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

Abstract

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-1β, IL-18), and IL-6 after TAC treatment. The chemokines MCP-1, MIP-1α; 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.

Cellular activation pathways and interaction networks in vascularized composite allotransplantation

“…This effect might be due to the induction of T cell hyporesponsiveness and reduced IFN-γ secretion. APC produce IL-12 leading to increased IL-4 levels, which have been demonstrated to promote M2 macrophage differentiation and function, as well as represent a marker for acute VCA rejection ( 151 ). This macrophage subset has been implicated with graft rejection ( 152 , 153 ).…”

Inferring Tissue-Specific, TLR4-Dependent Type 17 Immune Interactions in Experimental Trauma/Hemorrhagic Shock and Resuscitation Using Computational Modeling

“…We therefore hypothesized that the expression of TLR4 impacts the cross-compartment spread of inflammation, and thus compared the dynamic evolution of inflammation in WT and TLR4 -/- mice following T/HS-R. While dynamic network inference algorithms such as Dynamic Network Analysis ( 18 ) and Dynamic Bayesian Network (DyBN) inference ( 20 ) have both proven useful in identifying novel features of systemic inflammation following trauma/hemorrhage, as well as for defining cross-tissue interactions in other context of inflammation ( 34 ), these methods are not designed explicitly for multi-dimensional analysis. Emerging hypergraph methods ( 23 – 26 ) hold the potential for this type of analysis, and therefore we carried out a hypergraph analysis of the multi-tissue dataset at each time point ( Figures 2 – 7 ).…”

“…Our prior work was aimed at defining networks of individual inflammatory mediators interacting over time within a given tissue, as well as principal characteristics/drivers of these responses. Here, we utilized hypergraphs to extend prior work defining how mediators interact across tissues over time ( 34 ). Hypergraphs provide several advantages when visualizing and interpreting multi-compartment data, such as that examined in this study.…”

Peripheral nerve repair is associated with augmented cross-tissue inflammation following vascularized composite allotransplantation

“…Given the unique sets of enriched pathways depending on tissue type, we hypothesized that nerve repair (NR) might impact the dynamic process of cross-tissue inflammation. Given the ethical and practical limitations of testing this hypothesis in human VCA, we carried out a reverse-translational study of experimental VCA in rats in which Lewis rats (n= 8 per group) received either syngeneic (Lewis) or allogeneic (Brown-Norway) orthotopic hind limb transplants including TAC ± sciatic NR as described previously (12). We studied the following experimental groups: syngeneic transplant, syngeneic transplant + NR, allogeneic (i.e., VCA) +TAC, and allogeneic +TAC + NR all studied to day 31.…”

“…Tacrolimus (FK506; TAC), the standard-of-care immunosuppressive agent used in VCA (9), can also enhance nerve regeneration (10,11). Immuno-inflammation is regulated by multiple neural mechanisms, and this mode of regulation may play a role in the context of transplantation (12). However, the relevance and impact of NR and downstream nerve regeneration as contributors to, or as targets of, the inflammatory immune response after VCA are unknown.…”