- and trimethylation on lysine 4 are major chromatin marks that correlate with active transcription. The influence of these modifications on transcription itself is, however, poorly understood. We have investigated the roles of H3K4 methylation in Saccharomyces cerevisiae by determining genome-wide expression-profiles of mutants in the Set1 complex, COMPASS, that lays down these marks. Loss of H3K4 trimethylation has virtually no effect on steady-state or dynamically-changing mRNA levels. Combined loss of H3K4 tri- and dimethylation results in steady-state mRNA upregulation and delays in the repression kinetics of specific groups of genes. COMPASS-repressed genes have distinct H3K4 methylation patterns, with enrichment of H3K4me3 at the 3′-end, indicating that repression is coupled to 3′-end antisense transcription. Further analyses reveal that repression is mediated by H3K4me3-dependent 3′-end antisense transcription in two ways. For a small group of genes including PHO84, repression is mediated by a previously reported trans-effect that requires the antisense transcript itself. For the majority of COMPASS-repressed genes, however, it is the process of 3′-end antisense transcription itself that is the important factor for repression. Strand-specific qPCR analyses of various mutants indicate that this more prevalent mechanism of COMPASS-mediated repression requires H3K4me3-dependent 3′-end antisense transcription to lay down H3K4me2, which seems to serve as the actual repressive mark. Removal of the 3′-end antisense promoter also results in derepression of sense transcription and renders sense transcription insensitive to the additional loss of SET1. The derepression observed in COMPASS mutants is mimicked by reduction of global histone H3 and H4 levels, suggesting that the H3K4me2 repressive effect is linked to establishment of a repressive chromatin structure. These results indicate that in S. cerevisiae, the non-redundant role of H3K4 methylation by Set1 is repression, achieved through promotion of 3′-end antisense transcription to achieve specific rather than global effects through two distinct mechanisms.

Adaptable gene-specific dye bias correction for two-channel DNA microarrays

DNA microarray technology is a powerful tool for monitoring gene expression or for finding the location of DNA‐bound proteins. DNA microarrays can suffer from gene‐specific dye bias (GSDB), causing some probes to be affected more by the dye than by the sample. This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. GSDB is not corrected by conventional normalization and has been difficult to address systematically because of its variance. We show that GSDB is influenced by label incorporation efficiency, explaining the variation of GSDB across different hybridizations. A correction method (Gene‐ And Slide‐Specific Correction, GASSCO) is presented, whereby sequence‐specific corrections are modulated by the overall bias of individual hybridizations. GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip. A sequence‐based model is also presented, which predicts which probes will suffer most from GSDB, useful for microarray probe design and correction of individual hybridizations. Software implementing the method is publicly available.

Validation of a Gene Expression Signature for Assessment of Lymph Node Metastasis in Oral Squamous Cell Carcinoma

PURPOSE Current assessment of lymph node metastasis in patients with head and neck squamous cell carcinoma is not accurate enough to prevent overtreatment. The aim of this study was validation of a gene expression signature for distinguishing metastasizing (N+) from nonmetastasizing (N0) squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC) in a large multicenter cohort, using a diagnostic DNA microarray in a Clinical Laboratory Improvement Amendments/International Organization for Standardization-approved laboratory. METHODS A multigene signature, previously reported as predictive for the presence of lymph node metastases in OSCC and OPSCC, was first re-evaluated and trained on 94 samples using generic, whole-genome, DNA microarrays. Signature genes were then transferred to a dedicated diagnostic microarray using the same technology platform. Additional samples (n=222) were collected from all head and neck oncologic centers in the Netherlands and analyzed with the diagnostic microarray. Human papillomavirus status was determined by real-time quantitative polymerase chain reaction. RESULTS The negative predictive value (NPV) of the diagnostic signature on the entire validation cohort (n=222) was 72%. The signature performed well on the most relevant subset of early-stage (cT1-T2N0) OSCC (n=101), with an NPV of 89%. CONCLUSION Combining current clinical assessment with the expression signature would decrease the rate of undetected nodal metastases from 28% to 11% in early-stage OSCC. This should be sufficient to enable clinicians to refrain from elective neck treatment. A new clinical decision model that incorporates the expression signature is therefore proposed for testing in a prospective study, which could substantially improve treatment for this group of patients.

Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma

Oral squamous-cell carcinomas arise in mucosal linings of the oral cavity and frequently metastasise to regional lymph nodes in the neck. The presence of nodal metastases is a determinant of prognosis and clinical management. The neck is staged by palpation and imaging, but accuracy of these techniques to detect small metastases is low. In general, 30-40% of patients will have occult nodal disease and will develop clinically detectable lymph-node metastases when the neck is left untreated. The choice at present is either elective treatment or careful observation followed by treatment of the neck in patients who develop manifest metastases. These unsatisfying therapeutic options have been the subject of debate for decades. Recent developments in staging of the neck, including expression profiling and sentinel lymph-node biopsy, will allow more personalised management of the neck.

An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention.

Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2

Gene expression profiling revealed a strong signature predicting lymph node metastases in oral squamous cell carcinoma (OSCC). Four of the most predictive genes are secretory leukocyte protease inhibitor (SLPI), lipocalin‐2 (LCN2), thrombospondin‐2 (THBS2), and tumor‐associated calcium signal transducer 2 (TACSTD2). This study correlates their protein expression with lymph node metastases, overall survival (OS), and disease‐specific survival (DSS).

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.