Influence network model uncovers relations between biological processes and mutational signatures.

BACKGROUND: There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures. METHODS: To gain insights into these relationships, we developed a network-based method, named GENESIGNET that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. RESULTS: Applying GENESIGNET to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GENESIGNET also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GENESIGNET also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. CONCLUSIONS: GENESIGNET provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GENESIGNET method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.

Detection of Z-DNA Structures in Supercoiled Genome.

Z-DNAs are nucleic acid secondary structures that form within a special pattern of nucleotides and are promoted by DNA supercoiling. Through Z-DNA formation, DNA encodes information by dynamic changes in its secondary structure. A growing body of evidence indicates that Z-DNA formation can play a role in gene regulation; it can affect chromatin architecture and demonstrates its association with genomic instability, genetic diseases, and genome evolution. Many functional roles of Z-DNA are yet to be discovered highlighting the need for techniques to detect genome-wide folding of DNA into this structure. Here, we describe an approach to convert linear genome into supercoiled genome sponsoring Z-DNA formation. Applying permanganate-based methodology and high-throughput sequencing to supercoiled genome allows genome-wide detection of single-stranded DNA. Single-stranded DNA is characteristic of the junctions between the classical B-form of DNA and Z-DNA. Consequently, analysis of single-stranded DNA map provides snapshots of the Z-DNA conformation over the whole genome.

Mutational Signatures as Sensors of Environmental Exposures: Analysis of Smoking-Induced Lung Tissue Remodeling.

Smoking is a widely recognized risk factor in the emergence of cancers and other lung diseases. Studies of non-cancer lung diseases typically investigate the role that smoking has in chronic changes in lungs that might predispose patients to the diseases, whereas most cancer studies focus on the mutagenic properties of smoking. Large-scale cancer analysis efforts have collected expression data from both tumor and control lung tissues, and studies have used control samples to estimate the impact of smoking on gene expression. However, such analyses may be confounded by tumor-related micro-environments as well as patient-specific exposure to smoking. Thus, in this paper, we explore the utilization of mutational signatures to study environment-induced changes of gene expression in control lung tissues from lung adenocarcinoma samples. We show that a joint computational analysis of mutational signatures derived from sequenced tumor samples, and the gene expression obtained from control samples, can shed light on the combined impact that smoking and tumor-related micro-environments have on gene expression and cell-type composition in non-neoplastic (control) lung tissue. The results obtained through such analysis are both supported by experimental studies, including studies utilizing single-cell technology, and also suggest additional novel insights. We argue that the study provides a proof of principle of the utility of mutational signatures to be used as sensors of environmental exposures not only in the context of the mutational landscape of cancer, but also as a reference for changes in non-cancer lung tissues. It also provides an example of how a database collected with the purpose of understanding cancer can provide valuable information for studies not directly related to the disease.

Mutational Signatures: From Methods to Mechanisms.

Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.

RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer.

Cancer genomes accumulate a large number of somatic mutations resulting from a combination of stochastic errors in DNA processing, cancer-related aberrations of the DNA repair machinery, or carcinogenic exposures; each mutagenic process leaves a characteristic mutational signature. A key challenge is understanding the interactions between signatures, particularly as DNA repair deficiencies often modify the effects of other mutagens. Here, we introduce RepairSig, a computational method that explicitly models additive primary mutagenic processes; non-additive secondary processes, which interact with the primary processes; and a mutation opportunity, that is, the distribution of sites across the genome that are vulnerable to damage or preferentially repaired. We demonstrate that RepairSig accurately recapitulates experimentally identified signatures, identifies autonomous signatures of deficient DNA repair processes, and explains mismatch repair deficiency in breast cancer by de novo inference of both primary and secondary signatures from patient data. RepairSig is freely available for download at https://github.com/ncbi/RepairSig.

Identifying Drug Sensitivity Subnetworks with NETPHIX.

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer.

BACKGROUND: Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from various endogenous and exogenous causes, including normal DNA damage and repair processes or cancer-related aberrations of DNA maintenance machinery as well as mutations triggered by carcinogenic exposures. Different mutagenic processes often produce characteristic mutational patterns called mutational signatures. Identifying mutagenic processes underlying mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. METHODS: To investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as cancer phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted a recently developed optimization method based on integer linear programming. RESULTS: Analyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC-related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age-related signatures-one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes. CONCLUSIONS: This work investigated, for the first time, a network-level association of mutational signatures and dysregulated pathways. The identified pathways and subnetworks provide novel insights into mutagenic processes that the cancer genomes might have undergone and important clues for developing personalized drug therapies.

A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer.

The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of mutations, termed processive groups, occur on the same DNA strand and are generated by a single process or signature. Here we provide a first probabilistic model of mutational signatures that accounts for their observed stickiness and strand coordination. The model conditions on the observed strand for each mutation and allows the same signature to generate a run of mutations. It can both use known signatures or learn new ones. We show that this model provides a more accurate description of the properties of mutagenic processes than independent-mutation achieving substantially higher likelihood on held-out data. We apply this model to characterize the processivity of mutagenic processes across multiple types of cancer.

DNA Repair Footprint Uncovers Contribution of DNA Repair Mechanism to Mutational Signatures.

Cancer genomes accumulate a large number of somatic mutations resulting from imperfection of DNA processing during normal cell cycle as well as from carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These processes often lead to distinctive patterns of mutations, called mutational signatures. Several computational methods have been developed to uncover such signatures from catalogs of somatic mutations. However, cancer mutational signatures are the end-effect of several interplaying factors including carcinogenic exposures and potential deficiencies of the DNA repair mechanism. To fully understand the nature of each signature, it is important to disambiguate the atomic components that contribute to the final signature. Here, we introduce a new descriptor of mutational signatures, DNA Repair FootPrint (RePrint), and show that it can capture common properties of deficiencies in repair mechanisms contributing to diverse signatures. We validate the method with published mutational signatures from cell lines targeted with CRISPR-Cas9-based knockouts of DNA repair genes.

In Vivo Chemical Probing for G-Quadruplex Formation.

While DNA inside the cells is predominantly canonical right-handed double helix, guanine-rich DNAs have potential to fold into four-stranded structures that contain stacks of G-quartets (G4 DNA quadruplex). Genome sequencing has revealed G4 sequences tend to localize at the gene control regions, especially in the promoters of oncogenes. A growing body of evidence indicates that G4 DNA quadruplexes might have important regulatory roles in genome function, highlighting the need for techniques to detect genome-wide folding of DNA into this structure. Potassium permanganate in vivo treatment of cells results in oxidizing of nucleotides in single-stranded DNA regions that accompany G4 DNA quadruplexes formation, providing an excellent probe for the conformational state of DNA inside the living cells. Here, we describe a permanganate-based methodology to detect G4 DNA quadruplex, genome-wide. This methodology combined with high-throughput sequencing provides a snapshot of the DNA conformation over the whole genome in vivo.

Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer.

Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.

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.

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 "breakome" of the DNA in human cells.

Detecting presence of mutational signatures in cancer with confidence.

MOTIVATION: Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome's mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient's mutational profile into signatures but also establish the accuracy of such decomposition. RESULTS: We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. AVAILABILITY AND IMPLEMENTATION: All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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 other novel findings such as the interaction between NCOR2 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 synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html.

Permanganate/S1 Nuclease Footprinting Reveals Non-B DNA Structures with Regulatory Potential across a Mammalian Genome.

DNA in cells is predominantly B-form double helix. Though certain DNA sequences in vitro may fold into other structures, such as triplex, left-handed Z form, or quadruplex DNA, the stability and prevalence of these structures in vivo are not known. Here, using computational analysis of sequence motifs, RNA polymerase II binding data, and genome-wide potassium permanganate-dependent nuclease footprinting data, we map thousands of putative non-B DNA sites at high resolution in mouse B cells. Computational analysis associates these non-B DNAs with particular structures and indicates that they form at locations compatible with an involvement in gene regulation. Further analyses support the notion that non-B DNA structure formation influences the occupancy and positioning of nucleosomes in chromatin. These results suggest that non-B DNAs contribute to the control of a variety of critical cellular and organismal processes.

Structural and Functional Characterization of the LPS Transporter LptDE from Gram-Negative Pathogens.

Incorporation of lipopolysaccharide (LPS) into the outer membrane of Gram-negative bacteria is essential for viability, and is accomplished by a two-protein complex called LptDE. We solved crystal structures of the core LptDE complexes from Yersinia pestis, Klebsiella pneumoniae, Pseudomonas aeruginosa, and a full-length structure of the K. pneumoniae LptDE complex. Our structures adopt the same plug and 26-strand ß-barrel architecture found recently for the Shigella flexneri and Salmonella typhimurium LptDE structures, illustrating a conserved fold across the family. A comparison of the only two full-length structures, SfLptDE and our KpLptDE, reveals a 21° rotation of the LptD N-terminal domain that may impart flexibility on the trans-envelope LptCAD scaffold. Utilizing mutagenesis coupled to an in vivo functional assay and molecular dynamics simulations, we demonstrate the critical role of Pro231 and Pro246 in the function of the LptD lateral gate that allows partitioning of LPS into the outer membrane.

RNA Polymerase II Regulates Topoisomerase 1 Activity to Favor Efficient Transcription.

We report a mechanism through which the transcription machinery directly controls topoisomerase 1 (TOP1) activity to adjust DNA topology throughout the transcription cycle. By comparing TOP1 occupancy using chromatin immunoprecipitation sequencing (ChIP-seq) versus TOP1 activity using topoisomerase 1 sequencing (TOP1-seq), a method reported here to map catalytically engaged TOP1, TOP1 bound at promoters was discovered to become fully active only after pause-release. This transition coupled the phosphorylation of the carboxyl-terminal-domain (CTD) of RNA polymerase II (RNAPII) with stimulation of TOP1 above its basal rate, enhancing its processivity. TOP1 stimulation is strongly dependent on the kinase activity of BRD4, a protein that phosphorylates Ser2-CTD and regulates RNAPII pause-release. Thus the coordinated action of BRD4 and TOP1 overcame the torsional stress opposing transcription as RNAPII commenced elongation but preserved negative supercoiling that assists promoter melting at start sites. This nexus between transcription and DNA topology promises to elicit new strategies to intercept pathological gene expression.

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.

Potential non-B DNA regions in the human genome are associated with higher rates of nucleotide mutation and expression variation.

While individual non-B DNA structures have been shown to impact gene expression, their broad regulatory role remains elusive. We utilized genomic variants and expression quantitative trait loci (eQTL) data to analyze genome-wide variation propensities of potential non-B DNA regions and their relation to gene expression. Independent of genomic location, these regions were enriched in nucleotide variants. Our results are consistent with previously observed mutagenic properties of these regions and counter a previous study concluding that G-quadruplex regions have a reduced frequency of variants. While such mutagenicity might undermine functionality of these elements, we identified in potential non-B DNA regions a signature of negative selection. Yet, we found a depletion of eQTL-associated variants in potential non-B DNA regions, opposite to what might be expected from their proposed regulatory role. However, we also observed that genes downstream of potential non-B DNA regions showed higher expression variation between individuals. This coupling between mutagenicity and tolerance for expression variability of downstream genes may be a result of evolutionary adaptation, which allows reconciling mutagenicity of non-B DNA structures with their location in functionally important regions and their potential regulatory role.