A new perspective on developmental plasticity and the principles of adaptive morph determination

Organisms can have divergent paths of development leading to alternative phenotypes, or morphs. The choice of developmental path may be set by environmental cues, the individual's genotype, or a combination of the two. Using individual-based simulation and analytical investigation, we explore the idea that from the viewpoint of a developmental switch, genetic morph determination can sometimes be regarded as adaptive developmental plasticity. We compare the possibilities for the evolution of environmental and genetic morph determination and combinations of the two in situations with spatial variation in conditions. We find that the accuracy of environmental cues in predicting coming selective conditions is important for environmental morph determination, in accordance with previous results, and that genetic morph determination is favored in a similar way by the accuracy of genetic cues, in the form of selectively maintained gene frequency differences between local populations. Restricted gene flow and strong selection acting on the phenotypic alternatives produce clearer gene frequency differences and lead to greater accuracy of genetic cues. For combined environmental and genetic morph determination, we show that the developmental machinery can evolve toward efficiently combining information in environmental and genetic cues for the purpose of predicting coming selective conditions.     

Challenges in implementing adaptive designs: comments on the viewpoints expressed by regulatory statisticians

This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill; 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.     

A population dynamics analysis of the interaction between adaptive regulatory T cells and antigen presenting cells

Background

Regulatory T cells are central actors in the maintenance of tolerance of self-antigens or allergens and in the regulation of the intensity of the immune response during infections by pathogens. An understanding of the network of the interaction between regulatory T cells, antigen presenting cells and effector T cells is starting to emerge. Dynamical systems analysis can help to understand the dynamical properties of an interaction network and can shed light on the different tasks that can be accomplished by a network.

Methodology and Principal Findings

We used a mathematical model to describe a interaction network of adaptive regulatory T cells, in which mature precursor T cells may differentiate into either adaptive regulatory T cells or effector T cells, depending on the activation state of the cell by which the antigen was presented. Using an equilibrium analysis of the mathematical model we show that, for some parameters, the network has two stable equilibrium states: one in which effector T cells are strongly regulated by regulatory T cells and another in which effector T cells are not regulated because the regulatory T cell population is vanishingly small. We then simulate different types of perturbations, such as the introduction of an antigen into a virgin system, and look at the state into which the system falls. We find that whether or not the interaction network switches from the regulated (tolerant) state to the unregulated state depends on the strength of the antigenic stimulus and the state from which the network has been perturbed.

Conclusion/Significance

Our findings suggest that the interaction network studied in this paper plays an essential part in generating and maintaining tolerance against allergens and self-antigens.     

A craniometric perspective on the transition to agriculture in Europe

Debates surrounding the nature of the Neolithic demographic transition in Europe have historically centered on two opposing models: a "demic" diffusion model whereby incoming farmers from the Near East and Anatolia effectively replaced or completely assimilated indigenous Mesolithic foraging communities, and an "indigenist" model resting on the assumption that ideas relating to agriculture and animal domestication diffused from the Near East but with little or no gene flow. The extreme versions of these dichotomous models were heavily contested primarily on the basis of archeological and modern genetic data. However, in recent years a growing acceptance has arisen of the likelihood that both processes were ongoing throughout the Neolithic transition and that a more complex, regional approach is required to fully understand the change from a foraging to a primarily agricultural mode of subsistence in Europe. Craniometric data were particularly useful for testing these more complex scenarios, as they can reliably be employed as a proxy for the genetic relationships among Mesolithic and Neolithic populations. In contrast, modern genetic data assume that modern European populations accurately reflect the genetic structure of Europe at the time of the Neolithic transition, while ancient DNA data are still not geographically or temporally detailed enough to test continent-wide processes. Here, with particular emphasis on the role of craniometric analyses, we review the current state of knowledge regarding the cultural and biological nature of the Neolithic transition in Europe.     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

An adaptive weighted log-rank test with application to cancer prevention and screening trials.

A class of adaptive weighted log-rank statistics is described where the vector of weights is chosen in a data-dependent way from a family of "smooth" weight vectors. A parametric family of weight vectors is identified which includes most shapes of weighting vectors that will be near optimal in many cancer prevention and screening trials. This family of weight vectors is used in an application of the proposed method to data from a breast cancer screening trial. Results from a small simulation study comparing the power of the adaptive statistic to that of the unweighted log-rank statistic are presented.     

Immunological hurdles in the path to gene therapy for Duchenne muscular dystrophy

Patients with Duchenne muscular dystrophy (DMD), an X-linked lethal muscle-wasting disease, have abnormal expression of the protein dystrophin within their muscle fibres. In the mdx mouse model of this condition, both germline and neonatal somatic gene transfers of dystrophin cDNAs have demonstrated the potential of gene therapy in treating DMD. However, in many DMD patients, there appears to be no dystrophin expression when muscle biopsies are immunostained or western blots are performed. This raises the possibility that the expression of dystrophin following gene transfer might trigger a destructive immune response against this 'neoantigen'. Immune responses can also be generated against the gene transfer vector used to transfect the dystrophic muscle, and the combined immune response could further damage the already inflamed muscle. These problems are now beginning to be investigated in immunocompetent mdx mice. Although much work remains to be done, there are promising indications that these immune responses might not prove as much of a concern as originally envisaged.     

11 Findings and hurdles on the path leading from formamide to the spontaneous generation of RNA

Our laboratories analyze the synthetic reactions leading from formamide, NH₂COH to prebiotically relevant compounds in the presence of catalysts. We have described the formation of all the biological nucleic bases of carboxylic acids of two aminoacids, and of condensing agents in the presence of catalysts of terrestrial origin (Saladino et al., 2012) and of one meteorite. Heat-dependent synthetic reactions from NH₂COH lead to the synthesis of acyclonucleosides, not (yet?) to that of nucleosides [hurdle # 1]. Nucleosides are phosphorylated in the presence of NH₂COH and a phosphate source yielding cyclic nucleotides as well. (Costanzo et al., 2007). 3′,5′-cyclic GMP nonenzymatically polymerizes up to at least 25mers, as shown by PAGE, MALDI ToF, ³¹P-NMR, specific RNAse and inhibitors analyses (Costanzo et al., 2012).The reaction is stimulated by 1,8-diazabicycloundec-7-ene and dimethylformamide. 3′,5′-cUMP does not polymerize spontaneously [hurdle # 2], 3′,5′-cAMP polymerizes very poorly [hurdle # 3]. We will discuss data on the polymerization of 3′,5′-cCMP and on a ribozyme activity exerted by oligomers neosynthesized from cyclic nucleotides. This approach finds its larger perspective in the evolutionary scenario depicted by Trifonov (2009).     

Genome sequence comparisons: hurdles in the fast lane to functional genomics

An important computational technique for extracting the wealth of information hidden in human genomic sequence data is to compare the sequence with that from the corresponding region of the mouse genome, looking for segments that are conserved over evolutionary time. Moreover, the approach generalises to comparison of sequences from any two related species. The underlying rationale (which is abundantly confirmed by observation) is that a random mutation in a functional region is usually deleterious to the organism, and hence unlikely to become fixed in the population, whereas mutations in a non-functional region are free to accumulate over time.The potential value of this approach is so attractive that the public and private projects to sequence the human genome are now turning to sequencing the mouse, and you will soon be able to compare the human and mouse sequences of your favourite genomic region.We are currently witnessing an explosion of computer tools for comparative analysis of two genomic sequences. Here the capabilities of two new network servers for comparing genomic sequences from any pair of closely related species are sketched.The Syntenic Gene Prediction Program SGP-I utilises sequence comparisons to enhance the ability to locate protein coding segments in genomic data. PipMaker attempts to determine all conserved genomic regions, regardless of their function.