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.

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.

Functional organization of the S. cerevisiae phosphorylation network.

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.

Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS.

BACKGROUND: Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. METHODS: We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and "fuses" evidence from RNA-seq and whole genome sequencing (WGS) using intron-exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. RESULTS: In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. CONCLUSIONS: Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making.

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

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.

Molecular mechanisms that distinguish TFIID housekeeping from regulatable SAGA promoters.

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.

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

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.

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

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.

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.

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.

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

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.

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

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 × 10-4), PPM1D (p = 4.65 × 10-2), and PHIP (p = 6.3 × 10-3). 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 × 10-2). 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.

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.

A consensus of core protein complex compositions for Saccharomyces cerevisiae.

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.

The genomic landscape of compensatory evolution.

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.

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

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.

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.

An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas.

Metastasis is the process by which cancers spread to distinct sites in the body. It is the principal cause of death in individuals suffering from cancer. For some types of cancer, early detection of metastasis at lymph nodes close to the site of the primary tumor is pivotal for appropriate treatment. Because it can be difficult to detect lymph node metastases reliably, many individuals currently receive inappropriate treatment. We show here that DNA microarray gene-expression profiling can detect lymph node metastases for primary head and neck squamous cell carcinomas that arise in the oral cavity and oropharynx. The predictor, established with an 82-tumor training set, outperforms current clinical diagnosis when independently validated. The 102 predictor genes offer unique insights into the processes underlying metastasis. The results show that the metastatic state can be deciphered from the primary tumor gene-expression pattern and that treatment can be substantially improved.

Cotranslational assembly of the yeast SET1C histone methyltransferase complex.

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.

Elevated enhancer-oncogene contacts and higher oncogene expression levels by recurrent CTCF inactivating mutations in acute T cell leukemia.

Monoallelic inactivation of CCCTC-binding factor (CTCF) in human cancer drives altered methylated genomic states, altered CTCF occupancy at promoter and enhancer regions, and deregulated global gene expression. In patients with T cell acute lymphoblastic leukemia (T-ALL), we find that acquired monoallelic CTCF-inactivating events drive subtle and local genomic effects in nearly half of t(5; 14) (q35; q32.2) rearranged patients, especially when CTCF-binding sites are preserved in between the BCL11B enhancer and the TLX3 oncogene. These solitary intervening sites insulate TLX3 from the enhancer by inducing competitive looping to multiple binding sites near the TLX3 promoter. Reduced CTCF levels or deletion of the intervening CTCF site abrogates enhancer insulation by weakening competitive looping while favoring TLX3 promoter to BCL11B enhancer looping, which elevates oncogene expression levels and leukemia burden.

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

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.