Sister chromatid cohesion: the beginning of a long and beautiful relationship

Our understanding of the mechanism of sister chromatid cohesion has advanced significantly with the recent identification and characterization of important regulatory factors, structural factors and chromosomal cohesion sites. These analyses reveal a surprisingly complex mechanism of cohesion that is just beginning to be elucidated and exciting connections between cohesion, cell-cycle regulation and other forms of DNA metabolism.     

Diversity and linkage disequilibrium analysis within a selected set of cultivated tomatoes

Within the Dutch genomics initiative the "Centre for Biosystems Genomics" (CBSG) a major research effort is directed at the identification and unraveling of processes and mechanisms affecting fruit quality in tomato. The basis of this fruit quality program was a diverse set of 94 cultivated tomato cultivars, representing a wide spectrum of phenotypes for quality related traits. This paper describes a diversity study performed on these cultivars, using information of 882 AFLP markers, of which 304 markers had a known map position. The AFLP markers were scored as much as possible in a co-dominant fashion. We investigated genome distribution and coverage for the mapped markers and conclude that it proved difficult to arrive at a dense and uniformly distributed coverage of the genome with markers. Mapped markers and unmapped markers were used to investigate population structure. A clear substructure was observed which seemed to coincide with a grouping based on fruit size. Finally, we studied amount and decay of linkage disequilibrium (LD) along the chromosomes. LD was observed over considerable (genetic) distances. We discuss the feasibility of marker-trait association studies and conclude that the amount of genetic variation in our set of cultivars is limited, but that there exists scope for association studies.     

The Autoimmune Disease Database: a dynamically compiled literature-derived database

Background  

Autoimmune diseases are disorders caused by an immune response directed against the body's own organs, tissues and cells. In practice more than 80 clinically distinct diseases, among them systemic lupus erythematosus and rheumatoid arthritis, are classified as autoimmune diseases. Although their etiology is unclear these diseases share certain similarities at the molecular level i.e. susceptibility regions on the chromosomes or the involvement of common genes. To gain an overview of these related diseases it is not feasible to do a literary review but it requires methods of automated analyses of the more than 500,000 Medline documents related to autoimmune disorders.     

AGRA: analysis of gene ranking algorithms

Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. AVAILABILITY AND IMPLEMENTATION: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. CONTACT: simon.kocbek@uni-mb.si.     

Grooming network cohesion and the role of individuals in a captive chimpanzee group

Social network analysis offers new tools to study the social structure of primate groups. We used social network analysis to investigate the cohesiveness of a grooming network in a captive chimpanzee group (N = 17) and the role that individuals may play in it. Using data from a year-long observation, we constructed an unweighted social network of preferred grooming interactions by retaining only those dyads that groomed above the group mean. This choice of criterion was validated by the finding that the properties of the unweighted network correlated with the properties of a weighted network (i.e. a network representing the frequency of grooming interactions) constructed from the same data. To investigate group cohesion, we tested the resilience of the unweighted grooming network to the removal of central individuals (i.e. individuals with high betweenness centrality). The network fragmented more after the removal of individuals with high betweenness centrality than after the removal of random individuals. Central individuals played a pivotal role in maintaining the network's cohesiveness, and we suggest that this may be a typical property of affiliative networks like grooming networks. We found that the grooming network correlated with kinship and age, and that individuals with higher social status occupied more central positions in the network. Overall, the grooming network showed a heterogeneous structure, yet did not exhibit scale-free properties similar to many other primate networks. We discuss our results in light of recent findings on animal social networks and chimpanzee grooming.     

goSTAG: gene ontology subtrees to tag and annotate genes within a set

Background

Over-representation analysis (ORA) detects enrichment of genes within biological categories. Gene Ontology (GO) domains are commonly used for gene/gene-product annotation. When ORA is employed, often times there are hundreds of statistically significant GO terms per gene set. Comparing enriched categories between a large number of analyses and identifying the term within the GO hierarchy with the most connections is challenging. Furthermore, ascertaining biological themes representative of the samples can be highly subjective from the interpretation of the enriched categories.

Results

We developed goSTAG for utilizing GO Subtrees to Tag and Annotate Genes that are part of a set. Given gene lists from microarray, RNA sequencing (RNA-Seq) or other genomic high-throughput technologies, goSTAG performs GO enrichment analysis and clusters the GO terms based on the p-values from the significance tests. GO subtrees are constructed for each cluster, and the term that has the most paths to the root within the subtree is used to tag and annotate the cluster as the biological theme. We tested goSTAG on a microarray gene expression data set of samples acquired from the bone marrow of rats exposed to cancer therapeutic drugs to determine whether the combination or the order of administration influenced bone marrow toxicity at the level of gene expression. Several clusters were labeled with GO biological processes (BPs) from the subtrees that are indicative of some of the prominent pathways modulated in bone marrow from animals treated with an oxaliplatin/topotecan combination. In particular, negative regulation of MAP kinase activity was the biological theme exclusively in the cluster associated with enrichment at 6 h after treatment with oxaliplatin followed by control. However, nucleoside triphosphate catabolic process was the GO BP labeled exclusively at 6 h after treatment with topotecan followed by control.

Conclusions

goSTAG converts gene lists from genomic analyses into biological themes by enriching biological categories and constructing GO subtrees from over-represented terms in the clusters. The terms with the most paths to the root in the subtree are used to represent the biological themes. goSTAG is developed in R as a Bioconductor package and is available at https://bioconductor.org/packages/goSTAG

     

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

ABSTRACT: BACKGROUND: Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state of the art pathway-based sets. RESULTS: The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. This semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. CONCLUSIONS: The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods.     

Inferring network interactions within a cell

The continuing growth in high-throughput data acquisition has led to a proliferation of network models to represent and analyse biological systems. These networks involve distinct interaction types detected by a combination of methods, ranging from directly observed physical interactions based in biochemistry to interactions inferred from phenotype measurements, genomic expression and comparative genomics. The discovery of interactions increasingly requires a blend of experimental and computational methods. Considering yeast as a model system, recent analytical methods are reviewed here and specific aims are proposed to improve network interaction inference and facilitate predictive biological modelling.     

Constructing a minimum phylogenetic network from a dense triplet set

For a given set L of species and a set T of triplets on L, we seek to construct a phylogenetic network which is consistent with T i.e. which represents all triplets of T. The level of a network is defined as the maximum number of hybrid vertices in its biconnected components. When T is dense, there exist polynomial time algorithms to construct level-0,1 and 2 networks (Aho et al., 1981; Jansson, Nguyen and Sung, 2006; Jansson and Sung, 2006; Iersel et al., 2009). For higher levels, partial answers were obtained in the paper by Iersel and Kelk (2008), with a polynomial time algorithm for simple networks. In this paper, we detail the first complete answer for the general case, solving a problem proposed in Jansson and Sung (2006) and Iersel et al. (2009). For any k fixed, it is possible to construct a level-k network having the minimum number of hybrid vertices and consistent with T, if there is any, in time O(T(k+1)n([4k/3]+1)).     

Cytoskeleton dynamics: fluctuations within the network

Out-of-equilibrium systems, such as the dynamics of a living cytoskeleton (CSK), are inherently noisy with fluctuations arising from the stochastic nature of the underlying biochemical and molecular events. Recently, such fluctuations within the cell were characterized by observing spontaneous nano-scale motions of an RGD-coated microbead bound to the cell surface [Bursac et al., Nat. Mater. 4 (2005) 557-561]. While these reported anomalous bead motions represent a molecular level reorganization (remodeling) of microstructures in contact with the bead, a precise nature of these cytoskeletal constituents and forces that drive their remodeling dynamics are largely unclear. Here, we focused upon spontaneous motions of an RGD-coated bead and, in particular, assessed to what extent these motions are attributable to (i) bulk cell movement (cell crawling), (ii) dynamics of focal adhesions, (iii) dynamics of lipid membrane, and/or (iv) dynamics of the underlying actin CSK driven by myosin motors.     

Stomatal development in Arabidopsis and grasses: differences and commonalities

Stomata, found on the epidermis of all terrestrial plants, consist of two specialized cells called guard cells, which surround a tiny pore. Major advances have been made in our understanding of the genetic control of stomatal development in Arabidopsis and grasses. In Arabidopsis, three basic-helix-loop-helix (bHLH) genes control the successive steps that lead to stomatal formation. SPEECHLESS (SPCH) drives the cell division that initiates the stomatal cell lineage, MUTE induces the formation of the immediate stomatal precursor cell, and FAMA causes the stomatal precursor cell to divide into the two guard cells. Recent results demonstrate that these genes share functions with their grass homologs, and that MUTE is expressed later in development than its grass counterparts. Other differences in stomatal development between these two plant groups are exemplified by the PANGLOSS1 (PAN1) gene of maize. PAN1, which encodes a leucine-rich repeat receptor-like kinase with an inactive kinase domain, promotes polarization of the subsidiary mother cell and orients its cell division plane. Because such events do not exist in Arabidopsis, it is likely that the PAN1-like genes of Arabidopsis and PAN1 are paralogs. Together, these results indicate that distinctions in the regulation of gene expression and protein function are both responsible for the divergence of stomatal development between Arabidopsis and grasses.     

Expression of a cascading genetic network within liposomes

Liposomes have long been used as possible compartments for artificial cells, and it has been shown that liposomes can sustain various types of biochemical reactions. To elevate the degree of molecular complexity of the system in liposomes, we have constructed a two-stage genetic network encapsulated in liposomes. This two-stage genetic network was constructed with the plasmid pTH, in which the protein product of the first stage (T7 RNA polymerase) is required to drive the protein synthesis of the second stage (GFP). We show that the two-stage genetic network constructed in a cell-free expression system is functional within liposomes.     

Connectivity within and among a network of temperate marine reserves

Networks of marine reserves are increasingly being promoted as a means of conserving marine biodiversity. One consideration in designing systems of marine reserves is the maintenance of connectivity to ensure the long-term persistence and resilience of populations. Knowledge of connectivity, however, is frequently lacking during marine reserve design and establishment. We characterise patterns of genetic connectivity of 3 key species of habitat-forming macroalgae across an established network of temperate marine reserves on the east coast of Australia and the implications for adaptive management and marine reserve design. Connectivity varied greatly among species. Connectivity was high for the subtidal macroalgae Ecklonia radiata and Phyllospora comosa and neither species showed any clear patterns of genetic structuring with geographic distance within or among marine parks. In contrast, connectivity was low for the intertidal, Hormosira banksii, and there was a strong pattern of isolation by distance. Coastal topography and latitude influenced small scale patterns of genetic structure. These results suggest that some species are well served by the current system of marine reserves in place along this temperate coast but it may be warranted to revisit protection of intertidal habitats to ensure the long-term persistence of important habitat-forming macroalgae. Adaptively managing marine reserve design to maintain connectivity may ensure the long-term persistence and resilience of marine habitats and the biodiversity they support.