The shoot apical meristem of Sinapis alba L. expands its central symplasmic field during the floral transition

The shoot apical meristem (SAM) is functionally subdivided into zones with distinct tasks. During vegetative growth the peripheral zone of the meristem gives rise to leaf primordia that develop into dorsiventral leaves under the influence of signals from the central zone. During the floral transition the function of the SAM is altered and its peripheral zone starts to form floral structures in a specific pattern. This requires alterations in the signal networks that coordinate the activities of the peripheral and central zone of the SAM. These signal networks are partly housed in the symplasmic space of the SAM. Dye-coupling experiments demonstrate that in the superficial layer of the Sinapis alba meristem this space is radially subdivided. The cells of the central zone are coupled into a symplasmic field, which is shielded from the peripheral zone by the positional closing of plasmodesmata. In the vegetative meristems, most of these central symplasmic fields have a triangular geometry and are relatively small in size. Plants that are induced to flower by exposure to a single long day alter the geometry as well as the size of their central symplasmic field. After two subsequent days under short photoperiod the central symplasmic fields exhibit a circular form. Simultaneously, their size strongly increases both in an absolute sense and relative to the enlarging meristem. The geometric change in the fields is hypothesized to be due to recruitment of extra initial cells, required to support the increase in phyllotactic complexity. The proportional increase in field size is interpreted as an adjustment in the balance between the central and peripheral zone of the SAM, accompanying the shift from leaf production to flower formation.     

Adipose tissue-organotypic culture system as a promising model for studying adipose tissue biology and regeneration

Adipose tissue consists of mature adipocytes, preadipocytes and mesenchymal stem cells (MSCs), but a culture system for analyzing their cell types within the tissue has not been established. We have recently developed “adipose tissue-organotypic culture system” that maintains unilocular structure, proliferative ability and functions of mature adipocytes for a long term, using three-dimensional collagen gel culture of the tissue fragments. In this system, both preadipocytes and MSCs regenerate actively at the peripheral zone of the fragments. Our method will open up a new way for studying both multiple cell types within adipose tissue and the cell-based mechanisms of obesity and metabolic syndrome. Thus, it seems to be a promising model for investigating adipose tissue biology and regeneration. In this article, we introduce adipose tissue-organotypic culture, and propose two theories regarding the mechanism of tissue regeneration that occurs specifically at peripheral zone of tissue fragments in vitro.Key words: adipose tissue-organotypic culture, three-dimensional, tissue fragments, peripheral zone, central zone, mature adipocytes, preadipocytes (immature adipocytes), mesenchymal stem cells, adipokines, tissue regeneration     

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

ABSTRACT: A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. RESULTS: We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. CONCLUSIONS: Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

Native and invasive hosts play different roles in host–parasite networks

Parasites are often key players in biological invasions since they can mediate the impact of host invasions or can themselves become invasive species. However, the nature and extent of parasite-mediated invasions are often difficult to delineate. Here, we used individual-based, weighted bipartite networks to study the roles (degrees of interactions of individuals in a modular network according to their within- and among-module connections) played by native and invasive host individuals to their parasite communities. We studied two phylogenetically and ecologically close fish species, Mugil cephalus s.l. and Planiliza haematocheilus (Teleostei: Mugilidae). Planiliza haematocheilus is native to the Sea of Japan and invasive in the Sea of Azov whereas, M. cephalus s.l. is native to both seas. Based on the common evolutionary history that drives native host–parasite networks, we hypothesised that 1) native networks have higher modularity than invaded ones; and 2) invasive hosts in the invaded area play a peripheral role to structure parasite communities. We analysed the whole parasite community and subsets based on transmission strategy and host specificity of the parasite species to establish whether modularity and host roles are related to these features in the native and invaded areas. All networks were found to be modular. However, modularity tended to be higher in networks of the native area rather than those of the invaded area. Host individuals of both fish species played similar roles in the native area, whereas invasive hosts played a peripheral role in the networks of the invaded area. We propose that long-term monitoring of the roles of invasive hosts in parasite communities can be a useful proxy for estimating the maturity of the establishment of the invasive hosts in an ecosystem.     

An evolving model of undirected networks based on microscopic biological interaction systems

With protein or gene interaction systems as the background, this paper proposes an evolving model of biological undirected networks, which are consistent with some plausible mechanisms in biology. Through introducing a rule of preferential duplication of a node inversely proportional to the degree of existing nodes and an attribute of the age of the node (the older, the more influence), by which the probability of a node receiving re-wiring links is chosen, the model networks generated in certain parameter conditions could reproduce series of statistic topological characteristics of real biological graphs, including the scale-free feature, small world effect, hierarchical modularity, limited structural robustness, and disassortativity of degree–degree correlation.     

Effects of Emotional Context on Memory for Details: The Role of Attention

It was repeatedly demonstrated that a negative emotional context enhances memory for central details while impairing memory for peripheral information. This trade-off effect is assumed to result from attentional processes: a negative context seems to narrow attention to central information at the expense of more peripheral details, thus causing the differential effects in memory. However, this explanation has rarely been tested and previous findings were partly inconclusive. For the present experiment 13 negative and 13 neutral naturalistic, thematically driven picture stories were constructed to test the trade-off effect in an ecologically more valid setting as compared to previous studies. During an incidental encoding phase, eye movements were recorded as an index of overt attention. In a subsequent recognition phase, memory for central and peripheral details occurring in the picture stories was tested. Explicit affective ratings and autonomic responses validated the induction of emotion during encoding. Consistent with the emotional trade-off effect on memory, encoding context differentially affected recognition of central and peripheral details. However, contrary to the common assumption, the emotional trade-off effect on memory was not mediated by attentional processes. By contrast, results suggest that the relevance of attentional processing for later recognition memory depends on the centrality of information and the emotional context but not their interaction. Thus, central information was remembered well even when fixated very briefly whereas memory for peripheral information depended more on overt attention at encoding. Moreover, the influence of overt attention on memory for central and peripheral details seems to be much lower for an arousing as compared to a neutral context.     

Evolution of complex modular biological networks

Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein–protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks.     

Why modules matter

The serendipitous discovery of the SH2 domain unleashed a sea-change in our conceptual molecular understanding of protein function. The reductionist approaches that followed from the recognition of modular protein interaction domains transformed our understanding of cellular signal transduction systems, how they evolve and how they may be manipulated. We now recognize thousands of conserved protein modules - many of which have been described in structure and function, implicated in disease, or underlie targeted therapeutics. The reductionist study of isolated protein modules has enabled the reconstruction of the protein interaction networks that underlie cellular signalling. Protein modules themselves are becoming tools to probe cellular activation states and identify key interactions hubs in both normal and diseased cells and the concept of protein modularity is central to the field of synthetic biology. This brief word of introduction serves to highlight the historical impact of the very powerful idea of protein modules and sets the stage for the exciting on-going discoveries discussed in this issue.     

Microsurgical and laser ablation analysis of interactions between the zones and layers of the tomato shoot apical meristem

Plants exhibit life-long organogenic and histogenic activity in a specialised organ, the shoot apical meristem. Leaves and flowers are formed within the ring-shaped peripheral zone, which surrounds the central zone, the site of the stem cells. We have undertaken a series of high-precision laser ablation and microsurgical tissue removal experiments to test the functions of different parts of the tomato meristem, and to reveal their interactions. Ablation of the central zone led to ectopic expression of the WUSCHEL gene at the periphery, followed by the establishment of a new meristem centre. After the ablation of the central zone, organ formation continued without a lag. Thus, the central zone does not participate in organogenesis, except as the ultimate source of founder cells. Microsurgical removal of the external L(1) layer induced periclinal cell divisions and terminal differentiation in the subtending layers. In addition, no organs were initiated in areas devoid of L(1), demonstrating an important role of the L(1) in organogenesis. L(1) ablation had only local effects, an observation that is difficult to reconcile with phyllotaxis theories that invoke physical tension operating within the meristem as a whole. Finally, regeneration of L(1) cells was never observed after ablation. This shows that while the zones of the meristem show a remarkable capacity to regenerate after interference, elimination of the L(1) layer is irreparable and causes terminal differentiation.     

Structural similarity of genetically interacting proteins

Background

The functions of a eukaryotic cell are largely performed by multi-subunit protein complexes that act as molecular machines or information processing modules in cellular networks. An important problem in systems biology is to understand how, in general, these molecular machines respond to perturbations.

Results

In yeast, genes that inhibit growth when their expression is reduced are strongly enriched amongst the subunits of multi-subunit protein complexes. This applies to both the core and peripheral subunits of protein complexes, and the subunits of each complex normally have the same loss-of-function phenotypes. In contrast, genes that inhibit growth when their expression is increased are not enriched amongst the core or peripheral subunits of protein complexes, and the behaviour of one subunit of a complex is not predictive for the other subunits with respect to over-expression phenotypes.

Conclusion

We propose the principle that the overall activity of a protein complex is in general robust to an increase, but not to a decrease in the expression of its subunits. This means that whereas phenotypes resulting from a decrease in gene expression can be predicted because they cluster on networks of protein complexes, over-expression phenotypes cannot be predicted in this way. We discuss the implications of these findings for understanding how cells are regulated, how they evolve, and how genetic perturbations connect to disease in humans.     

Evolution of networks for body plan patterning; interplay of modularity, robustness and evolvability

A major goal of evolutionary developmental biology (evo-devo) is to understand how multicellular body plans of increasing complexity have evolved, and how the corresponding developmental programs are genetically encoded. It has been repeatedly argued that key to the evolution of increased body plan complexity is the modularity of the underlying developmental gene regulatory networks (GRNs). This modularity is considered essential for network robustness and evolvability. In our opinion, these ideas, appealing as they may sound, have not been sufficiently tested. Here we use computer simulations to study the evolution of GRNs' underlying body plan patterning. We select for body plan segmentation and differentiation, as these are considered to be major innovations in metazoan evolution. To allow modular networks to evolve, we independently select for segmentation and differentiation. We study both the occurrence and relation of robustness, evolvability and modularity of evolved networks. Interestingly, we observed two distinct evolutionary strategies to evolve a segmented, differentiated body plan. In the first strategy, first segments and then differentiation domains evolve (SF strategy). In the second scenario segments and domains evolve simultaneously (SS strategy). We demonstrate that under indirect selection for robustness the SF strategy becomes dominant. In addition, as a byproduct of this larger robustness, the SF strategy is also more evolvable. Finally, using a combined functional and architectural approach, we determine network modularity. We find that while SS networks generate segments and domains in an integrated manner, SF networks use largely independent modules to produce segments and domains. Surprisingly, we find that widely used, purely architectural methods for determining network modularity completely fail to establish this higher modularity of SF networks. Finally, we observe that, as a free side effect of evolving segmentation and differentiation in combination, we obtained in-silico developmental mechanisms resembling mechanisms used in vertebrate development.     

The capacity of central and peripheral catecholaminergic neurons to innervate the pineal organ and cerebral cortex of the rat: in vitro immunohistochemical observations

The locus coeruleus (LC) or superior cervical ganglion (SCG) of neonatal rats were co-cultured either with the pineal organ or cerebral cortex (CX) to investigate the innervating capacity of central and peripheral catacholamine neurons under these experimental conditions. After 2 weeks of co-culturing, cultures were fixed for tyrosine hydroxylase (TH) immunohistochemistry to examine the distribution of catecholamine neurons and their fibers. Glial fibrillary acidic protein and fibronectin immunohistochemistry was performed to determine the cell types proliferating around the explants. In LC/CX co-cultures, numerous astrocytes spread between the two explants, and TH-immunoreactive neurites were generally seen to invade CX explants. In contrast, neurite extension from LC to pineal explants occurred only when a glial cell sheet grew between the two explants, and when the pineal explants were not surrounded by a tight fibronectin-positive cell layer. Neurites of the SCG usually invaded both CX and pineal explants, regardless of the existence of glial or non-glial cell layer. These results indicate that central and peripheral catecholamine neurites have the potential of invading both the cortex and pineal, although they are distributed only in particular regions of the intact brain. The distribution of LC neurites, however, seems to be profoundly affected by the cell types spreading around the explants; glial cells appear to support LC neurite extension, whereas non-glial cells appear to inhibit it.     

Emergence of complex social networks from spatial structure and rules of thumb: a modelling approach

Individual-based computer models show that simple heuristic governing individuals’ behavior may suffice to generate complex patterns of social behavior at the group level such as those observed in animal societies. ‘GrooFiWorld’ is an example of such kind of computer models. In this model, self-organization and simple behavioral rules generate complex patterns of social behavior like those described in tolerant and intolerant societies of macaques. Social complexity results from the socio-spatial structure of the group, the nature of which is, in turn, a side-effect of intensity of aggression. The model suggests that a similar mechanism may give rise to complex social structures in macaques. It is, however, unknown if the spatial structure of the model and that of macaques are indeed similar. Here we used social networks analysis as a proxy for spatial structure of the group. Our findings show that the social networks of the model share similar qualitative features with those of macaques. As group size increases, the density and the average individual eigenvector centrality decrease and the modularity and centralization of the network increase. In social networks emerging from simulations resembling intolerant societies the density is lower, the modularity and centralization are higher, and the individuals ranking higher in the dominance hierarchy are more central than in the social networks emerging from simulations resembling egalitarian societies. Given the qualitative similarity between the social networks of the model and that of empirical data, our results suggest that the spatial structure of macaques is similar to that of the model. It seems thus plausible that, as in the model, the spatial structure combined with simple behavioral rules plays a role in the emergence of complex social networks and complex social behavior in macaques.     

Using chemical reaction network theory to discard a kinetic mechanism hypothesis

Feinberg's chemical reaction network theory (CRNT) connects the structure of a biochemical reaction network to qualitative properties of the corresponding system of ordinary differential equations. No information about parameter values is needed. As such, it seems to be well suited for application in systems biology, where parameter uncertainty is predominant. However, its application in this area is rare. To demonstrate the potential benefits from its application, different reaction networks representing a single layer of the well-studied mitogen-activated protein kinase (MAPK) cascade are analysed. Recent results from Markevich et al. (2004) show that, unexpectedly, multilayered protein kinase cascades can exhibit multistationarity, even on a single cascade level. Using CRNT, we show that their assumption of a distributive mechanism for double phosphorylation and dephosphorylation is crucial for multistationarity on the single cascade level.     

Modularity in vertebrate brain development and evolution.

Embryonic modularity and functional modularity are two principles of brain organization. Embryonic modules are histogenetic fields that are specified by position-dependent expression of patterning genes. Within each embryonic module, secondary and higher-level pattern formation takes places during development, finally giving rise to brain nuclei and cortical layers. Defined subsets of these structures become connected by fiber tracts to form the information-processing neural circuits, which represent the functional modules of the brain. We review evidence that a group of cell adhesion molecules, the cadherins, provides an adhesive code for both types of modularity, based on a preferentially homotypic binding mechanism. Embryonic modularity is transformed into functional modularity, in part by translating early-generated positional information into an array of adhesive cues, which regulate the binding of functional neural structures distributed across the embryonic modules. Brain modularity may provide a basis for adaptability in evolution.     

Towards zoomable multidimensional maps of the cell

The detailed structure of molecular networks, including their dependence on conditions and time, are now routinely assayed by various experimental techniques. Visualization is a vital aid in integrating and interpreting such data. We describe emerging approaches for representing and visualizing systems data and for achieving semantic zooming, or changes in information density concordant with scale. A central challenge is to move beyond the display of a static network to visualizations of networks as a function of time, space and cell state, which capture the adaptability of the cell. We consider approaches for representing the role of protein complexes in the cell cycle, displaying modules of metabolism in a hierarchical format, integrating experimental interaction data with structured vocabularies such as Gene Ontology categories and representing conserved interactions among orthologous groups of genes.