Publications by authors named "Patrick Kemmeren"

47 Publications

Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology.

NPJ Precis Oncol 2021 Mar 2;5(1):15. Epub 2021 Mar 2.

Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.

Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.
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http://dx.doi.org/10.1038/s41698-021-00155-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925608PMC
March 2021

Suboptimal Global Transcriptional Response Increases the Harmful Effects of Loss-of-Function Mutations.

Mol Biol Evol 2021 03;38(3):1137-1150

HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.

The fitness impact of loss-of-function mutations is generally assumed to reflect the loss of specific molecular functions associated with the perturbed gene. Here, we propose that rewiring of the transcriptome upon deleterious gene inactivation is frequently nonspecific and mimics stereotypic responses to external environmental change. Consequently, transcriptional response to gene deletion could be suboptimal and incur an extra fitness cost. Analysis of the transcriptomes of ∼1,500 single-gene deletion Saccharomyces cerevisiae strains supported this scenario. First, most transcriptomic changes are not specific to the deleted gene but are rather triggered by perturbations in functionally diverse genes. Second, gene deletions that alter the expression of dosage-sensitive genes are especially harmful. Third, by elevating the expression level of downregulated genes, we could experimentally mitigate the fitness defect of gene deletions. Our work shows that rewiring of genomic expression upon gene inactivation shapes the harmful effects of mutations.
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http://dx.doi.org/10.1093/molbev/msaa280DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947755PMC
March 2021

Genome-wide off-rates reveal how DNA binding dynamics shape transcription factor function.

Mol Syst Biol 2020 10;16(10):e9885

Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.

Protein-DNA interactions are dynamic, and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here, we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide range, varying between 4.2 and 33 min. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.
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http://dx.doi.org/10.15252/msb.20209885DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7586999PMC
October 2020

An Optimized Chromatin Immunoprecipitation Protocol for Quantification of Protein-DNA Interactions.

STAR Protoc 2020 Jun 19;1(1):100020. Epub 2020 Jun 19.

Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, the Netherlands.

Transcription factors are important regulators of cell fate and function. Knowledge about where transcription factors are bound in the genome is crucial for understanding their function. A common method to study protein-DNA interactions is chromatin immunoprecipitation (ChIP). Here, we present a revised ChIP protocol to determine protein-DNA interactions for the yeast . We optimized several aspects of the procedure, including cross-linking and quenching, cell lysis, and immunoprecipitation steps. This protocol facilitates sensitive and reproducible quantitation of protein-DNA interactions. For complete details on the use and execution of this protocol, please refer to (de Jonge et al., 2019).
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http://dx.doi.org/10.1016/j.xpro.2020.100020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7357673PMC
June 2020

An organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity.

Nat Commun 2020 03 11;11(1):1310. Epub 2020 Mar 11.

Oncode Institute, Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands.

Kidney tumours are among the most common solid tumours in children, comprising distinct subtypes differing in many aspects, including cell-of-origin, genetics, and pathology. Pre-clinical cell models capturing the disease heterogeneity are currently lacking. Here, we describe the first paediatric cancer organoid biobank. It contains tumour and matching normal kidney organoids from over 50 children with different subtypes of kidney cancer, including Wilms tumours, malignant rhabdoid tumours, renal cell carcinomas, and congenital mesoblastic nephromas. Paediatric kidney tumour organoids retain key properties of native tumours, useful for revealing patient-specific drug sensitivities. Using single cell RNA-sequencing and high resolution 3D imaging, we further demonstrate that organoid cultures derived from Wilms tumours consist of multiple different cell types, including epithelial, stromal and blastemal-like cells. Our organoid biobank captures the heterogeneity of paediatric kidney tumours, providing a representative collection of well-characterised models for basic cancer research, drug-screening and personalised medicine.
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http://dx.doi.org/10.1038/s41467-020-15155-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066173PMC
March 2020

Kaposiform hemangioendothelioma and tufted angioma - (epi)genetic analysis including genome-wide methylation profiling.

Ann Diagn Pathol 2020 Feb 10;44:151434. Epub 2019 Dec 10.

Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands; Princess Màxima Center for Pediatric Oncology, Utrecht, the Netherlands. Electronic address:

Kaposiform hemangioendothelioma (KHE) is a locally aggressive vascular condition of childhood and is clinicopathologically related to tufted angioma (TA), a benign skin lesion. Due to their rarity molecular data are scarce. We investigated 7 KHE and 3 TA by comprehensive mutational analysis and genome-wide methylation profiling and compared the clustering, also with vascular malformations. Lesions were from 7 females and 3 males. The age range was 2 months to 9 years with a median of 10 months. KHEs arose in the soft tissue of the thigh (n = 2), retroperitoneum (n = 1), thoracal/abdominal (n = 1), supraclavicular (n = 1) and neck (n = 1). One patient presented with multiple lesions without further information. Two patients developed a Kasabach-Merritt phenomenon. TAs originated in the skin of the shoulder (n = 2) and nose/forehead (n = 1). Of the 5 KHEs and 2 TAs investigated by DNA sequencing, one TA showed a hot spot mutation in NRAS, and one KHE a mutation in RAD50. Unsupervised hierarchical clustering analysis indicated a common methylation pattern of KHEs and TAs, which separated from the homogeneous methylation pattern of vascular malformations. In conclusion, methylation profiling provides further evidence for KHEs and TAs potentially forming a spectrum of one entity. Using next generation sequencing, heterogeneous mutations were found in a subset of cases (2/7) without the presence of GNA14 mutations, previously reported in KHE and TA.
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http://dx.doi.org/10.1016/j.anndiagpath.2019.151434DOI Listing
February 2020

A framework for exhaustive modelling of genetic interaction patterns using Petri nets.

Bioinformatics 2020 04;36(7):2142-2149

Department of Computer Science, Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands.

Motivation: Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs.

Results: In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets.

Availability And Implementation: The framework is available at http://www.ibi.vu.nl/programs/ExhMod.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz917DOI Listing
April 2020

DNA Methylation Profiling Identifies Distinct Clusters in Angiosarcomas.

Clin Cancer Res 2020 01 27;26(1):93-100. Epub 2019 Sep 27.

Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.

Purpose: DNA methylation profiling has previously uncovered biologically and clinically meaningful subgroups within many tumor types, but was not yet performed in angiosarcoma. Angiosarcoma is a rare sarcoma with very heterogeneous clinical presentations, which may be based on differences in biological background. In this exploratory study, DNA methylation profiling of 36 primary angiosarcoma samples from visceral, deep soft tissue, radiation-induced, and UV-induced localizations was performed.

Experimental Design: Primary angiosarcoma formalin-fixed paraffin-embedded samples from visceral, soft tissue, radiation-induced, and UV-induced origin were collected from a nationwide search for angiosarcoma in the Netherlands. DNA was extracted for methylation profiling with the Illumina Infinium MethylationEPIC array. Quality control assessment and unsupervised hierarchical clustering were performed. Copy-number profiles were generated and analyzed for chromosomal stability. Clinical data were obtained from the Netherlands Cancer Registry.

Results: DNA methylation profiling by unsupervised hierarchical clustering of 36 angiosarcoma samples (6 visceral, 5 soft tissue, 14 radiation-induced, 11 UV-induced) revealed two main clusters (A and B), which were divided into four subclusters. The clusters largely corresponded with clinical subtypes, showing enrichment of UV-induced cases in cluster A1 and radiation-induced cases in cluster A2. Visceral and soft tissue cases almost exclusively fell into cluster B. Cluster A showed significantly increased chromosomal instability and better overall survival (22 vs. 6 months, = 0.046) compared with cluster B.

Conclusions: In this novel methylation profiling study, we demonstrated for the first time four different angiosarcoma clusters. These clusters correlated with clinical subtype, overall survival, and chromosomal stability.
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http://dx.doi.org/10.1158/1078-0432.CCR-19-2180DOI Listing
January 2020

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern.

PLoS Comput Biol 2019 05 13;15(5):e1007061. Epub 2019 May 13.

Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.
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http://dx.doi.org/10.1371/journal.pcbi.1007061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532943PMC
May 2019

Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders.

Genet Med 2019 08 20;21(8):1719-1725. Epub 2018 Dec 20.

Princess Máxima Center for Pediatric Oncology, Bilthoven, The Netherlands.

Purpose: The interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance.

Methods: Facial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population.

Results: We validated our model using images from 71 individuals with Koolen-de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10), PPM1D (p = 4.65 × 10), and PHIP (p = 6.3 × 10). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p < 1.52 × 10).

Conclusion: Our results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.
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http://dx.doi.org/10.1038/s41436-018-0404-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752476PMC
August 2019

The clinical implementation of copy number detection in the age of next-generation sequencing.

Expert Rev Mol Diagn 2018 10 27;18(10):907-915. Epub 2018 Sep 27.

a Princess Máxima Center for Pediatric Oncology , Utrecht , Netherlands.

Introduction: The role of copy number variants (CNVs) in disease is now well established. In parallel NGS technologies, such as long-read technologies, there is continual development and data analysis methods continue to be refined. Clinical exome sequencing data is now a reality for many diagnostic laboratories in both congenital genetics and oncology. This provides the ability to detect and report both SNVs and structural variants, including CNVs, using a single assay for a wide range of patient cohorts. Areas covered: Currently, whole-genome sequencing is mainly restricted to research applications and clinical utility studies. Furthermore, detecting the full-size spectrum of CNVs as well as somatic events remains difficult for both exome and whole-genome sequencing. As a result, the full extent of genomic variants in an individual's genome is still largely unknown. Recently, new sequencing technologies have been introduced which maintain the long-range genomic context, aiding the detection of CNVs and structural variants. Expert commentary: The development of long-read sequencing promises to resolve many CNV and SV detection issues but is yet to become established. The current challenge for clinical CNV detection is how to fully exploit all the data which is generated by high throughput sequencing technologies.
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http://dx.doi.org/10.1080/14737159.2018.1523723DOI Listing
October 2018

Growth condition dependency is the major cause of non-responsiveness upon genetic perturbation.

PLoS One 2017 3;12(3):e0173432. Epub 2017 Mar 3.

Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.

Investigating the role and interplay between individual proteins in biological processes is often performed by assessing the functional consequences of gene inactivation or removal. Depending on the sensitivity of the assay used for determining phenotype, between 66% (growth) and 53% (gene expression) of Saccharomyces cerevisiae gene deletion strains show no defect when analyzed under a single condition. Although it is well known that this non-responsive behavior is caused by different types of redundancy mechanisms or by growth condition/cell type dependency, it is not known what the relative contribution of these different causes is. Understanding the underlying causes of and their relative contribution to non-responsive behavior upon genetic perturbation is extremely important for designing efficient strategies aimed at elucidating gene function and unraveling complex cellular systems. Here, we provide a systematic classification of the underlying causes of and their relative contribution to non-responsive behavior upon gene deletion. The overall contribution of redundancy to non-responsive behavior is estimated at 29%, of which approximately 17% is due to homology-based redundancy and 12% is due to pathway-based redundancy. The major determinant of non-responsiveness is condition dependency (71%). For approximately 14% of protein complexes, just-in-time assembly can be put forward as a potential mechanistic explanation for how proteins can be regulated in a condition dependent manner. Taken together, the results underscore the large contribution of growth condition requirement to non-responsive behavior, which needs to be taken into account for strategies aimed at determining gene function. The classification provided here, can also be further harnessed in systematic analyses of complex cellular systems.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173432PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336285PMC
August 2017

Molecular mechanisms that distinguish TFIID housekeeping from regulatable SAGA promoters.

EMBO J 2017 02 15;36(3):274-290. Epub 2016 Dec 15.

Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands

An important distinction is frequently made between constitutively expressed housekeeping genes versus regulated genes. Although generally characterized by different DNA elements, chromatin architecture and cofactors, it is not known to what degree promoter classes strictly follow regulatability rules and which molecular mechanisms dictate such differences. We show that SAGA-dominated/TATA-box promoters are more responsive to changes in the amount of activator, even compared to TFIID/TATA-like promoters that depend on the same activator Hsf1. Regulatability is therefore an inherent property of promoter class. Further analyses show that SAGA/TATA-box promoters are more dynamic because TATA-binding protein recruitment through SAGA is susceptible to removal by Mot1. In addition, the nucleosome configuration upon activator depletion shifts on SAGA/TATA-box promoters and seems less amenable to preinitiation complex formation. The results explain the fundamental difference between housekeeping and regulatable genes, revealing an additional facet of combinatorial control: an activator can elicit a different response dependent on core promoter class.
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http://dx.doi.org/10.15252/embj.201695621DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286361PMC
February 2017

Proteome-wide Changes in Protein Turnover Rates in C. elegans Models of Longevity and Age-Related Disease.

Cell Rep 2016 09;16(11):3041-3051

Center for Molecular Medicine, Molecular Cancer Research Section, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, the Netherlands. Electronic address:

The balance between protein synthesis and protein breakdown is a major determinant of protein homeostasis, and loss of protein homeostasis is one of the hallmarks of aging. Here we describe pulsed SILAC-based experiments to estimate proteome-wide turnover rates of individual proteins. We applied this method to determine protein turnover rates in Caenorhabditis elegans models of longevity and Parkinson's disease, using both developing and adult animals. Whereas protein turnover in developing, long-lived daf-2(e1370) worms is about 30% slower than in controls, the opposite was observed in day 5 adult worms, in which protein turnover in the daf-2(e1370) mutant is twice as fast as in controls. In the Parkinson's model, protein turnover is reduced proportionally over the entire proteome, suggesting that the protein homeostasis network has a strong ability to adapt. The findings shed light on the relationship between protein turnover and healthy aging.
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http://dx.doi.org/10.1016/j.celrep.2016.08.025DOI Listing
September 2016

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation.

Sci Rep 2016 Mar 4;6:22558. Epub 2016 Mar 4.

Center for Health Informatics and Bioinformatics, New York University Medical Center, New York, New York, USA.

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods' performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost.
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http://dx.doi.org/10.1038/srep22558DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4778024PMC
March 2016

A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions.

BMC Biol 2015 Dec 23;13:112. Epub 2015 Dec 23.

Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands.

Background: Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease.

Results: To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression".

Conclusions: These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.
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http://dx.doi.org/10.1186/s12915-015-0222-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4690272PMC
December 2015

De-novo learning of genome-scale regulatory networks in S. cerevisiae.

PLoS One 2014 12;9(9):e106479. Epub 2014 Sep 12.

Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY, United States of America; Department of Medicine, New York University School of Medicine, New York, NY, United States of America.

De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0106479PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162580PMC
May 2015

The genomic landscape of compensatory evolution.

PLoS Biol 2014 Aug 26;12(8):e1001935. Epub 2014 Aug 26.

Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center, Szeged, Hungary.

Adaptive evolution is generally assumed to progress through the accumulation of beneficial mutations. However, as deleterious mutations are common in natural populations, they generate a strong selection pressure to mitigate their detrimental effects through compensatory genetic changes. This process can potentially influence directions of adaptive evolution by enabling evolutionary routes that are otherwise inaccessible. Therefore, the extent to which compensatory mutations shape genomic evolution is of central importance. Here, we studied the capacity of the baker's yeast genome to compensate the complete loss of genes during evolution, and explored the long-term consequences of this process. We initiated laboratory evolutionary experiments with over 180 haploid baker's yeast genotypes, all of which initially displayed slow growth owing to the deletion of a single gene. Compensatory evolution following gene loss was rapid and pervasive: 68% of the genotypes reached near wild-type fitness through accumulation of adaptive mutations elsewhere in the genome. As compensatory mutations have associated fitness costs, genotypes with especially low fitnesses were more likely to be subjects of compensatory evolution. Genomic analysis revealed that as compensatory mutations were generally specific to the functional defect incurred, convergent evolution at the molecular level was extremely rare. Moreover, the majority of the gene expression changes due to gene deletion remained unrestored. Accordingly, compensatory evolution promoted genomic divergence of parallel evolving populations. However, these different evolutionary outcomes are not phenotypically equivalent, as they generated diverse growth phenotypes across environments. Taken together, these results indicate that gene loss initiates adaptive genomic changes that rapidly restores fitness, but this process has substantial pleiotropic effects on cellular physiology and evolvability upon environmental change. Our work also implies that gene content variation across species could be partly due to the action of compensatory evolution rather than the passive loss of genes.
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http://dx.doi.org/10.1371/journal.pbio.1001935DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144845PMC
August 2014

Cell cycle population effects in perturbation studies.

Mol Syst Biol 2014 Jun 21;10:732. Epub 2014 Jun 21.

Molecular Cancer Research, University Medical Center Utrecht, Utrecht, the Netherlands

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.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4265054PMC
http://dx.doi.org/10.15252/msb.20145172DOI Listing
June 2014

Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors.

Cell 2014 Apr;157(3):740-52

Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht 3584 CG, the Netherlands. Electronic address:

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.
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http://dx.doi.org/10.1016/j.cell.2014.02.054DOI Listing
April 2014

The role of Ctk1 kinase in termination of small non-coding RNAs.

PLoS One 2013 4;8(12):e80495. Epub 2013 Dec 4.

Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands.

Transcription termination in Saccharomyces cerevisiae can be performed by at least two distinct pathways and is influenced by the phosphorylation status of the carboxy-terminal domain (CTD) of RNA polymerase II (Pol II). Late termination of mRNAs is performed by the CPF/CF complex, the recruitment of which is dependent on CTD-Ser2 phosphorylation (Ser2P). Early termination of shorter cryptic unstable transcripts (CUTs) and small nucleolar/nuclear RNAs (sno/snRNAs) is performed by the Nrd1-Nab3-Sen1 (NNS) complex that binds phosphorylated CTD-Ser5 (Ser5P) via the CTD-interacting domain (CID) of Nrd1p. In this study, mutants of the different termination pathways were compared by genome-wide expression analysis. Surprisingly, the expression changes observed upon loss of the CTD-Ser2 kinase Ctk1p are more similar to those derived from alterations in the Ser5P-dependent NNS pathway, than from loss of CTD-Ser2P binding factors. Tiling array analysis of ctk1Δ cells reveals readthrough at snoRNAs, at many cryptic unstable transcripts (CUTs) and stable uncharacterized transcripts (SUTs), but only at some mRNAs. Despite the suggested predominant role in termination of mRNAs, we observed that a CTK1 deletion or a Pol II CTD mutant lacking all Ser2 positions does not result in a global mRNA termination defect. Rather, termination defects in these strains are widely observed at NNS-dependent genes. These results indicate that Ctk1p and Ser2 CTD phosphorylation have a wide impact in termination of small non-coding RNAs but only affect a subset of mRNA coding genes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080495PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851182PMC
September 2014

Yeast glucose pathways converge on the transcriptional regulation of trehalose biosynthesis.

BMC Genomics 2012 Jun 14;13:239. Epub 2012 Jun 14.

Molecular Cancer Research, University Medical Centre Utrecht, Utrecht, the Netherlands.

Background: Cellular glucose availability is crucial for the functioning of most biological processes. Our understanding of the glucose regulatory system has been greatly advanced by studying the model organism Saccharomyces cerevisiae, but many aspects of this system remain elusive. To understand the organisation of the glucose regulatory system, we analysed 91 deletion mutants of the different glucose signalling and metabolic pathways in Saccharomyces cerevisiae using DNA microarrays.

Results: In general, the mutations do not induce pathway-specific transcriptional responses. Instead, one main transcriptional response is discerned, which varies in direction to mimic either a high or a low glucose response. Detailed analysis uncovers established and new relationships within and between individual pathways and their members. In contrast to signalling components, metabolic components of the glucose regulatory system are transcriptionally more frequently affected. A new network approach is applied that exposes the hierarchical organisation of the glucose regulatory system.

Conclusions: The tight interconnection between the different pathways of the glucose regulatory system is reflected by the main transcriptional response observed. Tps2 and Tsl1, two enzymes involved in the biosynthesis of the storage carbohydrate trehalose, are predicted to be the most downstream transcriptional components. Epistasis analysis of tps2Δ double mutants supports this prediction. Although based on transcriptional changes only, these results suggest that all changes in perceived glucose levels ultimately lead to a shift in trehalose biosynthesis.
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http://dx.doi.org/10.1186/1471-2164-13-239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472246PMC
June 2012

The specificity and topology of chromatin interaction pathways in yeast.

Mol Cell 2011 May;42(4):536-49

Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1016/j.molcel.2011.03.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435841PMC
May 2011

Functional overlap and regulatory links shape genetic interactions between signaling pathways.

Cell 2010 Dec;143(6):991-1004

University Medical Centre Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1016/j.cell.2010.11.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3073509PMC
December 2010

Epistatic relationships reveal the functional organization of yeast transcription factors.

Mol Syst Biol 2010 Oct;6:420

Department of Biochemistry and Biophysics, University of California-San Francisco, San Francisco, CA 94143-2542, USA.

The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.
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http://dx.doi.org/10.1038/msb.2010.77DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990640PMC
October 2010

A consensus of core protein complex compositions for Saccharomyces cerevisiae.

Mol Cell 2010 Jun;38(6):916-28

Department of Physiological Chemistry, University Medical Centre Utrecht, 3584 CG Utrecht, The Netherlands.

Analyses of biological processes would benefit from accurate definitions of protein complexes. High-throughput mass spectrometry data offer the possibility of systematically defining protein complexes; however, the predicted compositions vary substantially depending on the algorithm applied. We determine consensus compositions for 409 core protein complexes from Saccharomyces cerevisiae by merging previous predictions with a new approach. Various analyses indicate that the consensus is comprehensive and of high quality. For 85 out of 259 complexes not recorded in GO, literature search revealed strong support in the form of coprecipitation. New complexes were verified by an independent interaction assay and by gene expression profiling of strains with deleted subunits, often revealing which cellular processes are affected. The consensus complexes are available in various formats, including a merge with GO, resulting in 518 protein complex compositions. The utility is further demonstrated by comparison with binary interaction data to reveal interactions between core complexes.
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http://dx.doi.org/10.1016/j.molcel.2010.06.002DOI Listing
June 2010

Cotranslational assembly of the yeast SET1C histone methyltransferase complex.

EMBO J 2009 Oct 27;28(19):2959-70. Epub 2009 Aug 27.

Institute of Molecular Biology, University of Zürich, Zürich, Switzerland.

While probing the role of RNA for the function of SET1C/COMPASS histone methyltransferase, we identified SET1RC (SET1 mRNA-associated complex), a complex that contains SET1 mRNA and Set1, Swd1, Spp1 and Shg1, four of the eight polypeptides that constitute SET1C. Characterization of SET1RC showed that SET1 mRNA binding did not require associated Swd1, Spp1 and Shg1 proteins or RNA recognition motifs present in Set1. RNA binding was not observed when Set1 protein and SET1 mRNA were derived from independent genes or when SET1 transcripts were restricted to the nucleus. Importantly, the protein-RNA interaction was sensitive to EDTA, to the translation elongation inhibitor puromycin and to the inhibition of translation initiation in prt1-1 mutants. Taken together, our results support the idea that SET1 mRNA binding was dependent on translation and that SET1RC assembled on nascent Set1 in a cotranslational manner. Moreover, we show that cellular accumulation of Set1 is limited by the availability of certain SET1C components, such as Swd1 and Swd3, and suggest that cotranslational protein interactions may exert an effect in the protection of nascent Set1 from degradation.
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http://dx.doi.org/10.1038/emboj.2009.240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760116PMC
October 2009

A comprehensive framework of E2-RING E3 interactions of the human ubiquitin-proteasome system.

Mol Syst Biol 2009 18;5:295. Epub 2009 Aug 18.

Division of Biomedical Genetics, Department of Physiological Chemistry, University Medical Center Utrecht, Utrecht, The Netherlands.

Covalent attachment of ubiquitin to substrates is crucial to protein degradation, transcription regulation and cell signalling. Highly specific interactions between ubiquitin-conjugating enzymes (E2) and ubiquitin protein E3 ligases fulfil essential roles in this process. We performed a global yeast-two hybrid screen to study the specificity of interactions between catalytic domains of the 35 human E2s with 250 RING-type E3s. Our analysis showed over 300 high-quality interactions, uncovering a large fraction of new E2-E3 pairs. Both within the E2 and the E3 cohorts, several members were identified that are more versatile in their interaction behaviour than others. We also found that the physical interactions of our screen compare well with reported functional E2-E3 pairs in in vitro ubiquitination experiments. For validation we confirmed the interaction of several versatile E2s with E3s in in vitro protein interaction assays and we used mutagenesis to alter the E3 interactions of the E2 specific for K63 linkages, UBE2N(Ubc13), towards the K48-specific UBE2D2(UbcH5B). Our data provide a detailed, genome-wide overview of binary E2-E3 interactions of the human ubiquitination system.
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http://dx.doi.org/10.1038/msb.2009.55DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2736652PMC
November 2009

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

Mol Syst Biol 2009 28;5:266. Epub 2009 Apr 28.

Department of Physiological Chemistry, University Medical Center Utrecht, Universiteitsweg, Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1038/msb.2009.21DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2683724PMC
June 2009

Functional organization of the S. cerevisiae phosphorylation network.

Cell 2009 Mar;136(5):952-63

Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 94158, USA.

Reversible protein phosphorylation is a signaling mechanism involved in all cellular processes. To create a systems view of the signaling apparatus in budding yeast, we generated an epistatic miniarray profile (E-MAP) comprised of 100,000 pairwise, quantitative genetic interactions, including virtually all protein and small-molecule kinases and phosphatases as well as key cellular regulators. Quantitative genetic interaction mapping reveals factors working in compensatory pathways (negative genetic interactions) or those operating in linear pathways (positive genetic interactions). We found an enrichment of positive genetic interactions between kinases, phosphatases, and their substrates. In addition, we assembled a higher-order map from sets of three genes that display strong interactions with one another: triplets enriched for functional connectivity. The resulting network view provides insights into signaling pathway regulation and reveals a link between the cell-cycle kinase, Cak1, the Fus3 MAP kinase, and a pathway that regulates chromatin integrity during transcription by RNA polymerase II.
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http://dx.doi.org/10.1016/j.cell.2008.12.039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2856666PMC
March 2009