Coevolutionary immune system dynamics driving pathogen speciation

We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.Comment: main article: 16 pages, 5 figures; supporting information: 3 page

Network-Based Investigations of Human Functional Brain Dynamics

The human brain is a complex system in which interactions of billions of neurons give rise to behavior. fMRI allows researchers to measure the functional activity of the working brain, allowing both the localization of specific functions within the brain and the investigation of multivariate patterns of functional activation. These patterns have been found to correspond both to short-term brain states such as focused attention or daydreaming, and to characteristics such as age or disease. Functional patterns also show substantial variation across individuals. Understanding the correspondence of distributed functional activity to these various factors is an ongoing research area.Network science is a valuable tool for representing complex brain function, providing a framework for quantifying multivariate activity as a network of interactions. Here, we build upon recent advances in dynamic network science, using time-evolving networks to investigate how the organization of brain dynamics is related to demographics and brain states.We use \textit{hypergraphs} to analyze brain network dynamics during different cognitive tasks and the transitions between them. We identify the presence of \textit{hyperedges}, groups of functional interactions that fluctuate coherently in strength over time both within and across brain states. We develop metrics to quantify the variation of hyperedge structure between tasks and across individuals. We find that the spatial location of hyperedges is relatively consistent across individuals, serving as a signature of a cognitive task, while hyperedge size exhibits variation across individuals but remains consistent between tasks.We also investigate the variation of brain dynamics across the human lifespan, using both hypergraphs and dynamic clusters, or \textit{communities}, of brain regions with similar activity. We find significant relationships between age and dynamic organization: younger subjects tend to have larger hyperedges, as well as less fragmented and more coherent communities, and their brain regions tend to switch between communities less often. Further, the dynamics of different cognitive brain systems respond differently to aging. Finally, we propose and evaluate a method of targeted node removal during the data-driven detection of communities, using synthetic and fMRI-derived networks to show that the method can improve identification of multi-scale community structure, and help to resolve key features of community dynamics

Brain network adaptability across task states.

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters

Parameters used in all simulations unless otherwise noted.

<p>Values are approximated within biologically relevant ranges, based on known immune system characteristics and previous modeling work <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102821#pone.0102821-Stromberg1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102821#pone.0102821-Stromberg2" target="_blank">[14]</a> (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102821#pone.0102821.s001" target="_blank">Appendix S1</a> for details). Although the exact phase diagram boundaries between regions with different infection outcomes may change at different parameter values, the qualitative dynamics within each infection outcome are not especially sensitive to the exact values of these parameters.</p

Examples of three of the four distinct types of infection observed in the deterministic model.

<p>(The case of sterilizing immunity is not shown.) The total population densities of both pathogens and T-cells (top panels), as well as the shape space distribution of the pathogens (bottom panels), are plotted over time for each infection. A. Early clearance after the acute phase of infection (). B. Chronic infection in which mutant pathogen strains avoid clearance by the initial immune response (). The first 50 days, in which this initial avoidance occurs, are shown on the left; on the right (days 50–500), the total pathogen density remains nearly constant while slow antigenic drift and branching occur. C. Early escape (). Some pathogens are cleared by the initial immune response, but the population reaches carrying capacity without being controlled. Parameter values in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102821#pone-0102821-t001" target="_blank">Table 1</a> were used unless otherwise noted.</p