Lee, N.-R. | bioRxiv Science

bioRxiv2022

Kinetics-based Inference of Environment-Dependent Microbial Interactions and Their Dynamic Variation

Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. This unique capability of our approach was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids), but competitively growing on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates, but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics. IMPORTANCEModeling of environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer complex dynamics of microbial communities. Moreover, robust prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among microorganisms such as plants and animals, as well as microorganisms.

bioinformatics↗

bioRxiv2018

Activation of RIG-I-mediated antiviral signaling triggers autophagy through the MAVS-TRAF6-Beclin-1 signaling axis

Autophagy has been implicated in innate immune responses against various intracellular pathogens. Recent studies have reported that autophagy can be triggered by pathogen recognizing sensors, including Toll-like receptors and cyclic guanosine monophosphate-adenosine monophosphate synthase, to participate in innate immunity. In the present study, we examined whether the RIG-I signaling pathway, which detects viral infections by recognizing viral RNA, triggers the autophagic process. The introduction of polyI:C into the cytoplasm, or Sendai virus infection, significantly induced autophagy in normal cells but not in RIG-I-deficient cells. PolyI:C transfection or Sendai virus infection induced autophagy in the cells lacking type-I interferon signaling. This demonstrated that the effect was not due to interferon signaling. RIG-I-mediated autophagy diminished by the deficiency of mitochondrial antiviral signaling protein (MAVS) or tumor necrosis factor receptor-associated factor (TRAF)6, showing that the RIG-I-MAVS-TRAF6 signaling axis was critical for RIG-I-mediated autophagy. We also found that Beclin-1 was translocated to the mitochondria, and it interacted with TRAF6 upon RIG-I activation. Furthermore, Beclin-1 underwent K63-polyubiquitination upon RIG-I activation, and the ubiquitination decreased in TRAF6-deficient cells. This suggests that the RIG-I-MAVS-TRAF6 axis induced K63-linked polyubiquitination of Beclin-1, which has been implicated in triggering autophagy. Collectively, the results of this study show that the recognition of viral infection by RIG-I is capable of inducing autophagy to control viral replication. As deficient autophagy increases the type-I interferon response, the induction of autophagy by the RIG-I pathway might also contribute to preventing an excessive interferon response as a negative-feedback mechanism.\n\nImportanceMammalian cells utilize various innate immune sensors to detect pathogens. Among those sensors, RIG-I recognizes viral RNA to detect intracellular viral replication. Although cells experience diverse physiological changes upon viral infection, studies to understand the role of RIG-I signaling have focused on the induction of type-I interferon. Autophagy is a process that sequesters cytosolic regions and degrades the contents to maintain cellular homeostasis. Autophagy participates in the immune system, and has been known to be triggered by some innate immune sensors, such as TLR4 and cGAS. We demonstrated that autophagy can be triggered by the activation of RIG-I. In addition, we also proved that MAVS-TRAF6 downstream signaling is crucial for the process. Beclin-1, a key molecule in autophagy, is translocated to mitochondria, where it undergoes K63-ubiquitination in a TRAF6-dependent manner upon RIG-I activation. As autophagy negatively regulates RIG-I-mediated signaling, the RIG-I-mediated activation of autophagy may function as a negative-feedback mechanism.

immunology↗