Synergistic Communication between CD4+ T Cells and Monocytes Impacts the Cytokine Environment.

Physiological cytokine environments arise from factors produced by diverse cell types in coordinated concert. Understanding the contributions of each cell type in the context of cell-cell communication is important for effectively designing disease modifying interventions. Here, we present multi-plexed measurement of 48 cytokines from a coculture system of primary human CD4+ T cells and monocytes across a spectrum of stimuli and for a range of relative T cell/monocyte compositions, coupled with corresponding measurements from PBMCs and plasma from the same donors. Computational analysis of the resulting data-sets elucidated communication-independent and communication-dependent contributions, including both positive and negative synergies. We find that cytokines in cell supernatants were uncorrelated to those found in plasma. Additionally, as an example of positive synergy, production levels of CXCR3 cytokines IP-10 and MIG, depend non-linearly on both IFNγ and TNFα levels in cross-talk between T cells and monocytes. Overall, this work demonstrates that communication between cell types can significantly impact the consequent cytokine environment, emphasizing the value of mixed cell population studies.

Pregnancy, Labor, and Delivery after Ebola Virus Disease and Implications for Infection Control in Obstetric Services, United States.

Many of the survivors of the 2014-2015 epidemic of Ebola virus disease (EVD) in western Africa were women of childbearing age. Limited clinical and laboratory data exist that describe these women's pregnancies and outcomes. We report the case of an EVD survivor who became pregnant and delivered her child in the United States, and we discuss implications of this case for infection control practices in obstetric services. Hospitals in the United States must be prepared to care for EVD survivors.

Enhancement of COPD biological networks using a web-based collaboration interface.

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

Network analysis of differential Ras isoform mutation effects on intestinal epithelial responses to TNF-α.

Tumor necrosis factor alpha (TNF-α) is an inflammatory cytokine that can elicit distinct cellular behaviors under different molecular contexts. Mitogen activated protein kinase (MAPK) pathways, especially the extracellular signal-regulated kinase (Erk) pathway, help to integrate influences from the environmental context, and therefore modulate the phenotypic effect of TNF-α exposure. To test how variations in flux through the Erk pathway modulate TNF-α-elicited phenotypes in a complex physiological environment, we exposed mice with different Ras mutations (K-Ras activation, N-Ras activation, and N-Ras ablation) to TNF-α and observed phenotypic and signaling changes in the intestinal epithelium. Hyperactivation of Mek1, an Erk kinase, was observed in the intestine of mice with K-Ras activation and, surprisingly, in N-Ras null mice. Nevertheless, these similar Mek1 outputs did not give rise to the same phenotype, as N-Ras null intestine was hypersensitive to TNF-α-induced intestinal cell death while K-Ras mutant intestine was not. A systems biology approach applied to sample the network state revealed that the signaling contexts presented by these two Ras isoform mutations were different. Consistent with our experimental data, N-Ras ablation induced a signaling network state that was mathematically predicted to be pro-death, while K-Ras activation did not. Further modeling by constrained Fuzzy Logic (cFL) revealed that N-Ras and K-Ras activate the signaling network with different downstream distributions and dynamics, with N-Ras effects being more transient and diverted more towards PI3K-Akt signaling and K-Ras effects being more sustained and broadly activating many pathways. Our study highlights the necessity to consider both environmental and genomic contexts of signaling pathway activation in dictating phenotypic responses, and demonstrates how modeling can provide insight into complex in vivo biological mechanisms, such as the complex interplay between K-Ras and N-Ras in their downstream effects.

Prediction of calcite morphology from computational and experimental studies of mutations of a de novo-designed peptide.

Many organisms use macromolecules, often proteins or peptides, to control the growth of inorganic crystals into complex materials. The ability to model peptide-mineral interactions accurately could allow for the design of novel peptides to produce materials with desired properties. Here, we tested a computational algorithm developed to predict the structure of peptides on mineral surfaces. Using this algorithm, we analyzed energetic and structural differences between a 16-residue peptide (bap4) designed to interact with a calcite growth plane and single- and double-point mutations of the charged residues. Currently, no experimental method is available to resolve the structures of proteins on solid surfaces, which precludes benchmarking for computational models. Therefore, to test the models, we chemically synthesized each peptide and analyzed its effects on calcite crystal growth. Whereas bap4 affected the crystal growth by producing heavily stepped corners and edges, point mutants had variable influences on morphology. Calculated residue-specific binding energies correlated with experimental observations; point mutations of residues predicted to be crucial to surface interactions produced morphologies most similar to unmodified calcite. These results suggest that peptide conformation plays a role in mineral interactions and that the computational model supplies valid energetic and structural data that can provide information about expected crystal morphology.

De novo design of peptide-calcite biomineralization systems.

Many organisms produce complex, hierarchically structured, inorganic materials via protein-influenced crystal growth--a process known as biomineralization. Understanding this process would shed light on hard-tissue formation and guide efforts to develop biomaterials. We created and tested a computational method to design protein-biomineralization systems. The algorithm folds a protein from a fully extended structure and simultaneously optimizes the fold, orientation, and sequence of the protein adsorbed to a crystal surface. We used the algorithm to design peptides (16 residues) to modify calcite (CaCO(3)) crystallization. We chemically synthesized six peptides that were predicted to bind different states of a calcite growth plane. All six peptides dramatically affected calcite crystal growth (as observed by scanning electron microscopy), and the effects were dependent on the targeted state of the {001} growth plane. Additionally, we synthesized and assayed scrambled variants of all six designed peptides to distinguish cases where sequence composition determines the interactions versus cases where sequence order (and presumably structure) plays a role. Scrambled variants of negatively charged peptides also had dramatic effects on calcite crystallization; in contrast, scrambled variants of positively charged peptides had a variable effect on crystallization, ranging from dramatic to mild. Special emphasis is often placed on acidic protein residues in calcified tissue mineralization; the work presented here suggests an important role for basic residues as well. In particular, this work implicates a potential role for basic residues in sequence-order specificity for peptide-mineral interactions.