The immune-body cytokine network defines a social architecture of cell interactions

BACKGROUND: Three networks of intercellular communication can be associated with cytokine secretion; one limited to cells of the immune system (immune cells), one limited to parenchymal cells of organs and tissues (body cells), and one involving interactions between immune and body cells (immune-body interface). These cytokine connections determine the inflammatory response to injury and subsequent healing as well as the biologic consequences of the adaptive immune response to antigens. We informatically probed the cytokine database to uncover the underlying network architecture of the three networks. RESULTS: We now report that the three cytokine networks are among the densest of complex networks yet studied, and each features a characteristic profile of specific three-cell motifs. Some legitimate cytokine connections are shunned (anti-motifs). Certain immune cells can be paired by their input-output positions in a cytokine architecture tree of five tiers: macrophages (MΦ) and B cells (BC) comprise the first tier; the second tier is formed by T helper 1 (Th1) and T helper 2 (Th2) cells; the third tier includes dendritic cells (DC), mast cells (MAST), Natural Killer T cells (NK-T) and others; the fourth tier is formed by neutrophils (NEUT) and Natural Killer cells (NK); and the Cytotoxic T cell (CTL) stand alone as a fifth tier. The three-cell cytokine motif architecture of immune system cells places the immune system in a super-family that includes social networks and the World Wide Web. Body cells are less clearly stratified, although cells involved in wound healing and angiogenesis are most highly interconnected with immune cells. CONCLUSION: Cytokine network architecture creates an innate cell-communication platform that organizes the biologic outcome of antigen recognition and inflammation. Informatics sheds new light on immune-body systems organization. REVIEWERS: This article was reviewed by Neil Greenspan, Matthias von Herrath and Anne Cooke

FitEM2EM—Tools for Low Resolution Study of Macromolecular Assembly and Dynamics

Studies of the structure and dynamics of macromolecular assemblies often involve comparison of low resolution models obtained using different techniques such as electron microscopy or atomic force microscopy. We present new computational tools for comparing (matching) and docking of low resolution structures, based on shape complementarity. The matched or docked objects are represented by three dimensional grids where the value of each grid point depends on its position with regard to the interior, surface or exterior of the object. The grids are correlated using fast Fourier transformations producing either matches of related objects or docking models depending on the details of the grid representations. The procedures incorporate thickening and smoothing of the surfaces of the objects which effectively compensates for differences in the resolution of the matched/docked objects, circumventing the need for resolution modification. The presented matching tool FitEM2EMin successfully fitted electron microscopy structures obtained at different resolutions, different conformers of the same structure and partial structures, ranking correct matches at the top in every case. The differences between the grid representations of the matched objects can be used to study conformation differences or to characterize the size and shape of substructures. The presented low-to-low docking tool FitEM2EMout ranked the expected models at the top

Matching related EM maps.

a<p>The first structure in each pair is the stationary object A and the second structure is the moving object B.</p>b<p>Values are given for matching with the lowest density cutoff for object A and highest cutoff for object B (see text).</p

Benchmark of EM structures used in the optimization of <i>w</i>, the parameter that determines the width and shape of the surface layer of object A.

a<p>The resolution quoted here is the value in the EMDB; average resolution is given for the pairs of related structures.</p>b<p>R1 is the virtual atoms radius calculated with the central values of the <i>w</i> ranges in column 5.</p>c<p>R2 is the virtual atoms radius calculated with <i>w</i> values derived from the linear dependency of <i>w</i> on the resolution (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003594#pone-0003594-g001" target="_blank">Figure 1</a>).</p

Examples of low-to-low resolution matching and docking.

<p>The top ranking matches are shown, obtained in <i>Fit</i>EM2EMin scans that employ <i>w</i> values calculated from the dependency graph in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003594#pone-0003594-g001" target="_blank">Figure 1</a>. EM envelopes are shown in yellow. The DifferenceGrid portions where object A protrudes out of object B are colored as object A, and vise versa for object B. The individual images were prepared with the software package Amira and are not to scale. Details for each row of pictures are listed from left to right. (A) Matching of two EM structures of the DnaB.DnaC complex (top and side views). Shown are the EM envelope of objects A (resolution 42.2 Å); the virtual atoms representation of object B (resolution 26 Å) in red within its EM envelope; the identity match (score 1963, ranked 1); match deviating by 6° (score 1352, ranked 4); match deviating by 12° (score 96, ranked 7). (B) The virtual atoms representations of GroES-ADP₇-GroEL-ATP₇ (blue) and GroEL-ATP₇ (red) within their EM envelopes; top and side views of the DifferenceGrid results. (C) Virtual atoms representations of AMPPNP-<i>T</i>ClpB (blue), ATP-<i>T</i>ClpB mutant (red) and ADP-<i>T</i>ClpB (cyan); top views of the DifferenceGrid results for the match of AMPPNP-<i>T</i>ClpB to ATP-<i>T</i>ClpB mutant and for the match of ATP-<i>T</i>ClpB mutant to ADP-<i>T</i>ClpB; sections through the side views of the DifferenceGrid results for the same matches. (D) Virtual atoms representations of the 80S ribosome (blue) and the 40S subunit (red); the top ranking match and the DifferenceGrid results. (E) Side and bottom views of the top ranking docking model between Kv4.2*-KChlP2 (blue) and the simulated map of β2₄ (red); changes in the complementarity score as function of ψ, the rotation angle about the 4-fold axis of the predicted complex.</p

The dependency of the optimal <i>w</i> values on the resolution of the matched EM maps.

<p>The full circles depict the results of self-matching (for systems listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003594#pone-0003594-t001" target="_blank">Table 1</a>). The hollow circles depict the results of matching of related EM maps (the average resolution is used here). The regression line (y = −0.0612x+3.7551; R² = 0.78) is based only on the full circles. The error bars reflect the range of the optimal <i>w</i>.</p

Structure Article Variable Internal Flexibility Characterizes the Helical Capsid Formed by Agrobacterium VirE2 Protein on Single-Stranded DNA

Agrobacterium is known for gene transfer to plants. In addition to a linear ssDNA oligonucleotide, Agrobacterium tumefaciens secretes an abundant ssDNA-binding effector, VirE2. In many ways VirE2 adapts the conjugation mechanism to transform the eukaryotic host. The crystal structure of VirE2 shows two compact domains joined by a flexible linker. Bound to ssDNA, VirE2 forms an ordered solenoidal shell, or capsid known as the T-complex. Here, we present a three-dimensional reconstruction of the VirE2-ssDNA complex using cryo-electron microscopy and iterative helical real-space reconstruction. High-resolution refinement was not possible due to inherent heterogeneity in the protein structure. By a combination of computational modeling, chemica