4.8 Article

The Effects of Tacrolimus on Tissue-Specific, Protein-Level Inflammatory Networks in Vascularized Composite Allotransplantation

Journal

FRONTIERS IN IMMUNOLOGY
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2021.591154

Keywords

vascularized composite allotransplantation; inflammation; systems biology; network analysis; immunosuppression; acute rejection

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Funding

  1. Dept. of Defense [W81 XWH15-1-0336]

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This study used a systems biology approach to investigate the effects of tacrolimus on inflammatory networks in vascularized composite allotransplantation (VCA). Results revealed the significant roles of specific mediators such as interleukin-1β, interleukin-18, and interleukin-6 in different tissues after TAC treatment. Statistical analysis showed significant alterations in 24/27 inflammatory mediators between control and TAC-treated groups over time in peripheral blood, skin, and/or muscle. Virtual VCA and Virtual Animal analyses identified the skin as a key control point of dynamic inflammatory networks.
Systems-level insights into inflammatory events after vascularized composite allotransplantation (VCA) are critical to the success of immunomodulatory strategies of these complex procedures. To date, the effects of tacrolimus (TAC) immunosuppression on inflammatory networks in VCA, such as in acute rejection (AR), have not been investigated. We used a systems biology approach to elucidate the effects of tacrolimus on dynamic networks and principal drivers of systemic inflammation in the context of dynamic tissue-specific immune responses following VCA. Lewis (LEW) rat recipients received orthotopic hind limb VCA from fully major histocompatibility complex-mismatched Brown Norway (BN) donors or matched LEW donors. Group 1 (syngeneic controls) received LEW limbs without TAC, and Group 2 (treatment group) received BN limbs with TAC. Time-dependent changes in 27 inflammatory mediators were analyzed in skin, muscle, and peripheral blood using Principal Component Analysis (PCA), Dynamic Bayesian Network (DyBN) inference, and Dynamic Network Analysis (DyNA) to define principal characteristics, central nodes, and putative feedback structures of systemic inflammation. Analyses were repeated on skin + muscle data to construct a Virtual VCA, and in skin + muscle + peripheral blood data to construct a Virtual Animal. PCA, DyBN, and DyNA results from individual tissues suggested important roles for leptin, VEGF, various chemokines, the NLRP3 inflammasome (IL-1 beta, IL-18), and IL-6 after TAC treatment. The chemokines MCP-1, MIP-1 alpha; and IP-10 were associated with AR in controls. Statistical analysis suggested that 24/27 inflammatory mediators were altered significantly between control and TAC-treated rats in peripheral blood, skin, and/or muscle over time. Virtual VCA and Virtual Animal analyses implicated the skin as a key control point of dynamic inflammatory networks, whose connectivity/complexity over time exhibited a U-shaped trajectory and was mirrored in the systemic circulation. Our study defines the effects of TAC on complex spatiotemporal evolution of dynamic inflammation networks in VCA. We also demonstrate the potential utility of computational analyses to elucidate nonlinear, cross-tissue interactions. These approaches may help define precision medicine approaches to better personalize TAC immunosuppression in VCA recipients.

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