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.

RNA polymerase III limits longevity downstream of TORC1.

Three distinct RNA polymerases transcribe different classes of genes in the eukaryotic nucleus. RNA polymerase (Pol) III is the essential, evolutionarily conserved enzyme that generates short, non-coding RNAs, including tRNAs and 5S rRNA. The historical focus on transcription of protein-coding genes has left the roles of Pol III in organismal physiology relatively unexplored. Target of rapamycin kinase complex 1 (TORC1) regulates Pol III activity, and is also an important determinant of longevity. This raises the possibility that Pol III is involved in ageing. Here we show that Pol III limits lifespan downstream of TORC1. We find that a reduction in Pol III extends chronological lifespan in yeast and organismal lifespan in worms and flies. Inhibiting the activity of Pol III in the gut of adult worms or flies is sufficient to extend lifespan; in flies, longevity can be achieved by Pol III inhibition specifically in intestinal stem cells. The longevity phenotype is associated with amelioration of age-related gut pathology and functional decline, dampened protein synthesis and increased tolerance of proteostatic stress. Pol III acts on lifespan downstream of TORC1, and limiting Pol III activity in the adult gut achieves the full longevity benefit of systemic TORC1 inhibition. Hence, Pol III is a pivotal mediator of this key nutrient-signalling network for longevity; the growth-promoting anabolic activity of Pol III mediates the acceleration of ageing by TORC1. The evolutionary conservation of Pol III affirms its potential as a therapeutic target.

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor.

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

pocketZebra: a web-server for automated selection and classification of subfamily-specific binding sites by bioinformatic analysis of diverse protein families.

The new web-server pocketZebra implements the power of bioinformatics and geometry-based structural approaches to identify and rank subfamily-specific binding sites in proteins by functional significance, and select particular positions in the structure that determine selective accommodation of ligands. A new scoring function has been developed to annotate binding sites by the presence of the subfamily-specific positions in diverse protein families. pocketZebra web-server has multiple input modes to meet the needs of users with different experience in bioinformatics. The server provides on-site visualization of the results as well as off-line version of the output in annotated text format and as PyMol sessions ready for structural analysis. pocketZebra can be used to study structure-function relationship and regulation in large protein superfamilies, classify functionally important binding sites and annotate proteins with unknown function. The server can be used to engineer ligand-binding sites and allosteric regulation of enzymes, or implemented in a drug discovery process to search for potential molecular targets and novel selective inhibitors/effectors. The server, documentation and examples are freely available at http://biokinet.belozersky.msu.ru/pocketzebra and there are no login requirements.

Correction to "Quantification and Mapping of Alkylation in the Human Genome Reveal Single Nucleotide Resolution Precursors of Mutational Signatures".

[This corrects the article DOI: 10.1021/acscentsci.2c01100.].

Trabectedin derails transcription-coupled nucleotide excision repair to induce DNA breaks in highly transcribed genes.

Most genotoxic anticancer agents fail in tumors with intact DNA repair. Therefore, trabectedin, anagent more toxic to cells with active DNA repair, specifically transcription-coupled nucleotide excision repair (TC-NER), provides therapeutic opportunities. To unlock the potential of trabectedin and inform its application in precision oncology, an understanding of the mechanism of the drug's TC-NER-dependent toxicity is needed. Here, we determine that abortive TC-NER of trabectedin-DNA adducts forms persistent single-strand breaks (SSBs) as the adducts block the second of the two sequential NER incisions. We map the 3'-hydroxyl groups of SSBs originating from the first NER incision at trabectedin lesions, recording TC-NER on a genome-wide scale. Trabectedin-induced SSBs primarily occur in transcribed strands of active genes and peak near transcription start sites. Frequent SSBs are also found outside gene bodies, connecting TC-NER to divergent transcription from promoters. This work advances the use of trabectedin for precision oncology and for studying TC-NER and transcription.

Temporal segregation of biosynthetic processes is responsible for metabolic oscillations during the budding yeast cell cycle.

Many cell biological and biochemical mechanisms controlling the fundamental process of eukaryotic cell division have been identified; however, the temporal dynamics of biosynthetic processes during the cell division cycle are still elusive. Here, we show that key biosynthetic processes are temporally segregated along the cell cycle. Using budding yeast as a model and single-cell methods to dynamically measure metabolic activity, we observe two peaks in protein synthesis, in the G1 and S/G2/M phase, whereas lipid and polysaccharide synthesis peaks only once, during the S/G2/M phase. Integrating the inferred biosynthetic rates into a thermodynamic-stoichiometric metabolic model, we find that this temporal segregation in biosynthetic processes causes flux changes in primary metabolism, with an acceleration of glucose-uptake flux in G1 and phase-shifted oscillations of oxygen and carbon dioxide exchanges. Through experimental validation of the model predictions, we demonstrate that primary metabolism oscillates with cell-cycle periodicity to satisfy the changing demands of biosynthetic processes exhibiting unexpected dynamics during the cell cycle.

Quantification and Mapping of Alkylation in the Human Genome Reveal Single Nucleotide Resolution Precursors of Mutational Signatures.

Chemical modifications to DNA bases, including DNA adducts arising from reactions with electrophilic chemicals, are well-known to impact cell growth, miscode during replication, and influence disease etiology. However, knowledge of how genomic sequences and structures influence the accumulation of alkylated DNA bases is not broadly characterized with high resolution, nor have these patterns been linked with overall quantities of modified bases in the genome. For benzo(a) pyrene (BaP), a ubiquitous environmental carcinogen, we developed a single-nucleotide resolution damage sequencing method to map in a human lung cell line the main mutagenic adduct arising from BaP. Furthermore, we combined this analysis with quantitative mass spectrometry to evaluate the dose-response profile of adduct formation. By comparing damage abundance with DNase hypersensitive sites, transcription levels, and other genome annotation data, we found that although overall adduct levels rose with increasing chemical exposure concentration, genomic distribution patterns consistently correlated with chromatin state and transcriptional status. Moreover, due to the single nucleotide resolution characteristics of this DNA damage map, we could determine preferred DNA triad sequence contexts for alkylation accumulation, revealing a characteristic DNA damage signature. This new BaP damage signature had a profile highly similar to mutational signatures identified previously in lung cancer genomes from smokers. Thus, these data provide insight on how genomic features shape the accumulation of alkylation products in the genome and predictive strategies for linking single-nucleotide resolution in vitro damage maps with human cancer mutations.

A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux.

RNA-based sensors for intracellular metabolites are a promising solution to the emerging issue of metabolic heterogeneity. However, their development, i.e., the conversion of an aptamer into an in vivo-functional intracellular metabolite sensor, still harbors challenges. Here, we accomplished this for the glycolytic flux-signaling metabolite, fructose-1,6-bisphosphate (FBP). Starting from in vitro selection of an aptamer, we constructed device libraries with a hammerhead ribozyme as actuator. Using high-throughput screening in yeast with fluorescence-activated cell sorting (FACS), next-generation sequencing, and genetic-environmental perturbations to modulate the intracellular FBP levels, we identified a sensor that generates ratiometric fluorescent readout. An abrogated response in sensor mutants and occurrence of two sensor conformations-revealed by RNA structural probing-indicated in vivo riboswitching activity. Microscopy showed that the sensor can differentiate cells with different glycolytic fluxes within yeast populations, opening research avenues into metabolic heterogeneity. We demonstrate the possibility to generate RNA-based sensors for intracellular metabolites for which no natural metabolite-binding RNA element exits.

Saccharomyces cerevisiae goes through distinct metabolic phases during its replicative lifespan.

A comprehensive description of the phenotypic changes during cellular aging is key towards unraveling its causal forces. Previously, we mapped age-related changes in the proteome and transcriptome (Janssens et al., 2015). Here, employing the same experimental procedure and model-based inference, we generate a comprehensive account of metabolic changes during the replicative life of Saccharomyces cerevisiae. With age, we found decreasing metabolite levels, decreasing growth and substrate uptake rates accompanied by a switch from aerobic fermentation to respiration, with glycerol and acetate production. The identified metabolic fluxes revealed an increase in redox cofactor turnover, likely to combat increased production of reactive oxygen species. The metabolic changes are possibly a result of the age-associated decrease in surface area per cell volume. With metabolism being an important factor of the cellular phenotype, this work complements our recent mapping of the transcriptomic and proteomic changes towards a holistic description of the cellular phenotype during aging.

Metabolic heterogeneity in clonal microbial populations.

In the past decades, numerous instances of phenotypic diversity were observed in clonal microbial populations, particularly, on the gene expression level. Much less is, however, known about phenotypic differences that occur on the level of metabolism. This is likely explained by the fact that experimental tools probing metabolism of single cells are still at an early stage of development. Here, we review recent exciting discoveries that point out different causes for metabolic heterogeneity within clonal microbial populations. These causes range from ecological factors and cell-inherent dynamics in constant environments to molecular noise in gene expression that propagates into metabolism. Furthermore, we provide an overview of current methods to quantify the levels of metabolites and biomass components in single cells.

Zebra: a web server for bioinformatic analysis of diverse protein families.

During evolution of proteins from a common ancestor, one functional property can be preserved while others can vary leading to functional diversity. A systematic study of the corresponding adaptive mutations provides a key to one of the most challenging problems of modern structural biology - understanding the impact of amino acid substitutions on protein function. The subfamily-specific positions (SSPs) are conserved within functional subfamilies but are different between them and, therefore, seem to be responsible for functional diversity in protein superfamilies. Consequently, a corresponding method to perform the bioinformatic analysis of sequence and structural data has to be implemented in the common laboratory practice to study the structure-function relationship in proteins and develop novel protein engineering strategies. This paper describes Zebra web server - a powerful remote platform that implements a novel bioinformatic analysis algorithm to study diverse protein families. It is the first application that provides specificity determinants at different levels of functional classification, therefore addressing complex functional diversity of large superfamilies. Statistical analysis is implemented to automatically select a set of highly significant SSPs to be used as hotspots for directed evolution or rational design experiments and analyzed studying the structure-function relationship. Zebra results are provided in two ways - (1) as a single all-in-one parsable text file and (2) as PyMol sessions with structural representation of SSPs. Zebra web server is available at http://biokinet.belozersky.msu.ru/zebra .