Dissection of Cancer Mutational Signatures with Individual Components of Cigarette Smoking.

Tobacco smoke delivers a complex mixture of hazardous and potentially hazardous chemicals. Some of these may induce the formation of DNA mutations, which increases the risk of various cancers that display characteristic patterns of accumulated mutations arising from the causative exposures. Tracking the contributions of individual mutagens to mutational signatures present in human cancers can help understand cancer etiology and advance disease prevention strategies. To characterize the potential contributions of individual constituents of tobacco smoke to tobacco exposure-associated mutational signatures, we first assessed the toxic potential of 13 tobacco-relevant compounds by determining their impact on the viability of a human bronchial lung epithelial cell line (BEAS-2B). Experimentally derived high-resolution mutational profiles were characterized for the seven most potent compounds by sequencing the genomes of clonally expanded mutants that arose after exposure to the individual chemicals. Analogous to the classification of mutagenic processes on the basis of signatures from human cancers, we extracted mutational signatures from the mutant clones. We confirmed the formation of previously characterized benzo[a]pyrene mutational signatures. Furthermore, we discovered three novel mutational signatures. The mutational signatures arising from benzo[a]pyrene and norharmane were similar to human lung cancer signatures attributed to tobacco smoking. However, the signatures arising from N-methyl-N'-nitro-N-nitrosoguanidine and 4-(acetoxymethyl)nitrosamino]-1-(3-pyridyl)-1-butanone were not directly related to known tobacco-linked mutational signatures from human cancers. This new data set expands the scope of the in vitro mutational signature catalog and advances understanding of how environmental agents mutate DNA.

Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge.

BACKGROUND: Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard. RESULTS: Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species-which were not reliably predicted-helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample's Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall. CONCLUSIONS: kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation.

Impact of 6-Month Exposure to Aerosols From Potential Modified Risk Tobacco Products Relative to Cigarette Smoke on the Rodent Gastrointestinal Tract.

Cigarette smoking causes adverse health effects that might occur shortly after smoking initiation and lead to the development of inflammation and cardiorespiratory disease. Emerging studies have demonstrated the role of the intestinal microbiome in disease pathogenesis. The intestinal microbiome is susceptible to the influence of environmental factors such as smoking, and recent studies have indicated microbiome changes in smokers. Candidate modified risk tobacco products (CMRTP) are being developed to provide substitute products to lower smoking-related health risks in smokers who are unable or unwilling to quit. In this study, the ApoE-/- mouse model was used to investigate the impact of cigarette smoke (CS) from the reference cigarette 3R4F and aerosols from two CMRTPs based on the heat-not-burn principle [carbon-heated tobacco product 1.2 (CHTP 1.2) and tobacco heating system 2.2 (THS 2.2)] on the intestinal microbiome over a 6-month period. The effect of cessation or switching to CHTP 1.2 after 3 months of CS exposure was also assessed. Next-generation sequencing was used to evaluate the impact of CMRTP aerosols in comparison to CS on microbiome composition and gene expression in the digestive tract of mice. Our analyses highlighted significant gene dysregulation in response to 3R4F exposure at 4 and 6 months. The findings showed an increase in the abundance of Akkermansiaceae upon CS exposure, which was reversed upon cessation. Cessation resulted in a significant decrease in Akkemansiaceae abundance, whereas switching to CHTP 1.2 resulted in an increase in Lactobacillaceae abundance. These microbial changes could be important for understanding the effect of CS on gut function and its relevance to disease pathogenesis via the microbiome.

A Literature Review and Framework Proposal for Halitosis Assessment in Cigarette Smokers and Alternative Nicotine-Delivery Products Users.

Halitosis is a health condition which counts cigarette smoking (CS) among its major risk factors. Cigarette smoke can cause an imbalance in the oral bacterial community, leading to several oral diseases and conditions, including intraoral halitosis. Although the best approach to decrease smoking-related health risks is quitting smoking, this is not feasible for many smokers. Switching to potentially reduced-risk products, like electronic vapor products (EVP) or heated tobacco products (HTP), may help improve the conditions associated with CS. To date, there have been few systematic studies on the effects of CS on halitosis and none have assessed the effects of EVP and HTP use. Self-assessment studies have shown large limitations owing to the lack of reliability in the participants' judgment. This has compelled the scientific community to develop a strategy for meaningful assessment of these new products in comparison with cigarettes. Here, we compiled a review of the existing literature on CS and halitosis and propose a 3-layer approach that combines the use of the most advanced breath analysis techniques and multi-omics analysis to define the interactions between oral bacterial species and their role in halitosis both in vitro and in vivo. Such an approach will allow us to compare the effects of different nicotine-delivery products on oral bacteria and quantify their impact on halitosis. Defining the impact of alternative nicotine-delivery products on intraoral halitosis and its associated bacteria will help the scientific community advance a step further toward understanding the safety of these products and their potentiall risks for consumers.

Discriminating Spontaneous From Cigarette Smoke and THS 2.2 Aerosol Exposure-Related Proliferative Lung Lesions in A/J Mice by Using Gene Expression and Mutation Spectrum Data.

Mice, especially A/J mice, have been widely employed to elucidate the underlying mechanisms of lung tumor formation and progression and to derive human-relevant modes of action. Cigarette smoke (CS) exposure induces tumors in the lungs; but, non-exposed A/J mice will also develop lung tumors spontaneously with age, which raises the question of discriminating CS-related lung tumors from spontaneous ones. However, the challenge is that spontaneous tumors are histologically indistinguishable from the tumors occurring in CS-exposed mice. We conducted an 18-month inhalation study in A/J mice to assess the impact of lifetime exposure to Tobacco Heating System (THS) 2.2 aerosol relative to exposure to 3R4F cigarette smoke (CS) on toxicity and carcinogenicity endpoints. To tackle the above challenge, a 13-gene gene signature was developed based on an independent A/J mouse CS exposure study, following by a one-class classifier development based on the current study. Identifying gene signature in one data set and building classifier in another data set addresses the feature/gene selection bias which is a well-known problem in literature. Applied to data from this study, this gene signature classifier distinguished tumors in CS-exposed animals from spontaneous tumors. Lung tumors from THS 2.2 aerosol-exposed mice were significantly different from those of CS-exposed mice but not from spontaneous tumors. The signature was also applied to human lung adenocarcinoma gene expression data (from The Cancer Genome Atlas) and discriminated cancers in never-smokers from those in ever-smokers, suggesting translatability of our signature genes from mice to humans. A possible application of this gene signature is to discriminate lung cancer patients who may benefit from specific treatments (i.e., EGFR tyrosine kinase inhibitors). Mutational spectra from a subset of samples were also utilized for tumor classification, yielding similar results. "Landscaping" the molecular features of A/J mouse lung tumors highlighted, for the first time, a number of events that are also known to play a role in human lung tumorigenesis, such as Lrp1b mutation and Ros1 overexpression. This study shows that omics and computational tools provide useful means of tumor classification where histopathological evaluation alone may be unsatisfactory to distinguish between age- and exposure-related lung tumors.

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook.

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

Inflammatory Bowel Disease-Associated Changes in the Gut: Focus on Kazan Patients.

BACKGROUND: Several studies have highlighted the role of host-microbiome interactions in the pathogenesis of inflammatory bowel disease (IBD), resulting in an increasing amount of data mainly focusing on Western patients. Because of the increasing prevalence of IBD in newly industrialized countries such as those in Asia, the Middle East, and South America, there is mounting interest in elucidating the gut microbiota of these populations. We present a comprehensive analysis of several IBD-related biomarkers and gut microbiota profiles and functions of a unique population of patients with IBD and healthy patients from Kazan (Republic of Tatarstan, Russia). METHODS: Blood and fecal IBD biomarkers, serum cytokines, and fecal short-chain fatty acid (SCFA) content were profiled. Finally, fecal microbiota composition was analyzed by 16S and whole-genome shotgun sequencing. RESULTS: Fecal microbiota whole-genome sequencing confirmed the presence of classic IBD dysbiotic features at the phylum level, with increased abundance of Proteobacteria, Actinobacteria, and Fusobacteria and decreased abundance of Firmicutes, Bacteroidetes, and Verrucomicrobia. At the genus level, the abundance of both fermentative (SCFA-producing and hydrogen (H2)-releasing) and hydrogenotrophic (H2-consuming) microbes was affected in patients with IBD. This imbalance was confirmed by the decreased abundance of SCFA species in the feces of patients with IBD and the change in anaerobic index, which mirrors the redox status of the intestine. CONCLUSIONS: Our analyses highlighted how IBD-related dysbiotic microbiota-which are generally mainly linked to SCFA imbalance-may affect other important metabolic pathways, such as H2 metabolism, that are critical for host physiology and disease development.

The reduction of DSS-induced colitis severity in mice exposed to cigarette smoke is linked to immune modulation and microbial shifts.

Exposure to cigarette smoke (CS) causes detrimental health effects, increasing the risk of cardiovascular, pulmonary diseases and carcinogenesis in exposed individuals. The impact of CS on Inflammatory Bowel Disease (IBD) has been established by a number of epidemiological and clinical studies. In fact, CS is associated with a higher risk of developing Crohn's disease (CD) while inversely correlates with the development, disease risks, and relapse rate of ulcerative colitis (UC). To investigate the effect of CS exposure on experimental colitis, we performed a comprehensive and integrated comparative analysis of colon transcriptome and microbiome in mice exposed to dextran sodium sulfate (DSS) and CS. Colon transcriptome analysis revealed that CS downregulated specific pathways in a concentration-dependent manner, affecting both the inflammatory state and composition of the gut microbiome. Metagenomics analysis demonstrated that CS can modulate DSS-induced dysbiosis of specific bacterial genera, contributing to resolve the inflammation or accelerate recovery. The risks of smoking far outweigh any possible benefit, thus smoking cessation must always be encouraged because of its significant health benefits. However, the inverse association between active smoking and the development of UC cannot be ignored and the present study lays the foundation for investigating potential molecular mechanisms responsible for the attenuation of colitis by certain compounds of tobacco when decoupled from combustion.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases.

Database URL: https://solgenomics.net/tools/solcyc/.

The tobacco genome sequence and its comparison with those of tomato and potato.

The allotetraploid plant Nicotiana tabacum (common tobacco) is a major crop species and a model organism, for which only very fragmented genomic sequences are currently available. Here we report high-quality draft genomes for three main tobacco varieties. These genomes show both the low divergence of tobacco from its ancestors and microsynteny with other Solanaceae species. We identify over 90,000 gene models and determine the ancestral origin of tobacco mosaic virus and potyvirus disease resistance in tobacco. We anticipate that the draft genomes will strengthen the use of N. tabacum as a versatile model organism for functional genomics and biotechnology applications.

Reference genomes and transcriptomes of Nicotiana sylvestris and Nicotiana tomentosiformis.

BACKGROUND: Nicotiana sylvestris and Nicotiana tomentosiformis are members of the Solanaceae family that includes tomato, potato, eggplant and pepper. These two Nicotiana species originate from South America and exhibit different alkaloid and diterpenoid production. N. sylvestris is cultivated largely as an ornamental plant and it has been used as a diploid model system for studies of terpenoid production, plastid engineering, and resistance to biotic and abiotic stress. N. sylvestris and N. tomentosiformis are considered to be modern descendants of the maternal and paternal donors that formed Nicotiana tabacum about 200,000 years ago through interspecific hybridization. Here we report the first genome-wide analysis of these two Nicotiana species. RESULTS: Draft genomes of N. sylvestris and N. tomentosiformis were assembled to 82.9% and 71.6% of their expected size respectively, with N50 sizes of about 80 kb. The repeat content was 72-75%, with a higher proportion of retrotransposons and copia-like long terminal repeats in N. tomentosiformis. The transcriptome assemblies showed that 44,000-53,000 transcripts were expressed in the roots, leaves or flowers. The key genes involved in terpenoid metabolism, alkaloid metabolism and heavy metal transport showed differential expression in the leaves, roots and flowers of N. sylvestris and N. tomentosiformis. CONCLUSIONS: The reference genomes of N. sylvestris and N. tomentosiformis represent a significant contribution to the SOL100 initiative because, as members of the Nicotiana genus of Solanaceae, they strengthen the value of the already existing resources by providing additional comparative information, thereby helping to improve our understanding of plant metabolism and evolution.

The Protein Model Portal.

Structural Genomics has been successful in determining the structures of many unique proteins in a high throughput manner. Still, the number of known protein sequences is much larger than the number of experimentally solved protein structures. Homology (or comparative) modeling methods make use of experimental protein structures to build models for evolutionary related proteins. Thereby, experimental structure determination efforts and homology modeling complement each other in the exploration of the protein structure space. One of the challenges in using model information effectively has been to access all models available for a specific protein in heterogeneous formats at different sites using various incompatible accession code systems. Often, structure models for hundreds of proteins can be derived from a given experimentally determined structure, using a variety of established methods. This has been done by all of the PSI centers, and by various independent modeling groups. The goal of the Protein Model Portal (PMP) is to provide a single portal which gives access to the various models that can be leveraged from PSI targets and other experimental protein structures. A single interface allows all existing pre-computed models across these various sites to be queried simultaneously, and provides links to interactive services for template selection, target-template alignment, model building, and quality assessment. The current release of the portal consists of 7.6 million model structures provided by different partner resources (CSMP, JCSG, MCSG, NESG, NYSGXRC, JCMM, ModBase, SWISS-MODEL Repository). The PMP is available at http://www.proteinmodelportal.org and from the PSI Structural Genomics Knowledgebase.

Assessment of CASP7 predictions for template-based modeling targets.

This manuscript presents the assessment of the template-based modeling category of the seventh Critical Assessment of Techniques for Protein Structure Prediction (CASP7). The accuracy of predicted protein models for 108 target domains was assessed based on a detailed comparison between the experimental and predicted structures. The assessment was performed using numerical measures for backbone and structural alignment accuracy, and by scoring correctly modeled hydrogen bond interactions in the predictions. Based on these criteria, our statistical analysis identified a number of groups whose predictions were on average significantly more accurate. Furthermore, the predictions for six target proteins were evaluated for the accuracy of their modeled cofactor binding sites. We also assessed the ability of predictors to improve over the best available single template structure, which showed that the best groups produced models closer to the target structure than the best single template for a significant number of targets. In addition, we assessed the accuracy of the error estimates (local confidence values) assigned to predictions on a per residue basis. Finally, we discuss some general conclusions about the state of the art of template-based modeling methods and their usefulness for practical applications.

Automated server predictions in CASP7.

With each round of CASP (Critical Assessment of Techniques for Protein Structure Prediction), automated prediction servers have played an increasingly important role. Today, most protein structure prediction approaches in some way depend on automated methods for fold recognition or model building. The accuracy of server predictions has significantly increased over the last years, and, in CASP7, we observed a continuation of this trend. In the template-based modeling category, the best prediction server was ranked third overall, i.e. it outperformed all but two of the human participating groups. This server also ranked among the very best predictors in the free modeling category as well, being clearly beaten by only one human group. In the high accuracy (HA) subset of TBM, two of the top five groups were servers. This article summarizes the contribution of automated structure prediction servers in the CASP7 experiment, with emphasis on 3D structure prediction, as well as information on their prediction scope and public availability.

Structural and electrostatic properties of ubiquitination and related pathways.

Post-translational modification by ubiquitin and ubiquitin-like (UBL) proteins is a key mechanism for cellular control. The specificity of the enzymes of ubiquitination and their close paralogs is dependent on their molecular electrostatic potentials. For example, analysis of molecular electrostatic potentials and electrostatically key residues can account for the selectivity of different E1s (activating enzymes) and of different SUMO proteases. The molecular interactions of the ubiquitin conjugating enzymes, the ubiquitin family proteins (UFP) and UBL domains are discussed in detail. An interesting observation is that the Non Canonical Ubiquitin Conjugating Enzymes (NCUBEs) have electrostatic potentials that are more similar to the UBC9 orthologs, the SUMO conjugating enzymes, than they are to other ubiquitin conjugating enzymes. It had previously been suggested that UBC9 may select for SUMO based on its difference in electrostatic potential as compared to other E2s but the NCUBE exception suggests that this may not be the case. The web site http://www.ubiquitin-resource.org/ allows users to find the E2s most electrostatically similar to a query E2. Where possible, models have been made for all E2 domains in the SMART database (http://smart.embl-heidelberg.de/). A brief overview of molecular electrostatic potentials and their application to understanding protein function is also given.

Determinants of functionality in the ubiquitin conjugating enzyme family.

The E2 enzymes are key enzymes in the ubiquitin and ubiquitin-like protein ligation pathways. To understand the functionality of the different E2 enzymes, we analyzed 190 protein sequences and 211 structures and electrostatic potentials. Key findings include: The ScUbc1 orthologs are defined by a C-terminal UBA domain. An N-terminal sequence motif that is highly conserved in all E2s except for Cdc34 orthologs is important for the stabilization of the L7 loop and is likely to be involved in E1 binding. ScUbc11p has a different electrostatic potential from E2-Cp and other proteins with which it has high sequence similarity but different functionality. All the E2s known to ubiquitinate histones have a negative potential. The members of the NCUBE family have a positive electrostatic potential, although its form is different from that of the SUMO conjugating E2s. The specificities of only the ScUbc4/Ubc5 and ScUbc1p orthologs are reflected in their L4 and L7 loops.