A Comprehensive Genome-Wide Map of Autonomously Replicating Sequences in a Naive Genome

Eukaryotic chromosomes initiate DNA synthesis from multiple replication origins. The machinery that initiates DNA synthesis is highly conserved, but the sites where the replication initiation proteins bind have diverged significantly. Functional comparative genomics is an obvious approach to study the evolution of replication origins. However, to date, the Saccharomyces cerevisiae replication origin map is the only genome map available. Using an iterative approach that combines computational prediction and functional validation, we have generated a high-resolution genome-wide map of DNA replication origins in Kluyveromyces lactis. Unlike other yeasts or metazoans, K. lactis autonomously replicating sequences (KlARSs) contain a 50 bp consensus motif suggestive of a dimeric structure. This motif is necessary and largely sufficient for initiation and was used to dependably identify 145 of the up to 156 non-repetitive intergenic ARSs projected for the K. lactis genome. Though similar in genome sizes, K. lactis has half as many ARSs as its distant relative S. cerevisiae. Comparative genomic analysis shows that ARSs in K. lactis and S. cerevisiae preferentially localize to non-syntenic intergenic regions, linking ARSs with loci of accelerated evolutionary change

Ipond2: The Next Generation The Development And Application Of Improved Methods For Assessment Of Replisome Protein Dynamics

Faithful replication of the genome during cell division is essential for the avoidance of disease-promoting mutations. Until recently, accurate quantification of replication factor alterations in response to cellular stress primarily relied on low sensitivity assays such as cell staining and nuclear extraction assays. In the past few years, the Cortez, Groth, and Santocanale laboratories have developed procedures to retrieve and analyze proteins associated with actively replicating DNA (iPOND, NCC, and Dm-Chp, respectively). Herein, we report improvements to iPOND that increase protein yield and quantitative sensitivity, as well as permit better statistical evaluation of candidate factors (iPOND2). These improvements were achieved by employment of sucrose based density gradient fractionation of samples prior to EdU-biotin retrievals. The use of iPOND2 increased the dynamic range of protein quantification by Mass Spec by more than 40-fold compared to recent iPOND. We investigated the replisome component response to stress and assessed the role of p97-mediated degradation in protein turnover at the fork with or without cell cycle checkpoint protein, ATR. Furthermore, increased replisome component yields permitted the detection of ubiquitylated peptides without secondary affinity-based retrievals. Due to the increased yield of iPOND2 we were able to combine iPOND2 with other purification methods such as K-ɛ-GG IP to gain further utility from the addition of sucrose fractionation to iPOND. For example, we have further improved our ability to analyze ubiquitin sites on the replisome in a high-throughput way and potentially developed a method capable of assessing terminated fork or origin composition across a variety of treatment conditions. In summary, iPOND2 exhibits greatly improved replisome retrieval specificity, yield, quantitative dynamic range, and statistical power to detect changes in replication-associated factors in response to stress conditions. iPOND2 can be used alone, in conjunction with a variety of MS analytical methods, and combined with secondary affinity purifications to improve our understanding of replisome dynamics following stress

Inactivation of pathogens on food and contact surfaces using ozone as a biocidal agent

This study focuses on the inactivation of a range of food borne pathogens using ozone as a biocidal agent. Experiments were carried out using Campylobacter jejuni, E. coli and Salmonella enteritidis in which population size effects and different treatment temperatures were investigate

Classifying transcription factor targets and discovering relevant biological features

<p>Abstract</p> <p>Background</p> <p>An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.</p> <p>Principal Findings</p> <p>(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4).</p> <p>(2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression.</p> <p>(3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties.</p> <p>(4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.</p> <p>Conclusion</p> <p>Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.</p> <p>Reviewers</p> <p>This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor.</p

Computational identification of transcriptional regulatory elements in DNA sequence

Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. The availability of complete genome sequence from multiple organisms, as well as mRNA profiling and high-throughput experimental methods for mapping protein-binding sites in DNA, have contributed to the development of methods that utilize these auxiliary data to inform the detection of transcriptional regulatory elements. Progress is also being made in the identification of cis-regulatory modules and higher order structures of the regulatory sequences, which is essential to the understanding of transcription regulation in the metazoan genomes. This article reviews the computational approaches for modeling and identification of genomic regulatory elements, with an emphasis on the recent developments, and current challenges