Maga Rowicka

Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes

Budding yeast grown under continuous, nutrient-limited conditions exhibit robust, highly periodic cycles in the form of respiratory bursts. Microarray studies reveal that over half of the yeast genome is expressed periodically during these metabolic cycles. Genes encoding proteins having a common function exhibit similar temporal expression patterns, and genes specifying functions associated with energy and metabolism tend to be expressed with exceptionally robust periodicity. Essential cellular and metabolic events occur in synchrony with the metabolic cycle, demonstrating that key processes in a simple eukaryotic cell are compartmentalized in time.

Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing

We present a genome-wide approach to map DNA double-strand breaks (DSBs) at nucleotide resolution by a method we termed BLESS (direct in situ breaks labeling, enrichment on streptavidin and next-generation sequencing). We validated and tested BLESS using human and mouse cells and different DSBs-inducing agents and sequencing platforms. BLESS was able to detect telomere ends, Sce endonuclease–induced DSBs and complex genome-wide DSB landscapes. As a proof of principle, we characterized the genomic landscape of sensitivity to replication stress in human cells, and we identified >2,000 nonuniformly distributed aphidicolin-sensitive regions (ASRs) overrepresented in genes and enriched in satellite repeats. ASRs were also enriched in regions rearranged in human cancers, with many cancer-associated genes exhibiting high sensitivity to replication stress. Our method is suitable for genome-wide mapping of DSBs in various cells and experimental conditions, with a specificity and resolution unachievable by current techniques.

High-resolution timing of cell cycle-regulated gene expression

The eukaryotic cell division cycle depends on an intricate sequence of transcriptional events. Using an algorithm based on maximum-entropy deconvolution, and expression data from a highly synchronized yeast culture, we have timed the peaks of expression of transcriptionally regulated cell cycle genes to an accuracy of 2 min (≈1% of the cell cycle time). The set of 1,129 cell cycle-regulated genes was identified by a comprehensive analysis encompassing all available cell cycle yeast data sets. Our results reveal distinct subphases of the cell cycle undetectable by morphological observation, as well as the precise timeline of macromolecular complex assembly during key cell cycle events.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Genome-wide mapping of long-range contacts unveils clustering of DNA double-strand breaks at damaged active genes

The ability of DNA double-strand breaks (DSBs) to cluster in mammalian cells has been a subject of intense debate in recent years. Here we used a high-throughput chromosome conformation capture assay (capture Hi-C) to investigate clustering of DSBs induced at defined loci in the human genome. The results unambiguously demonstrated that DSBs cluster, but only when they are induced within transcriptionally active genes. Clustering of damaged genes occurs primarily during the G1 cell-cycle phase and coincides with delayed repair. Moreover, DSB clustering depends on the MRN complex as well as the Formin 2 (FMN2) nuclear actin organizer and the linker of nuclear and cytoplasmic skeleton (LINC) complex, thus suggesting that active mechanisms promote clustering. This work reveals that, when damaged, active genes, compared with the rest of the genome, exhibit a distinctive behavior, remaining largely unrepaired and clustered in G1, and being repaired via homologous recombination in postreplicative cells.

Comprehensive Structural and Substrate Specificity Classification of the Saccharomyces cerevisiae Methyltransferome

Methylation is one of the most common chemical modifications of biologically active molecules and it occurs in all life forms. Its functional role is very diverse and involves many essential cellular processes, such as signal transduction, transcriptional control, biosynthesis, and metabolism. Here, we provide further insight into the enzymatic methylation in S. cerevisiae by conducting a comprehensive structural and functional survey of all the methyltransferases encoded in its genome. Using distant homology detection and fold recognition, we found that the S. cerevisiae methyltransferome comprises 86 MTases (53 well-known and 33 putative with unknown substrate specificity). Structural classification of their catalytic domains shows that these enzymes may adopt nine different folds, the most common being the Rossmann-like. We also analyzed the domain architecture of these proteins and identified several new domain contexts. Interestingly, we found that the majority of MTase genes are periodically expressed during yeast metabolic cycle. This finding, together with calculated isoelectric point, fold assignment and cellular localization, was used to develop a novel approach for predicting substrate specificity. Using this approach, we predicted the general substrates for 24 of 33 putative MTases and confirmed these predictions experimentally in both cases tested. Finally, we show that, in S. cerevisiae, methylation is carried out by 34 RNA MTases, 32 protein MTases, eight small molecule MTases, three lipid MTases, and nine MTases with still unknown substrate specificity.

A probabilistic approach to learn chromatin architecture and accurate inference of the NF-κB/RelA regulatory network using ChIP-Seq

Using nuclear factor-κB (NF-κB) ChIP-Seq data, we present a framework for iterative learning of regulatory networks. For every possible transcription factor-binding site (TFBS)-putatively regulated gene pair, the relative distance and orientation are calculated to learn which TFBSs are most likely to regulate a given gene. Weighted TFBS contributions to putative gene regulation are integrated to derive an NF-κB gene network. A de novo motif enrichment analysis uncovers secondary TFBSs (AP1, SP1) at characteristic distances from NF-κB/RelA TFBSs. Comparison with experimental ENCODE ChIP-Seq data indicates that experimental TFBSs highly correlate with predicted sites. We observe that RelA-SP1-enriched promoters have distinct expression profiles from that of RelA-AP1 and are enriched in introns, CpG islands and DNase accessible sites. Sixteen novel NF-κB/RelA-regulated genes and TFBSs were experimentally validated, including TANK, a negative feedback gene whose expression is NF-κB/RelA dependent and requires a functional interaction with the AP1 TFBSs. Our probabilistic method yields more accurate NF-κB/RelA-regulated networks than a traditional, distance-based approach, confirmed by both analysis of gene expression and increased informativity of Genome Ontology annotations. Our analysis provides new insights into how co-occurring TFBSs and local chromatin context orchestrate activation of NF-κB/RelA sub-pathways differing in biological function and temporal expression patterns.

Strategies for Achieving High Sequencing Accuracy for Low Diversity Samples and Avoiding Sample Bleeding Using Illumina Platform

Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer’s, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy.

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

Background: Interacting proteins modulate the activity of NF-κB/RelA transcription factor and expression of its targets. Results: By analyzing gene expression, protein binding, and DNA binding, we inferred and characterized 8349 such modulations. Conclusion: Different modulator groups affect separate pathways. Significance: We provide new insight into the activity of NF-κB/RelA. Our inference model can be applied to other processes and pathways. Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

Ssb1 and Ssb2 cooperate to regulate mouse hematopoietic stem and progenitor cells by resolving replicative stress

Hematopoietic stem and progenitor cells (HSPCs) are vulnerable to endogenous damage and defects in DNA repair can limit their function. The 2 single-stranded DNA (ssDNA) binding proteins SSB1 and SSB2 are crucial regulators of the DNA damage response; however, their overlapping roles during normal physiology are incompletely understood. We generated mice in which both Ssb1 and Ssb2 were constitutively or conditionally deleted. Constitutive Ssb1/Ssb2 double knockout (DKO) caused early embryonic lethality, whereas conditional Ssb1/Ssb2 double knockout (cDKO) in adult mice resulted in acute lethality due to bone marrow failure and intestinal atrophy featuring stem and progenitor cell depletion, a phenotype unexpected from the previously reported single knockout models of Ssb1 or Ssb2 Mechanistically, cDKO HSPCs showed altered replication fork dynamics, massive accumulation of DNA damage, genome-wide double-strand breaks enriched at Ssb-binding regions and CpG islands, together with the accumulation of R-loops and cytosolic ssDNA. Transcriptional profiling of cDKO HSPCs revealed the activation of p53 and interferon (IFN) pathways, which enforced cell cycling in quiescent HSPCs, resulting in their apoptotic death. The rapid cell death phenotype was reproducible in in vitro cultured cDKO-hematopoietic stem cells, which were significantly rescued by nucleotide supplementation or after depletion of p53. Collectively, Ssb1 and Ssb2 control crucial aspects of HSPC function, including proliferation and survival in vivo by resolving replicative stress to maintain genomic stability.