BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

Dosage-Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome.

DNA copy number variation is associated with many high phenotypic heterogeneity disorders. We systematically examined the impact of Drosophila melanogaster deletions on gene expression profiles to ask whether increased expression variability owing to reduced gene dose might underlie this phenotypic heterogeneity. Indeed, we found that one-dose genes have higher gene expression variability relative to two-dose genes. We then asked whether this increase in variability could be explained by intrinsic noise within cells due to stochastic biochemical events, or whether expression variability is due to extrinsic noise arising from more complex interactions. Our modeling showed that intrinsic gene expression noise averages at the organism level and thus cannot explain increased variation in one-dose gene expression. Interestingly, expression variability was related to the magnitude of expression compensation, suggesting that regulation, induced by gene dose reduction, is noisy. In a remarkable exception to this rule, the single X chromosome of males showed reduced expression variability, even compared with two-dose genes. Analysis of sex-transformed flies indicates that X expression variability is independent of the male differentiation program. Instead, we uncovered a correlation between occupancy of the chromatin-modifying protein encoded by males absent on the first (mof) and expression variability, linking noise suppression to the specialized X chromosome dosage compensation system. MOF occupancy on autosomes in both sexes also lowered transcriptional noise. Our results demonstrate that gene dose reduction can lead to heterogeneous responses, which are often noisy. This has implications for understanding gene network regulatory interactions and phenotypic heterogeneity. Additionally, chromatin modification appears to play a role in dampening transcriptional noise

The genome-wide distribution of non-B DNA motifs is shaped by operon structure and suggests the transcriptional importance of non-B DNA structures in Escherichia coli

Although the right-handed double helical B-form DNA is most common under physiological conditions, DNA is dynamic and can adopt a number of alternative structures, such as the four-stranded G-quadruplex, left-handed Z-DNA, cruciform and others. Active transcription necessitates strand separation and can induce such non-canonical forms at susceptible genomic sequences. Therefore, it has been speculated that these non-B DNA motifs can play regulatory roles in gene transcription. Such conjecture has been supported in higher eukaryotes by direct studies of several individual genes, as well as a number of large-scale analyses. However, the role of non-B DNA structures in many lower organisms, in particular proteobacteria, remains poorly understood and incompletely documented. In this study, we performed the first comprehensive study of the occurrence of B DNA–non-B DNA transition-susceptible sites (non-B DNA motifs) within the context of the operon structure of the Escherichia coli genome. We compared the distributions of non-B DNA motifs in the regulatory regions of operons with those from internal regions. We found an enrichment of some non-B DNA motifs in regulatory regions, and we show that this enrichment cannot be simply explained by base composition bias in these regions. We also showed that the distribution of several non-B DNA motifs within intergenic regions separating divergently oriented operons differs from the distribution found between convergent ones. In particular, we found a strong enrichment of cruciforms in the termination region of operons; this enrichment was observed for operons with Rho-dependent, as well as Rho-independent terminators. Finally, a preference for some non-B DNA motifs was observed near transcription factor-binding sites. Overall, the conspicuous enrichment of transition-susceptible sites in these specific regulatory regions suggests that non-B DNA structures may have roles in the transcriptional regulation of specific operons within the E. coli genome

Ups and Downs of Poised RNA Polymerase II in B-Cells.

Recent genome-wide analyses have uncovered a high accumulation of RNA polymerase II (Pol II) at the 5' end of genes. This elevated Pol II presence at promoters, referred to here as Poll II poising, is mainly (but not exclusively) attributed to temporal pausing of transcription during early elongation which, in turn, has been proposed to be a regulatory step for processes that need to be activated "on demand". Yet, the full genome-wide regulatory role of Pol II poising is yet to be delineated. To elucidate the role of Pol II poising in B cell activation, we compared Pol II profiles in resting and activated B cells. We found that while Pol II poised genes generally overlap functionally among different B cell states and correspond to the functional groups previously identified for other cell types, non-poised genes are B cell state specific. Focusing on the changes in transcription activity upon B cell activation, we found that the majority of such changes were from poised to non-poised state. The genes showing this type of transition were functionally enriched in translation, RNA processing and mRNA metabolic process. Interestingly, we also observed a transition from non-poised to poised state. Within this set of genes we identified several Immediate Early Genes (IEG), which were highly expressed in resting B cell and shifted from non-poised to poised state after B cell activation. Thus Pol II poising does not only mark genes for rapid expression in the future, but it is also associated with genes that are silenced after a burst of their expression. Finally, we performed comparative analysis of the presence of G4 motifs in the context of poised versus non-poised but active genes. Interestingly we observed a differential enrichment of these motifs upstream versus downstream of TSS depending on poising status. The enrichment of G4 sequence motifs upstream of TSS of non-poised active genes suggests a potential role of quadruplexes in expression regulation

Teasing apart translational and transcriptional components of stochastic variations in eukaryotic gene expression

The intrinsic stochasticity of gene expression leads to cell-to-cell variations, noise, in protein abundance. Several processes, including transcription, translation, and degradation of mRNA and proteins, can contribute to these variations. Recent single cell analyses of gene expression in yeast have uncovered a general trend where expression noise scales with protein abundance. This trend is consistent with a stochastic model of gene expression where mRNA copy number follows the random birth and death process. However, some deviations from this basic trend have also been observed, prompting questions about the contribution of gene-specific features to such deviations. For example, recent studies have pointed to the TATA box as a sequence feature that can influence expression noise by facilitating expression bursts. Transcription-originated noise can be potentially further amplified in translation. Therefore, we asked the question of to what extent sequence features known or postulated to accompany translation efficiency can also be associated with increase in noise strength and, on average, how such increase compares to the amplification associated with the TATA box. Untangling different components of expression noise is highly nontrivial, as they may be gene or gene-module specific. In particular, focusing on codon usage as one of the sequence features associated with efficient translation, we found that ribosomal genes display a different relationship between expression noise and codon usage as compared to other genes. Within nonribosomal genes we found that sequence high codon usage is correlated with increased noise relative to the average noise of proteins with the same abundance. Interestingly, by projecting the data on a theoretical model of gene expression, we found that the amplification of noise strength associated with codon usage is comparable to that of the TATA box, suggesting that the effect of translation on noise in eukaryotic gene expression might be more prominent than previously appreciated.MOE (Min. of Education, S’pore)Published versio

MODELING CELL HETEROGENEITY: FROM SINGLE-CELL VARIATIONS TO MIXED CELLS

Emerging technologies such as single cell gene expression analysis and single cell genome sequencing provide an unprecedented opportunity to quantitatively probe biological interactions at the single cell level. This new level of insight has begun to reveal a more accurate picture of cellular behavior, and to highlight the importance of understanding cellular variation in a wide range of biological contexts. The aim of this workshop is to bring together researchers working on identifying and modeling cell heterogeneity that arises by a variety of mechanisms, including but not limited to cell-to-cell noise, cell-state switches and cell differentiation, heterogeneity in immune responses, cancer evolution, and heterogeneity in disease progression. 1. Background Quantifying the molecular mechanisms underlying cell behaviors and functions is one of the ultimate goals of biology and medicine. Until recently, most current measures to classify and characterize cellular behavior have been performed on the average of all cells in a sample instead of a single cell. However, measurements derived from pooled populations of cells lack the specificity to capture outlier cell behavior that might explain cell differentiation and transitionsfrom normal to disease cellular states. The noise, or variance, between the genomic state of different cells-- even among cells assumed to be homogenous-- has been shown to be strongl

DNA Break Mapping Reveals Topoisomerase II Activity Genome-Wide

Genomic DNA is under constant assault by endogenous and exogenous DNA damaging agents. DNA breakage can represent a major threat to genome integrity but can also be necessary for genome function. Here we present approaches to map DNA double-strand breaks (DSBs) and single-strand breaks (SSBs) at the genome-wide scale by two methods called DSB- and SSB-Seq, respectively. We tested these methods in human colon cancer cells and validated the results using the Topoisomerase II (Top2)-poisoning agent etoposide (ETO). Our results show that the combination of ETO treatment with break-mapping techniques is a powerful method to elaborate the pattern of Top2 enzymatic activity across the genome

Mapping DNA breaks by next-generation sequencing

Here, we present two approaches to map DNA double-strand breaks (DSBs) and single-strand breaks (SSBs) in the genome of human cells. We named these methods respectively DSB-Seq and SSB-Seq. We tested the DSB and SSB-Seq in HCT1116, human colon cancer cells, and validated the results using the topoisomerase 2 (Top2)-poisoning agent etoposide (ETO). These methods are powerful tools for the direct detection of the physiological and pathological \ue2\u80\u9cbreakome\ue2\u80\u9d of the DNA in human cells