Spatiotemporally specific roles of TLR4, TNF, and IL-17A in murine endotoxin-induced inflammation inferred from analysis of dynamic networks

Abstract Background Bacterial lipopolysaccharide (LPS) induces a multi-organ, Toll-like receptor 4 (TLR4)-dependent acute inflammatory response. Methods Using network analysis, we defined the spatiotemporal dynamics of 20, LPS-induced, protein-level inflammatory mediators over 0–48 h in the heart, gut, lung, liver, spleen, kidney, and systemic circulation, in both C57BL/6 (wild-type) and TLR4-null mice. Results Dynamic Network Analysis suggested that inflammation in the heart is most dependent on TLR4, followed by the liver, kidney, plasma, gut, lung, and spleen, and raises the possibility of non-TLR4 LPS signaling pathways at defined time points in the gut, lung, and spleen. Insights from computational analyses suggest an early role for TLR4-dependent tumor necrosis factor in coordinating multiple signaling pathways in the heart, giving way to later interleukin-17A—possibly derived from pathogenic Th17 cells and effector/memory T cells—in the spleen and blood. Conclusions We have derived novel, systems-level insights regarding the spatiotemporal evolution acute inflammation.

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Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification

10.1101/2021.05.06.443021 ◽

2021 ◽

Author(s):

Emily Y Su ◽

Abby Spangler ◽

Qin Bian ◽

Jessica Y Kasamoto ◽

Patrick Cahan

Keyword(s):

Signaling Pathways ◽

Cell Fate ◽

Regulatory Networks ◽

Target Genes ◽

Dynamic Networks ◽

Dynamic Network ◽

Primitive Streak ◽

Reconstruction Methods

Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Computational gene regulatory network (GRN) reconstruction methods aim to map this control by inferring relationships from transcriptomic data. Unfortunately, existing methods are imprecise, may be computationally burdensome, and do not illustrate how networks transition from one topology to another. Here we present Epoch, a computational network reconstruction tool that leverages single cell transcriptomics to infer dynamic network structures. Epoch performs favorably when benchmarked on reconstruction of synthetically generated, in vivo, and in vitro data. To illustrate the usefulness of Epoch, we used it to identify the dynamic networks underpinning directed differentiation of mouse ESC guided by multiple primitive streak induction treatments. Our analysis demonstrates that modulating signaling pathways drives topological network changes that shape cell fate potential. We also find that Peg3 is a central contributor to the rewiring of the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRN structures, we traced how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. The methods presented here are available in the R package Epoch, and provide a foundation for future work in understanding the biological implications of dynamic regulatory structures.

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Dynamic contextual influences on social motivation and behavior in schizophrenia: a case-control network analysis

npj Schizophrenia ◽

10.1038/s41537-021-00189-6 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Varsha D. Badal ◽

Emma M. Parrish ◽

Jason L. Holden ◽

Colin A. Depp ◽

Eric Granholm

Keyword(s):

Network Analysis ◽

Positive Emotions ◽

Dynamic Networks ◽

Dynamic Network ◽

Social Motivation ◽

Emotional States ◽

Contextual Influences ◽

Network Analyses ◽

And Behavior ◽

Ema Data

AbstractContextual influences on social behavior and affective dynamics are not well understood in schizophrenia. We examined the role of social context on emotions, and the motivation to interact in the future, using dynamic network analysis of ecological momentary assessment (EMA) data. Participants included 105 outpatients with schizophrenia or schizoaffective disorder (SZ) and 76 healthy comparators (HC) who completed 7 days, 7 times a day of EMA. Dynamic networks were constructed using EMA data to visualize causal interactions between emotional states, motivation, and context (e.g., location, social interactions). Models were extended to include the type and frequency of interactions and the motivation to interact in the near future. Results indicated SZ networks were generally similar to HC but that contextual influences on emotion and social motivation were more evident in SZ. Further, feedback loops in HC were likely adaptive (e.g., positive emotions leading to social motivation), but most were likely maladaptive in SZ (e.g., sadness leading to reduced happiness leading to increased sadness). Overall, these findings indicate that network analyses may be useful in specifying emotion regulation problems in SZ and that instability related to contextual influences may be a central aspect of aberrant regulation.

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