The Dynamics of Homologous Pairing during Mating Type Interconversion in Budding Yeast

Cells repair most double-strand breaks (DSBs) that arise during replication or by environmental insults through homologous recombination, a high-fidelity process critical for maintenance of genomic integrity. However, neither the detailed mechanism of homologous recombination nor the specific roles of critical components of the recombination machinery—such as Bloom and Werner syndrome proteins—have been resolved. We have taken a novel approach to examining the mechanism of homologous recombination by tracking both a DSB and the template from which it is repaired during the repair process in individual yeast cells. The two loci were labeled with arrays of DNA binding sites and visualized in live cells expressing green fluorescent protein–DNA binding protein chimeras. Following induction of an endonuclease that introduces a DSB next to one of the marked loci, live cells were imaged repeatedly to determine the relative positions of the DSB and the template locus. We found a significant increase in persistent associations between donor and recipient loci following formation of the DSB, demonstrating DSB-induced pairing between donor and template. However, such associations were transient and occurred repeatedly in every cell, a result not predicted from previous studies on populations of cells. Moreover, these associations were absent in sgs1 or srs2 mutants, yeast homologs of the Bloom and Werner syndrome genes, but were enhanced in a rad54 mutant, whose protein product promotes efficient strand exchange in vitro. Our results indicate that a DSB makes multiple and reversible contacts with a template during the repair process, suggesting that repair could involve interactions with multiple templates, potentially creating novel combinations of sequences at the repair site. Our results further suggest that both Sgs1 and Srs2 are required for efficient completion of recombination and that Rad54 may serve to dissociate such interactions. Finally, these results demonstrate that mechanistic insights into recombination not accessible from studies of populations of cells emerge from observations of individual cells

Decoding Complex Chemical Mixtures with a Physical Model of a Sensor Array

Combinatorial sensor arrays, such as the olfactory system, can detect a large number of analytes using a relatively small number of receptors. However, the complex pattern of receptor responses to even a single analyte, coupled with the non-linearity of responses to mixtures of analytes, makes quantitative prediction of compound concentrations in a mixture a challenging task. Here we develop a physical model that explicitly takes receptor-ligand interactions into account, and apply it to infer concentrations of highly related sugar nucleotides from the output of four engineered G-protein-coupled receptors. We also derive design principles that enable accurate mixture discrimination with cross-specific sensor arrays. The optimal sensor parameters exhibit relatively weak dependence on component concentrations, making a single designed array useful for analyzing a sizable range of mixtures. The maximum number of mixture components that can be successfully discriminated is twice the number of sensors in the array. Finally, antagonistic receptor responses, well-known to play an important role in natural olfactory systems, prove to be essential for the accurate prediction of component concentrations

Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interesting biological insights. In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust.</p> <p>Results</p> <p>We apply our proposed iterative algorithm to three sets of experimental DNA microarray data from experiments with the yeast <it>Saccharomyces cerevisiae </it>and show that the proposed iterative approach improves biological coherence. Comparison with other clustering techniques suggests that our iterative algorithm provides superior performance with regard to biological coherence. An important consequence of our approach is that an increasing proportion of genes find membership in clusters of high biological coherence and that the average cluster specificity improves.</p> <p>Conclusion</p> <p>The results from these clustering experiments provide a robust basis for extracting motifs and trans-acting factors that determine particular patterns of expression. In addition, the biological coherence of the clusters is iteratively assessed independently of the clustering. Thus, this method will not be severely impacted by functional annotations that are missing, inaccurate, or sparse.</p

The yeast genome undergoes significant topological reorganization in quiescence

We have examined the three-dimensional organization of the yeast genome during quiescence by a chromosome capture technique as a means of understanding how genome organization changes during development. For exponentially growing cells we observe high levels of inter-centromeric interaction but otherwise a predominance of intrachromosomal interactions over interchromosomal interactions, consistent with aggregation of centromeres at the spindle pole body and compartmentalization of individual chromosomes within the nucleoplasm. Three major changes occur in the organization of the quiescent cell genome. First, intrachromosomal associations increase at longer distances in quiescence as compared to growing cells. This suggests that chromosomes undergo condensation in quiescence, which we confirmed by microscopy by measurement of the intrachromosomal distances between two sites on one chromosome. This compaction in quiescence requires the condensin complex. Second, inter-centromeric interactions decrease, consistent with prior data indicating that centromeres disperse along an array of microtubules during quiescence. Third, inter-telomeric interactions significantly increase in quiescence, an observation also confirmed by direct measurement. Thus, survival during quiescence is associated with substantial topological reorganization of the genome

Genetically Modified Labeling Policies: Moving Forward or Backward?

One of the priorities to address food security is to increase the access of farmers to biotechnology, through the application of scientific advances, such as genetically modified organisms and food (GMF). However, the spread of (mis)information about their safety strengthens the clamor for mandatory GMF labeling. This paper provides an overview of food labeling policies, considering the principles suggested by the Codex Alimentarius Commission, and analyzes the consequences for the world food security of the Brazilian labeling policies compared to developed countries. We discuss the discriminatory application of GMF mandatory labeling in the absence of any scientific evidence as it has the potential of causing social harm and jeopardizes research, production, and distribution of food and consumers' right to information

Predicting Cellular Growth from Gene Expression Signatures

Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate

Microarray Profiling of Phage-Display Selections for Rapid Mapping of Transcription Factor–DNA Interactions

Modern computational methods are revealing putative transcription-factor (TF) binding sites at an extraordinary rate. However, the major challenge in studying transcriptional networks is to map these regulatory element predictions to the protein transcription factors that bind them. We have developed a microarray-based profiling of phage-display selection (MaPS) strategy that allows rapid and global survey of an organism's proteome for sequence-specific interactions with such putative DNA regulatory elements. Application to a variety of known yeast TF binding sites successfully identified the cognate TF from the background of a complex whole-proteome library. These factors contain DNA-binding domains from diverse families, including Myb, TEA, MADS box, and C2H2 zinc-finger. Using MaPS, we identified Dot6 as a trans-active partner of the long-predicted orphan yeast element Polymerase A & C (PAC). MaPS technology should enable rapid and proteome-scale study of bi-molecular interactions within transcriptional networks

NOS1AP is a novel molecular target and critical factor in TDP-43 pathology

Cappelli et al. reported that Nitric Oxide Synthase 1 Adaptor Protein is a co-regulated transcript of the TAR DNA-binding protein 43 kDa, reduced in amyotrophic lateral sclerosis and frontotemporal lobar degeneration patients with TAR DNA-binding protein 43 kDa pathology. Overall, their results highlight Nitric Oxide Synthase 1 Adaptor Protein as a novel druggable disease-relevant gene in TAR DNA-binding protein 43 kDa-related proteinopathies.Many lines of evidence have highlighted the role played by heterogeneous nuclear ribonucleoproteins in amyotrophic lateral sclerosis. In this study, we have aimed to identify transcripts co-regulated by TAR DNA-binding protein 43 kDa and highly conserved heterogeneous nuclear ribonucleoproteins which have been previously shown to regulate TAR DNA-binding protein 43 kDa toxicity (deleted in azoospermia-associated protein 1, heterogeneous nuclear ribonucleoprotein -Q, -D, -K and -U). Using the transcriptome analyses, we have uncovered that Nitric Oxide Synthase 1 Adaptor Protein mRNA is a direct TAR DNA-binding protein 43 kDa target, and in flies, its modulation alone can rescue TAR DNA-binding protein 43 kDa pathology. In primary mouse cortical neurons, we show that TAR DNA-binding protein 43 kDa mediated downregulation of Nitric Oxide Synthase 1 Adaptor Protein expression strongly affects the NMDA-receptor signalling pathway. In human patients, the downregulation of Nitric Oxide Synthase 1 Adaptor Protein mRNA strongly correlates with TAR DNA-binding protein 43 kDa proteinopathy as measured by cryptic Stathmin-2 and Unc-13 homolog A cryptic exon inclusion. Overall, our results demonstrate that Nitric Oxide Synthase 1 Adaptor Protein may represent a novel disease-relevant gene, potentially suitable for the development of new therapeutic strategies

The Origin Recognition Complex Interacts with a Subset of Metabolic Genes Tightly Linked to Origins of Replication

The origin recognition complex (ORC) marks chromosomal sites as replication origins and is essential for replication initiation. In yeast, ORC also binds to DNA elements called silencers, where its primary function is to recruit silent information regulator (SIR) proteins to establish transcriptional silencing. Indeed, silencers function poorly as chromosomal origins. Several genetic, molecular, and biochemical studies of HMR-E have led to a model proposing that when ORC becomes limiting in the cell (such as in the orc2-1 mutant) only sites that bind ORC tightly (such as HMR-E) remain fully occupied by ORC, while lower affinity sites, including many origins, lose ORC occupancy. Since HMR-E possessed a unique non-replication function, we reasoned that other tight sites might reveal novel functions for ORC on chromosomes. Therefore, we comprehensively determined ORC “affinity” genome-wide by performing an ORC ChIP–on–chip in ORC2 and orc2-1 strains. Here we describe a novel group of orc2-1–resistant ORC–interacting chromosomal sites (ORF–ORC sites) that did not function as replication origins or silencers. Instead, ORF–ORC sites were comprised of protein-coding regions of highly transcribed metabolic genes. In contrast to the ORC–silencer paradigm, transcriptional activation promoted ORC association with these genes. Remarkably, ORF–ORC genes were enriched in proximity to origins of replication and, in several instances, were transcriptionally regulated by these origins. Taken together, these results suggest a surprising connection among ORC, replication origins, and cellular metabolism

Rare variant analyses validate known ALS genes in a multi-ethnic population and identifies ANTXR2 as a candidate in PLS

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting over 300,000 people worldwide. It is characterized by the progressive decline of the nervous system that leads to the weakening of muscles which impacts physical function. Approximately, 15% of individuals diagnosed with ALS have a known genetic variant that contributes to their disease. As therapies that slow or prevent symptoms continue to develop, such as antisense oligonucleotides, it is important to discover novel genes that could be targets for treatment. Additionally, as cohorts continue to grow, performing analyses in ALS subtypes, such as primary lateral sclerosis (PLS), becomes possible due to an increase in power. These analyses could highlight novel pathways in disease manifestation.MethodsBuilding on our previous discoveries using rare variant association analyses, we conducted rare variant burden testing on a substantially larger multi-ethnic cohort of 6,970 ALS patients, 166 PLS patients, and 22,524 controls. We used intolerant domain percentiles based on sub-region Residual Variation Intolerance Score (subRVIS) that have been described previously in conjunction with gene based collapsing approaches to conduct burden testing to identify genes that associate with ALS and PLS.ResultsA gene based collapsing model showed significant associations with SOD1, TARDBP, and TBK1 (OR = 19.18, p = 3.67 × 10–39; OR = 4.73, p = 2 × 10–10; OR = 2.3, p = 7.49 × 10–9, respectively). These genes have been previously associated with ALS. Additionally, a significant novel control enriched gene, ALKBH3 (p = 4.88 × 10–7), was protective for ALS in this model. An intolerant domain-based collapsing model showed a significant improvement in identifying regions in TARDBP that associated with ALS (OR = 10.08, p = 3.62 × 10–16). Our PLS protein truncating variant collapsing analysis demonstrated significant case enrichment in ANTXR2 (p = 8.38 × 10–6).ConclusionsIn a large multi-ethnic cohort of 6,970 ALS patients, collapsing analyses validated known ALS genes and identified a novel potentially protective gene, ALKBH3. A first-ever analysis in 166 patients with PLS found a candidate association with loss-of-function mutations in ANTXR2