Inflammatory bowel disease (IBD) includes two principal types: ulcerative colitis (UC) and Crohn's disease (CD) . Different from CD that can happen anywhere in the gastrointestinal tract, UC is a chronic disorder, which occurs in the colon and rectum with the primary symptoms of abdominal pain and diarrhea mixed with blood . The etiology of IBD is unknown, current understanding of the pathogenesis suggests that a dysregulated immune response to intra-luminal antigens, which are of microbial origin (e.g. bacteria), leads to IBD including UC in a genetically susceptible host [1-3]. UC is characterized by chronic inflammation and epithelial injury but limited to the mucosa and submucosa with cryptitis and crypt abscesses [4, 5]. As the incidence of UC is generally increasing globally, there is significant morbidity and mortality associated with UC, which poses a major public health challenge worldwide particularly in developed countries . Despite significant advances in understanding of the pathogenesis in recent years, therapeutic treatment, which is far from optimal, has made UC as well as CD a notorious area of unmet medical need . Therefore, it is of great importance to understand the cellular and molecular mechanisms of immune responses in the progression of UC for development of effective therapies.
In the last few decades most studies of IBD immunopathogenesis have been concentrated on adaptive immunity [1, 8]. While CD has been designated to be a proinflammatory T helper (Th) 1-and/or Th17-type disease, UC has been characterized as a typical Th2 condition in which anti-inflammatory/regulatory cytokines, interleukin (IL)-13 and IL-5 rather than IL-4 predominate in the UC cytokine network . However, recent clinical data have shown that proinflammatory immune cells such as Th17 and Th1, respectively, with their secreted cytokines IL-17 and interferon-
In this work, a network model based on our previous study  is expanded to describe the dynamics of immune response in the progression of UC. We aim to elucidate the detailed cellular and molecular mechanism of the disease development and address the issues mentioned above.Ⅱ. METHODS A. Network Model
The UC-associated immune system is highly complex, providing a challenge to quantify the dynamics of immune response in UC progression. To reduce the complexity, a multi-scale network model is developed in this work by treating important cytokines, immune cells, and gut tissues as network nodes in a way similar to that in our previous study . In this network model, two types of inputs are initiated from a node: a positive or an up-regulation input (denoted by "
Although the exact pathogenesis of IBD (including UC) is not fully understood, it is generally accepted that genetic and environmental factors induce impaired epithelial barrier function (i.e., epithelial cell damage) that allows the translocation of commensal bacteria and microbial antigens from the gut lumen into the lamina propia, leading to immune cell activation and cytokine production [1, 9]. The innate immune cells such as macrophages and dentritic cells provide the first line of defense against any invading pathogens. Macrophages (M0) and dentritic cells (DC0) are located mostly in the intestinal lamina propia in close proximity to the epithelial monolayer. While resident macrophages and dentritic cells in the healthy intestine display an anergic and tolerogenic phenotype mediating tolerance to commensal bacteria (Bc) [11, 12], pro-inflammatory macrophages (M1) and dentritic cells (De) are activated in response to pathogenic bacteria (Bp) in IBD progression . M1 and De cells produce molecular mediators including IL-1, IL6, IL-12, IL-23, and tumor necrosis factor-
TD is also an important early source of IL-4 production  that leads to alternatively activated macrophages (M2) and Th4, respectively . M2 can release IL-10 and transforming growth factor, TGF-
Dendritic cells (DCs) are specialized antigen-presenting cells that orchestrate innate and adaptive immune responses. DCs (DC0) can be activated to incite a proinflammatory response (De) or to induce immune tolerance (Dt) in different local environments . Mature DCs migrate to mesenteric lymph nodes and present antigens to Naïve lymphocytes . Naïve, quiescent T cells (Th0) cannot enter the gut mucosa. Once activated by matured DC, they can move into the lamina propia and differentiate into effector T cells (predominantly Th1, Th2 and Th17 cells) and regulatory T cells T-regulatory (Treg) cells in their corresponding cytokine environments. For example, in the presence of IL-12 secreted by M1 (as well as De), Naïve CD4
The cytokines, immune cells, and TD discussed above are treated as the network nodes, whose interactions are then integrated into the network model shown in FIG. 1. In this network model, M1, De, Th1, and Th17 with their associated cytokines, TNF-
In this work, an agent-based network modeling (ABNM) method is used to study dynamics of the network discussed above. In this agent-based method (ABM) [25-27], TD and cytokines are treated as patch variables whereas the immune cells are treated as agents . The TD patches have a local parameter denoted as tissue life associated with tissue damage. This parameter is set between 0 and 100 in which 0 represents a completely destroyed patch where the value of TD=100%, and 100 means full health (TD=0%). Agents represent individual entities that can move from patch to patch . The agents in the present model represent multiple types of cells, (FIG. 1). In the following discussion, 11 agent variables are used for Bc and Bp, and the different immune cell types. Netlogo5.3 (Center for Connected Learning and Computer Based Modeling, Northwestern University) is applied to perform the ABNM simulations.
Model dynamics starts with an entry of Bc and Bp bacteria into intestinal lamina propria by overcoming epithelial barrier, triggering a cascade of immune responses. The ABNM simulations are initiated by randomly placing a certain number of Bc and Bp in lamina propria. Here, 5 units of Bc and 25 units of Bp are used for the first time. The same amount of Bc and Bp can be placed at every later time point for continual simulations of the immune response. ABNM dynamics is governed by a set of rules that describe the interactions between the network nodes (agents and patches) shown in FIG. 1. The associated rules are given in Table S1 in supplementary materials. The model environment comprises 2500 patches on a square arranged in a grid (50 patches
The time courses of the changes in the immune cells, cytokines in response to Bc and Bp are presented in FIG. 2-5, respectively. Seen in FIG. 2, the population of M1 along with M1-produced TNF-
In recent years, targeting cytokines for UC therapies have become an important therapeutic strategy for UC treatment . To identify important cytokines in UC progression, in silico knockout simulations for TNF-
As seen in FIG. 7, TNF-
As discussed above, UC is chronic inflammatory disease caused by an abnormal immune response against persistent invasions of commensal bacteria in genetically susceptible subjects. Despite significant advances in understanding of UC immuno-pathogenesis, the immunological profile in UC patients still remains controversial. UC has been classically characterized as an atypical Th-2 type disease in which anti-inflammatory/regulatory cytokines such as IL-13 and IL-5 are predominantly enhanced at high levels, but with low expression of IL-4 [1, 31, 32]. However, experiments showed that pro-inflammatory immune cells, M1, Th17 and Th1 with the associated cytokines TNF-
To address the above issue, a network model is developed in this work based on our previous study but in different context . An agent-based network modeling method (ABNM) is then applied for computer simulations of the dynamics of immune response in UC progression. Our modeling study demonstrates that the immune response in UC progression is mainly anti-inflammatory/regulatory, but pro-inflammatory cells and their associated molecular mediators still remain at certain levels (generally lower than those with CD). Specifically, from our simulations anti-inflammatory/regulatory cells, M2 and type Ⅱ NKT, dominate proinflammatory M1 cells. However, M1 and the M1-producing cytokines, TNF-
This work was supported by the National Natural Science Foundation of China (No.21273209).
Supplementary materials: Table S1 lists agent-based rules.
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