Multi-pathway network analysis of mammalian epithelial cell responses in inflammatory environments

In immune defense: redefining the role of the immune system in chronic disease

The recognition of altered immune system function in many chronic disease states has proven to be a pivotal advance in biomedical research over the past decade. For many metabolic and mood disorders, this altered immune activity has been characterized as inflammation, with the attendant assumption that the immune response is aberrant. However, accumulating evidence challenges this assumption and suggests that the immune system may be mounting adaptive responses to chronic stressors. Further, the inordinate complexity of immune function renders a simplistic, binary model incapable of capturing critical mechanistic insights. In this perspective article, we propose alternative paradigms for understanding the role of the immune system in chronic disease. By invoking allostasis or systems biology rather than inflammation, we can ascribe greater functional significance to immune mediators, gain newfound appreciation of the adaptive facets of altered immune activity, and better avoid the potentially disastrous effects of translating erroneous assumptions into novel therapeutic strategies.

Combinatorial code governing cellular responses to complex stimuli

Cells adapt to their environment through the integration of complex signals. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. Here, we use a systematic theoretical approach to enumerate the possible interaction profiles for outputs measured in the conditions 0 (control), signals X, Y, X+Y. Combinatorial analysis reveals 82 possible interaction profiles, which we biologically and mathematically grouped into five positive and five negative interaction modes. To experimentally validate their use in living cells, we apply an original computational workflow to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each interaction mode was preferentially used in specific biological pathways, suggesting a functional role in the adaptation to multiple signals. Our work defines an exhaustive map of interaction modes for cells integrating pairs of physiopathological and pharmacological stimuli.

Cells constantly integrate information from multiple stimuli. By considering every possible means by which two stimuli can interact, Cappuccio et al. define 10 interaction modes and demonstrate their preferential use by dendritic cells responding to different combinations of microbial and host inflammatory cues.

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.