Mapping the strand-specific transcriptome of fission yeast

Pervasive genome-wide transcription is widespread in eukaryotic cells, but key features of the transcriptome have yet to be fully characterized. a new study using antibody-based detection of RNA-DNA duplexes on tiling arrays now reveals a complex, strand-specific transcriptional world in fission yeast

Universality of local weak interactions and its application for interferometric alignment

The modification of the effect of interactions of a particle as a function of its pre- and postselected states is analyzed theoretically and experimentally. The universality property of this modification in the case of local interactions of a spatially pre- and postselected particle has been found. It allowed to define an operational approach for characterization of the presence of a quantum particle in a particular place: the way it modifies the effect of local interactions. The experiment demonstrating this universality property provides an efficient interferometric alignment method, in which the beam on a single detector throughout one phase scan yields all misalignment parameters.Comment: 12 pages, 7 figure

The linker histone H1.0 generates epigenetic and functional intratumor heterogeneity

Tumors comprise functionally diverse subpopulations of cells with distinct proliferative potential. Here, we show that dynamic epigenetic states defined by the linker histone H1.0 determine which cells within a tumor can sustain the long-term cancer growth. Numerous cancer types exhibit high inter- and intratumor heterogeneity of H1.0, with H1.0 levels correlating with tumor differentiation status, patient survival, and, at the single-cell level, cancer stem cell markers. Silencing of H1.0 promotes maintenance of self-renewing cells by inducing derepression of megabase-sized gene domains harboring downstream effectors of oncogenic pathways. Self-renewing epigenetic states are not stable, and reexpression of H1.0 in subsets of tumor cells establishes transcriptional programs that restrict cancer cells’ long-term proliferative potential and drive their differentiation. Our results uncover epigenetic determinants of tumor-maintaining cells

Consistency, comprehensiveness, and compatibility of pathway databases

<p>Abstract</p> <p>Background</p> <p>It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need.</p> <p>Description</p> <p>Our results show a low level of consistency, comprehensiveness and compatibility among three popular pathway databases (KEGG, Ingenuity and Wikipathways). The level of consistency for genes in similar pathways across databases ranges from 0% to 88%. The corresponding level of consistency for interacting genes pairs is 0%-61%. These three original sources can be assumed to be reliable in the sense that the interacting gene pairs reported in them are correct because they are curated. However, the lack of concordance between these databases suggests each source has missed out many genes and interacting gene pairs.</p> <p>Conclusions</p> <p>Researchers will hence find it challenging to obtain consistent pathway information out of these diverse data sources. It is therefore critical to enable them to access these sources via a consistent, comprehensive and unified pathway API. We accumulated sufficient data to create such an aggregated resource with the convenience of an API to access its information. This unified resource can be accessed at <url>http://www.pathwayapi.com</url>.</p

SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data

A Continuum of Cell States Spans Pluripotency and Lineage Commitment in Human Embryonic Stem Cells

Background: Commitment in embryonic stem cells is often depicted as a binary choice between alternate cell states, pluripotency and specification to a particular germ layer or extraembryonic lineage. However, close examination of human ES cell cultures has revealed significant heterogeneity in the stem cell compartment. Methodology/Principal Findings: We isolated subpopulations of embryonic stem cells using surface markers, then examined their expression of pluripotency genes and lineage specific transcription factors at the single cell level, and tested their ability to regenerate colonies of stem cells. Transcript analysis of single embryonic stem cells showed that there is a gradient and a hierarchy of expression of pluripotency genes in the population. Even cells at the top of the hierarchy generally express only a subset of the stem cell genes studied. Many cells co-express pluripotency and lineage specific genes. Cells along the continuum show a progressively decreasing likelihood of self renewal as their expression of stem cell surface markers and pluripotency genes wanes. Most cells that are positive for stem cell surface markers express Oct-4, but only those towards the top of the hierarchy express the nodal receptor TDGF-1 and the growth factor GDF3. Significance: These findings on gene expression in single embryonic stem cells are in concert with recent studies of early mammalian development, which reveal molecular heterogeneity and a stochasticity of gene expression in blastomeres. Our work indicates that only a small fraction of the population resides at the top of the hierarchy, that lineage priming (co-expression of stem cell and lineage specific genes) characterizes pluripotent stem cell populations, and that extrinsic signaling pathways are upstream of transcription factor networks that control pluripotency

Human Cancer Protein-Protein Interaction Network: A Structural Perspective

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates

Inferring Pathway Activity toward Precise Disease Classification

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease