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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Improved sgRNA design in bacteria via genome-wide activity profiling

CRISPR/Cas9 is a promising tool in prokaryotic genome engineering, but its success is limited by the widely varying on-target activity of single guide RNAs (sgRNAs). Based on the association of CRISPR/Cas9-induced DNA cleavage with cellular lethality, we systematically profiled sgRNA activity by co-expressing a genome-scale library (~70,000 sgRNAs) with Cas9 or its specificity-improved mutant in E. coli. Based on this large-scale dataset, we constructed a comprehensive and high-density sgRNA activity map, which enables selecting highly active sgRNAs for any locus across the genome in this model organism. We also identified resistant genomic loci with respect to CRISPR/Cas9 activity, notwithstanding the highly accessible DNA in bacterial cells. Moreover, we found that previous sgRNA activity prediction models that were trained on mammalian cell datasets were inadequate when coping with our results, highlighting the key limitations and biases of previous models. We hence developed an integrated algorithm to accurately predict highly effective sgRNAs, aiming to facilitate the design of CRISPR/Cas9-based genome engineering or screenings in bacteria. We also isolated the important sgRNA features that contribute to DNA cleavage and characterized their key differences among wild type Cas9 and its mutant, shedding light on the biophysical mechanisms of the CRISPR/Cas9 system.

microbiology↗

bioRxiv2018

The conserved phosphatase GSP-2/PP1 promotes germline immortality via small RNA-mediated genome silencing during meiosis

Genomic silencing can promote germ cell immortality, or transgenerational maintenance of the germ line, via mechanisms that may occur during mitosis or meiosis. Here we report that the gsp-2 PP1/Glc7 phosphatase promotes germ cell immortality. We identified a separation-of-function allele of C. elegans GSP-2 that caused a meiosis-specific chromosome segregation defect and defects in transgenerational small RNA-induced genome silencing. GSP-2 is recruited to meiotic chromosomes by LAB-1, which also promoted germ cell immortality. Sterile gsp-2 and lab-1 mutant adults displayed germline degeneration, univalents and histone phosphorylation defects in oocytes, similar to small RNA genome silencing mutants. Epistasis and RNA analysis suggested that GSP-2 functions downstream of small RNAs. We conclude that a meiosis-specific function of GSP-2/LAB-1 ties small RNA-mediated silencing of the epigenome to germ cell immortality. Given that hemizygous genetic elements can drive transgenerational epigenomic silencing, and given that LAB-1 promotes pairing of homologous chromosomes and localizes to the interface between homologous chromosomes during pachytene, we suggest that discontinuities at this interface could promote nuclear silencing in a manner that depends on GSP-2.\n\nAuthor SummaryThe germ line of an organism is considered immortal in its capacity to give rise to an unlimited number of future generations. To protect the integrity of the germ line, mechanisms act to suppress the accumulation of transgenerational damage to the genome or epigenome. Loss of germ cell immortality can result from mutations that disrupt the small RNA-mediated silencing pathway that helps to protect the integrity of the epigenome. Here we report for the first time that the C. elegans protein phosphatase GSP-2 that promotes core chromosome biology functions during meiosis is also required for germ cell immortality. Specifically, we identified a partial loss of function allele of gsp-2 that exhibits defects in meiotic chromosome segregation and is also dysfunctional for transgenerational small RNA-mediated genome silencing. Our results are consistent with a known role of Drosophila Protein Phosphatase 1 in heterochromatin silencing, and point to a meiotic phosphatase function that is relevant to germ cell immortality, conceivably related to its roles in chromosome pairing or sister chromatid cohesion.

genetics↗

bioRxiv2018

Efficient CRISPR/Cas9-based genome editing and its application to conditional genetic analysis in Marchantia polymorpha

Marchantia polymorpha is one of the model species of basal land plants. Although CRISPR/Cas9-based genome editing has already been demonstrated for this plant, the efficiency was too low to apply to functional analysis. In this study, we show the establishment of CRISPR/Cas9 genome editing vectors with high efficiency for both construction and genome editing. Codon optimization of Cas9 to Arabidopsis achieved over 70% genome editing efficiency at two loci tested. Systematic assessment revealed that guide sequences of 17 nt or shorter dramatically decreased this efficiency. We also demonstrated that a combinatorial use of this system and a floxed complementation construct enabled conditional analysis of a nearly essential gene. This study reports that simple, rapid, and efficient genome editing is feasible with the series of developed vectors.

plant biology↗

bioRxiv2018

Population genetics of Paramecium mitochondrial genomes: recombination, mutation spectrum, and efficacy of selection.

The evolution of mitochondrial genomes and their population-genetic environment among unicellular eukaryotes are understudied. Ciliate mitochondrial genomes exhibit a unique combination of characteristics, including a linear organization and the presence of multiple genes with no known function or detectable homologs in other eukaryotes. Here we study the variation of ciliate mitochondrial genomes both within and across thirteen highly diverged Paramecium species, including multiple species from the P. aurelia species complex, with four outgroup species: P. caudatum, P. multimicronucleatum, and two strains that may represent novel related species. We observe extraordinary conservation of gene order and protein-coding content in Paramecium mitochondria across species. In contrast, significant differences are observed in tRNA content and copy number, which is highly conserved in species belonging to the P. aurelia complex but variable among and even within the other Paramecium species. There is an increase in GC content from ~20% to ~40% on the branch leading to the P. aurelia complex. Patterns of polymorphism in population-genomic data and mutation-accumulation experiments suggest that the increase in GC content is primarily due to changes in the mutation spectra in the P. aurelia species. Finally, we find no evidence of recombination in Paramecium mitochondria and find that the mitochondrial genome appears to experience either similar or stronger efficacy of purifying selection than the nucleus.

evolutionary biology↗

bioRxiv2018

Continuous-trait probabilistic model for comparing multi-species functional genomic data

A large amount of multi-species functional genomic data from high-throughput assays are becoming available to help understand the molecular mechanisms for phenotypic diversity across species. However, continuous-trait probabilistic models, which are key to such comparative analysis, remain underexplored. Here we develop a new model, called phylogenetic hidden Markov Gaussian processes (Phylo-HMGP), to simultaneously infer heterogeneous evolutionary states of functional genomic features in a genome-wide manner. Both simulation studies and real data application demonstrate the effectiveness of Phylo-HMGP. Importantly, we applied Phylo-HMGP to analyze a new cross-species DNA replication timing (RT) dataset from the same cell type in five primate species (human, chimpanzee, orangutan, gibbon, and green monkey). We demonstrate that our Phylo-HMGP model enables discovery of genomic regions with distinct evolutionary patterns of RT. Our method provides a generic framework for comparative analysis of multi-species continuous functional genomic signals to help reveal regions with conserved or lineage-specific regulatory roles.

bioinformatics↗

bioRxiv2018

Recombination rate variation shapes barriers to introgression across butterfly genomes

Hybridisation and introgression can dramatically alter the relationships among groups of species, leading to phylogenetic discordance across the genome and between populations. Introgression can also erode species differences over time, but selection against introgression at certain loci acts to maintain post-mating species barriers. Theory predicts that species barriers made up of many loci throughout the genome should lead to a broad correlation between introgression and recombination rate, which determines the extent to which selection on deleterious foreign alleles will affect neutral alleles at physically linked loci. Here we describe the variation in genealogical relationships across the genome among three species of Heliconius butterflies: H. melpomene, H. cydno and H. timareta, using whole genomes of 92 individuals, and ask whether this variation can be explained by heterogeneous barriers to introgression. We find that species relationships vary predictably at the chromosomal scale. By quantifying recombination rate and admixture proportions, we then show that rates of introgression are predicted by variation in recombination rate. This implies that species barriers are highly polygenic, with selection acting against introgressed alleles across most of the genome. In addition, long chromosomes, which have lower recombination rates, produce stronger barriers on average than short chromosomes. Finally, we find a consistent difference between two species pairs on either side of the Andes, which suggests differences in the architecture of the species barriers. Our findings illustrate how the combined effects of hybridisation, recombination and natural selection, acting at multitudes of loci over long periods, can dramatically sculpt the phylogenetic relationships among species.

evolutionary biology↗

bioRxiv2018

A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates a k-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 460 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on the K. pneumoniae test set, 88% on the P. aeruginosa test set and 96.5% on the C. difficile test set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.\n\nPhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).\n\nSummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

bioinformatics↗

bioRxiv2018

Characterization of genomic DNA of lactic acid bacteria for activation of plasmacytoid dendritic cells .

Lactococcus lactis strain Plasma (LC-Plasma) possesses strong activity of stimulating plasmacytoid dendritic cells (pDCs) via the TLR9-Myd88 pathway. To reveal the effective genome structure for pDCs stimulatory activity, we performed an in vitro screening, using randomly selected DNA fragments from the LC-Plasma genome. The results showed that CpG motifs are necessary factor for active DNA fragment, but the copy number of CpG motifs did not show strong correlation to the pDCs stimulatory activity of DNA fragment. We also found that the G+C contents of DNA fragments have significant negative effects on pDCs stimulatory activity. We also performed bioinformatics analysis of genome of lactic acid bacteria (LAB) and investigated the relation between CpG copy number in the genome and pDCs stimulatory activity. We found that strains of lactic acid bacteria (LAB) with high copy number of CpG motifs in the low-G+C region of the genome had higher probability of having high pDCs stimulatory activity. Three species, L.lactis subsp. lactis, Leuconostoc mesenteroides, and Pediococcus pentosaceus were the typical examples of high pDCs stimulatory activity LAB.\n\nImportanceThis study provides a new perspective on the structure of DNA fragments that are able to activate pDCs via the TLR9-Myd88 pathway. The information from this study should be useful for designing new DNA fragments, including phosphodiesterbond-DNA oligomers containing CpG motifs and DNA-containing vaccines. This work also presented an in silico screening method for identifying bacterial species that are able to activate pDCs. Therefore, this study should be useful for providing data for the development of vaccine adjuvants and therapeutics for infectious and allergic diseases.

microbiology↗

bioRxiv2018

Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results

The independent reproduction of research results is a cornerstone of experimental research, yet it is beset by numerous challenges, including the quality and veracity of reagents and materials. Much of life science research depends on life materials, including human tissue culture cells. In this study we aimed at determining the degree of variability in the molecular makeup and the ensuing phenotypic consequences in commonly used human tissue culture cells. We collected 14 stock HeLa aliquots from 13 different laboratories across the globe, cultured them in uniform conditions and profiled the genome-wide copy numbers, mRNAs, proteins and protein turnover rates via genomic techniques and SWATH mass spectrometry, respectively. We also phenotyped each cell line with respect to the ability of transfected Let7 mimics to modulate Salmonella infection.\n\nWe discovered significant heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto variety. We also observed progressive divergence within a specific cell line over 50 successive passages. From the aggregate multi-omic datasets we quantified the response of the cells to genomic variability across the transcriptome and proteome. We discovered organelle-specific proteome remodeling and buffering of protein abundance by protein complex stoichiometry, mediated by the adaptation of protein turnover rates. By associating quantitative proteotype and phenotype measurements we identified protein patterns that explained the varying response of the different cell lines to Salmonella infection.\n\nAltogether the results indicate a striking degree of genomic variability, the rapid evolution of genomic variability in culture and its complex translation into distinctive expressed molecular and phenotypic patterns. The results have broad implications for the interpretation and reproducibility of research results obtained from HeLa cells and provide important basis for a general discussion of the value and requirements for communicating research results obtained from human tissue culture cells.

systems biology↗

bioRxiv2018

Expanding the size limit of RNA viruses: Evidence of a novel divergent nidovirus in California sea hare, with a ~35.9 kb virus genome

While RNA viruses thrive with massive structural and functional diversity, their genomes size variation is particularly low, ranging only from ~2-to-33 kb. Here, I present the characterization of RNA sequences corresponding to the first virus associated with Aplysia californica. Genome structure and domain architecture suggest that the identified virus is a novel member of Nidovirales. The proposed aplysia californica nido-like virus (AcNV), with a genome sequence of ca.35,906 nt, represents the longest ever recorded RNA virus yet. Phylogenetic insights indicate that AcNV clusters in a major phylloclade of unclassified invertebrate nidoviruses, Roniviridae, and Mesoniviridae. Basal branching in this emerging cluster could indicate that AcNV is a member of a novel divergent clade within Nidovirales. Further, virus RNA detection in multiple independent studies suggests that AcNV is neurotropic with a broad cell/tissue/organ tropism, supported by AcNV occurrence in diverse organs, including the first detection of a Nidovirales in single specific neurons.\n\nHighlights-RNA virus genomes reported in the literature are limited at ca. 33.4 kb\n-A novel nidovirus was identified in the gastropod mollusk Aplysia californica\n-The aplysia californica nido-like virus (AcNV) presents a 35.9 kb RNA genome\n-AcNV has a broad tropism, is enriched in the CNS, and accumulates in neurons\n-The unique features of A. californica enables single-neuron virus dynamics of AcNV

microbiology↗

bioRxiv2018

Neolithic and Medieval virus genomes reveal complex evolution of Hepatitis B

The hepatitis B virus (HBV) is one of the most widespread human pathogens known today, yet its origin and evolutionary history are still unclear and controversial. Here, we report the analysis of three ancient HBV genomes recovered from human skeletons found at three different archaeological sites in Germany. We reconstructed two Neolithic and one medieval HBV genomes by de novo assembly from shotgun DNA sequencing data. Additionally, we observed HBV-specific peptides using paleo-proteomics. Our results show that HBV circulates in the European population for at least 7000 years. The Neolithic HBV genomes show a high genomic similarity to each other. In a phylogenetic network, they do not group with any human-associated HBV genome and are most closely related to those infecting African non-human primates. These ancient virus forms appear to represent distinct lineages that have no close relatives today and went possibly extinct. Our results reveal the great potential of ancient DNA from human skeletons in order to study the long-time evolution of blood borne viruses.

evolutionary biology↗

bioRxiv2018

LOW-BIAS RNA SEQUENCING OF THE HIV-2 GENOME FROM BLOOD PLASMA

Accurate determination of the genetic diversity present in the HIV quasi-species is critical for the development of a preventative vaccine: in particular, little is known about viral genetic diversity for the second strain of HIV, HIV-2. A better understanding of HIV-2 biology is relevant to the HIV vaccine field because a substantial proportion of infected people experience long-term viral control, and prior HIV-2 infection is associated with slower HIV-1 disease progression in co-infected subjects. The majority of traditional and next generation sequencing methods have relied on target amplification prior to sequencing, introducing biases that may obscure the true signals of diversity in the viral population. Additionally, target-enrichment through PCR requires a priori sequence knowledge, which is lacking for HIV-2. Therefore, a target enrichment free method of library preparation would be valuable for the field. We applied an RNA shotgun sequencing (RNA-Seq) method without PCR amplification to cultured viral stocks and patient plasma samples from HIV-2 infected individuals. Libraries generated from total plasma RNA were analysed with a two-step pipeline: (1) de novo genome assembly, followed by (2) read re-mapping. By this approach, whole genome sequences were generated with a 28x-67x mean depth of coverage. Assembled reads showed a low level of GC-bias and comparison of the genome diversity on the intra-host level showed low diversity in the accessory gene vpx in all patients. Our study demonstrates that RNA-Seq is a feasible full-genome de novo sequencing method for blood plasma samples collected from HIV-2 infected individuals.\n\nIMPORTANCEAn accurate picture of viral genetic diversity is critical for the development of a globally effective HIV vaccine. However, sequencing strategies are often complicated by target enrichment prior to sequencing, introducing biases that can distort variant frequencies, which are not easily corrected for in downstream analyses. Additionally, detailed a priori sequence knowledge is needed to inform robust primer design when employing PCR amplification, a factor that is often lacking when working with tropical diseases localised in developing countries. Previous work has demonstrated that direct RNA shotgun sequencing (RNA-Seq) can be used to circumvent these issues for HCV and Norovirus. We applied shotgun RNA sequencing (RNA-Seq) to total RNA extracted from HIV-2 blood plasma samples, demonstrating the applicability of this technique to HIV-2 and allowing us to generate a dynamic picture of genetic diversity over the whole genome of HIV-2 in the context of low-bias sequencing.

microbiology↗

bioRxiv2018

The UCSC Xena Platform for cancer genomics data visualization and interpretation

UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

cancer biology↗

bioRxiv2018

Genome sequence and description of Blautia brookingsii str SG772 nov., a new species of anaerobic bacterium isolated from healthy human gut

Strain SG-772 is a Gram positive, strictly anoxic bacterium isolated from the feces of a healthy human fecal donor. Based on 16S rRNA gene sequence, the strain showed maximum similarity (94.39%) with Blautia stercoris GAM6-1 in EZ-Taxon server and thus assigned the genus Blautia. The scanning electron micrograph of the bacterium revealed the characteristic coccobacillus shape as well as the complete absence of flagellum, suggesting its non-motile phenotype. This strain was found to utilize 27 substrates based on Biolog AN plates assay, with maximum preference for D-mannitol. Additionally, the strain was found to be resistant to tetracycline and streptomycin. Genome sequencing and analysis revealed an overall genome size of 3.49 Mbp and GC content of 43.97%. Based on RAST annotation server, the closest neighbor was Blautia hansenii DSM20583. Average Nucleotide Identity (ANI) of these strains were 81.69%, suggesting a high level of genomic variation. The comparative genome analysis of strain SG772 with B. hansenii DSM20583 revealed a total of 411 orthologous genes coding for basic metabolic functions. Furthermore, the genomes were functionally distinct based on COG categories. Thus, based on all these differences, we propose a novel species of genus Blautia named as Blautia brookingsii SG772.

microbiology↗

bioRxiv2018

plyranges: A grammar of genomic data transformation

The Bioconductor project provides many interoperable data abstractions for analyzing high-throughput genomics experiments; however implementing a typical genomic workflow with Bioconductor requires learning these abstractions and understanding them at an integrative level. This places a large cognitive burden on the user, especially for non-programmers. To reduce this burden we have created a grammar of genomic data transformation that operates on a single, central Bioconductor data structure, GRanges, which naturally represents genomic intervals and their associated measurements. The grammar defines verbs for performing actions on and between genomic interval data through a simplified, coherent interface to existing Bioconductor infrastructure, resulting in fluent analysis workflows. We have implemented this grammar as an R/Bioconductor package called plyranges.

bioinformatics↗

bioRxiv2018

Metabolic marker gene mining provides insight in global mcrA diversity and, coupled with targeted genome reconstruction, sheds light on metabolic versatility of the Methanomassiliicoccales

Over the past years, metagenomics has revolutionized our view of microbial diversity. Moreover, extracting near-complete genomes from metagenomes has led to the discovery of known metabolic traits in unsuspected lineages. Genome-resolved metagenomics relies on assembly of the sequencing reads and subsequent binning of assembled contigs, which might be hampered by strain heterogeneity or low abundance of a target organism. Here we present a complementary approach, metagenome marker gene mining, and use it to assess the global diversity of archaeal methane metabolism through the mcrA gene. To this end, we have screened 18,465 metagenomes for the presence of reads matching a database representative of all known mcrA proteins and reconstructed gene sequences from the matching reads. We use our mcrA dataset to assess the environmental distribution of the Methanomassiliicoccales and reconstruct and analyze a draft genome belonging to the Lake Pavin cluster, an understudied environmental clade of the Methanomassiliicoccales. Thus, we show that marker gene mining can enhance the discovery power of metagenomics, by identifying novel lineages and aiding selection of targets for in-depth analyses. Marker gene mining is less sensitive to strain heterogeneity and has a lower abundance threshold than genome-resolved metagenomics, as it only requires short contigs and there is no binning step. Additionally, it is computationally cheaper than genome resolved metagenomics, since only a small subset of reads needs to be assembled. It is therefore a suitable approach to extract knowledge from the many publicly available sequencing projects.

microbiology↗

bioRxiv2018

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The diagnosis is typically made by measuring a few known determinants previously identified from whole genome sequencing, and thus is restricted to existing information on biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average F1 score of 0.88 on held-out data (range 0.66-0.96). While the best models most frequently employed all inputs, an average F1 score of 0.73 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and failed to improve prediction for ten out of 11 antibiotics. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data are informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.\n\nSummaryOne of the major health threats of 21st century is emergence of antibiotic resistance. To manage its economic impact, efforts are made to develop novel diagnostic tools that rapidly detect resistant strains in clinical settings. In our study, we employed a range machine learning tools to predict antibiotic resistance from whole genome sequencing data for E. coli. We used the presence or absence of genes, population structure and isolation year of isolates as predictors, and could attain average precision of 0.93 and recall of 0.83, without prior knowledge about the causal mechanisms. These results demonstrate the potential application of machine learning methods as a diagnostic tool in healthcare settings.

bioinformatics↗

bioRxiv2018

The Chromatin-binding Protein Spn1 contributes to Genome Instability in Saccharomyces cerevisiae

Cells expend a large amount of energy to maintain their DNA sequence. DNA repair pathways, cell cycle checkpoint activation, proofreading polymerases, and chromatin structure are ways in which the cell minimizes changes to the genome. During replication, the DNA damage tolerance pathway allows the replication forks to bypass damage on the template strand. This avoids prolonged replication fork stalling, which can contribute to genome instability. The DNA damage tolerance pathway includes two sub-pathways: translesion synthesis and template switch. Post-translational modification of PCNA and the histone tails, cell cycle phase, and local DNA structure have all been shown to influence sub-pathway choice. Chromatin architecture contributes to maintaining genome stability by providing physical protection of the DNA and by regulating DNA processing pathways. As such, chromatin-binding factors have been implicated in maintaining genome stability. Using Saccharomyces cerevisiae, we examined the role of Spn1, a chromatin binding and transcription elongation factor, in DNA damage tolerance. Expression of a mutant allele of SPN1 results in increased resistance to the DNA damaging agent methyl methanesulfonate, lower spontaneous and damage-induced mutation rates, along with increased chronological lifespan. We attribute these effects to an increased usage of the template switch branch of the DNA damage tolerance pathway in the spn1 strain. This provides evidence for a role of wild type Spn1 in promoting genome instability, as well as having ties to overcoming replication stress and contributing to chronological aging.

genetics↗