Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response

Dramatic rise of mutators has been found to accompany adaptation of bacteria in response to many kinds of stress. Two views on the evolutionary origin of this phenomenon emerged: the pleiotropic hypothesis positing that it is a byproduct of environmental stress or other specific stress response mechanisms and the second order selection which states that mutators hitchhike to fixation with unrelated beneficial alleles. Conventional population genetics models could not fully resolve this controversy because they are based on certain assumptions about fitness landscape. Here we address this problem using a microscopic multiscale model, which couples physically realistic molecular descriptions of proteins and their interactions with population genetics of carrier organisms without assuming any a priori fitness landscape. We found that both pleiotropy and second order selection play a crucial role at different stages of adaptation: the supply of mutators is provided through destabilization of error correction complexes or fluctuations of production levels of prototypic mismatch repair proteins (pleiotropic effects), while rise and fixation of mutators occur when there is a sufficient supply of beneficial mutations in replication-controlling genes. This general mechanism assures a robust and reliable adaptation of organisms to unforeseen challenges. This study highlights physical principles underlying physical biological mechanisms of stress response and adaptation

Feed-Forward Microprocessing and Splicing Activities at a MicroRNA–Containing Intron

The majority of mammalian microRNA (miRNA) genes reside within introns of protein-encoding and non-coding genes, yet the mechanisms coordinating primary transcript processing into both mature miRNA and spliced mRNA are poorly understood. Analysis of melanoma invasion suppressor miR-211 expressed from intron 6 of melastatin revealed that microprocessing of miR-211 promotes splicing of the exon 6–exon 7 junction of melastatin by a mechanism requiring the RNase III activity of Drosha. Additionally, mutations in the 5′ splice site (5′SS), but not in the 3′SS, branch point, or polypyrimidine tract of intron 6 reduced miR-211 biogenesis and Drosha recruitment to intron 6, indicating that 5′SS recognition by the spliceosome promotes microprocessing of miR-211. Globally, knockdown of U1 splicing factors reduced intronic miRNA expression. Our data demonstrate novel mutually-cooperative microprocessing and splicing activities at an intronic miRNA locus and suggest that the initiation of spliceosome assembly may promote microprocessing of intronic miRNAs

In silico evolution of signaling networks using rule-based models: bistable response dynamics

One of the ultimate goals in biology is to understand the design principles of biological systems. Such principles, if they exist, can help us better understand complex, natural biological systems and guide the engineering of de novo ones. Towards deciphering design principles, in silico evolution of biological systems with proper abstraction is a promising approach. Here, we demonstrate the application of in silico evolution combined with rule-based modelling for exploring design principles of cellular signaling networks. This application is based on a computational platform, called BioJazz, which allows in silico evolution of signaling networks with unbounded complexity. We provide a detailed introduction to BioJazz architecture and implementation and describe how it can be used to evolve and/or design signaling networks with defined dynamics. For the latter, we evolve signaling networks with switch-like response dynamics and demonstrate how BioJazz can result in new biological insights on network structures that can endow bistable response dynamics. This example also demonstrated both the power of BioJazz in evolving and designing signaling networks and its limitations at the current stage of development.Comment: 24 pages, 7 figure

ABI3 ectopic expression reduces in vitro and in vivo cell growth properties while inducing senescence

<p>Abstract</p> <p>Background</p> <p>Mounting evidence has indicated that <it>ABI3 </it>(ABI family member 3) function as a tumor suppressor gene, although the molecular mechanism by which ABI3 acts remains largely unknown.</p> <p>Methods</p> <p>The present study investigated <it>ABI3 </it>expression in a large panel of benign and malignant thyroid tumors and explored a correlation between the expression of ABI3 and its potential partner ABI3-binding protein (ABI3BP). We next explored the biological effects of <it>ABI3 </it>ectopic expression in thyroid and colon carcinoma cell lines, in which its expression was reduced or absent.</p> <p>Results</p> <p>We not only observed that <it>ABI3 </it>expression is reduced or lost in most carcinomas but also that there is a positive correlation between <it>ABI3 </it>and <it>ABI3BP </it>expression. Ectopic expression of <it>ABI3 </it>was sufficient to lead to a lower transforming activity, reduced tumor <it>in vitro </it>growth properties, suppressed <it>in vitro </it>anchorage-independent growth and <it>in vivo </it>tumor formation while, cellular senescence increased. These responses were accompanied by the up-regulation of the cell cycle inhibitor <it>p21 </it>^WAF1and reduced ERK phosphorylation and <it>E2F1 </it>expression.</p> <p>Conclusions</p> <p>Our result links <it>ABI3 </it>to the pathogenesis and progression of some cancers and suggests that ABI3 or its pathway might have interest as therapeutic target. These results also suggest that the pathways through which <it>ABI3 </it>works should be further characterized.</p

Does technology and Innovation Management improve Market Position? Empirical Evidence from Innovating Firms in South Africa

There is a growing recognition of the central role of technology and knowledge management for market success of organizations. Little is empirically know, however, about this relationship. Drawing on the South African Innovation Survey, a unique dataset on innovative behavior of South African firms in manufacturing and services, this paper investigates the question to what extent and in which ways do technology and innovation management activities affect firms’ market position. Findings show that conducting technology strategy activities pays out. Moreover, especially a combination of internal and external technology audits seems to be beneficial for organizational performance

Understanding network concepts in modules

<p>Abstract</p> <p>Background</p> <p>Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.</p> <p>Results</p> <p>Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.</p> <p>Conclusion</p> <p>Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: <url>http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks</url></p

Institutional review board challenges related to community-based participatory research on human exposure to environmental toxins: A case study

<p>Abstract</p> <p>Background</p> <p>We report on the challenges of obtaining Institutional Review Board (IRB) coverage for a community-based participatory research (CBPR) environmental justice project, which involved reporting biomonitoring and household exposure results to participants, and included lay participation in research.</p> <p>Methods</p> <p>We draw on our experiences guiding a multi-partner CBPR project through university and state Institutional Review Board reviews, and other CBPR colleagues' written accounts and conference presentations and discussions. We also interviewed academics involved in CBPR to learn of their challenges with Institutional Review Boards.</p> <p>Results</p> <p>We found that Institutional Review Boards are generally unfamiliar with CBPR, reluctant to oversee community partners, and resistant to ongoing researcher-participant interaction. Institutional Review Boards sometimes unintentionally violate the very principles of beneficence and justice which they are supposed to uphold. For example, some Institutional Review Boards refuse to allow report-back of individual data to participants, which contradicts the CBPR principles that guide a growing number of projects. This causes significant delays and may divert research and dissemination efforts. Our extensive education of our university Institutional Review Board convinced them to provide human subjects protection coverage for two community-based organizations in our partnership.</p> <p>Conclusions</p> <p>IRBs and funders should develop clear, routine review guidelines that respect the unique qualities of CBPR, while researchers and community partners can educate IRB staff and board members about the objectives, ethical frameworks, and research methods of CBPR. These strategies can better protect research participants from the harm of unnecessary delays and exclusion from the research process, while facilitating the ethical communication of study results to participants and communities.</p

Cross-Over between Discrete and Continuous Protein Structure Space: Insights into Automatic Classification and Networks of Protein Structures

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively, average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.ph

Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein’s neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution