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.

Summary 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 is 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 new 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.

SUMMARY 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.

Detecting the genomic changes underlying phenotypic changes between species is a main goal of evolutionary biology and genomics. Evolutionary theory predicts that changes in cis-regulatory elements are important for morphological changes. We combined genome sequencing, functional genomics and genome-wide comparative analyses to investigate regulatory elements in lineages that lost morphological traits. We first show that limb loss in snakes is associated with widespread divergence of limb regulatory elements. We next show that eye degeneration in subterranean mammals is associated with widespread divergence of eye regulatory elements. In both cases, sequence divergence results in an extensive loss of transcription factor binding sites. Importantly, diverged regulatory elements are associated with genes required for normal limb patterning or normal eye development and function, suggesting that regulatory divergence contributed to the loss of these phenotypes. Together, our results show that genome-wide decay of the phenotype-specific cis-regulatory landscape is a hallmark of lost morphological traits.

Embryonic stem (ES) cells are pluripotent and characterized by open chromatin and high transcription levels, achieved through auto-regulatory and feed-forward transcription factor loops. ES-cell identity is maintained by a core of factors including Oct4 (also known as Pou5f1), Sox2, Klf4, c-Myc (OSKM) and Nanog, and forced expression of the OSKM factors can reprogram somatic cells into induced pluripotent stem cells (iPSCs) resembling ES cells. These gene-specific factors for RNA-polymerase-II-mediated transcription recruit transcriptional cofactors and chromatin regulators that control access to and activity of the basal transcription machinery on gene promoters. How the basal transcription machinery is involved in setting and maintaining the pluripotent state is unclear. Here we show that knockdown of the transcription factor IID (TFIID) complex affects the pluripotent circuitry in mouse ES cells and inhibits reprogramming of fibroblasts. TFIID subunits and the OSKM factors form a feed-forward loop to induce and maintain a stable transcription state. Notably, transient expression of TFIID subunits greatly enhanced reprogramming. These results show that TFIID is critical for transcription-factor-mediated reprogramming. We anticipate that, by creating plasticity in gene expression programs, transcription complexes such as TFIID assist reprogramming into different cellular states.

Next-generation sequencers such as Illumina can now produce reads up to 300 bp with high throughput, which is attractive for genome assembly. A first step in genome assembly is to computationally correct sequencing errors. However, correcting all errors in these longer reads is challenging. Here, we show that reads with remaining errors after correction often overlap repeats, where short erroneous k-mers occur in other copies of the repeat. We developed an iterative error correction pipeline that runs the previously published String Graph Assembler (SGA) in multiple rounds of k-mer-based correction with an increasing k-mer size, followed by a final round of overlap-based correction. By combining the advantages of small and large k-mers, this approach corrects more errors in repeats and minimizes the total amount of erroneous reads. We show that higher read accuracy increases contig lengths two to three times. We provide SGA-Iteratively Correcting Errors (https://github.com/hillerlab/IterativeErrorCorrection/) that implements iterative error correction by using modules from SGA.

BackgroundGenetic 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.ResultsTo 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”.ConclusionsThese 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-015-0222-5) contains supplementary material, which is available to authorized users.

BackgroundCellular 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.ResultsIn 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.ConclusionsThe 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.