In this study, we sought to investigate possible CD4+ T cell phenotypes under diverse environmental conditions, and how the dosage of extracellular inputs (cytokines and TCR) regulates these phenotypes. To study these questions, we build a knowledge driven logic based mechanistic model of CD4+ T-cells. We performed extensive search of published literature to build the model, however it is possible that model may have missing links or components. Logic based models found to be useful in the absence of detailed biochemical information and were previously used to model gene regulation, and signal transduction (Ref.). Next, using all inputs compositions, we systematically characterized the network behaviors. We found that the network arrived …show more content…
Thus, the clear differentiation of Th17 will depend on dosage of IL6. In a recent study it was found that RORγt – Foxp3 co-expressing cells were enriched in the large intestine (Ohnmacht et al., 2015, Fang and Zhu, 2017). Furthermore, Th17-Treg phenotypes were also observed in vivo in autoimmune diabetes model and in vitro in lamina propria (Ichiyama et al., 2008; Zhou et al., 2008; Evans and Jenner, 2013). Moreover, Th1-Treg intermediate phenotypes were observed during Th1 polarizing infections (Stock et al., 2004; Koch et al., 2009, Oldenhove et al., 2009; Evans and Jenner, 2013).
In addition to the hybrid phenotypes co-expressing two lineage specifying T cells, we also obtained phenotypes with the activity of more than two TFs, i.e., Th1-Th2-Treg, Th1-Th17-treg, and Th1-Th2-Th17-Treg. It was shown previously that the basal levels of Tbet and GATA3 can be expressed by subset of Treg cells and the dynamic balance of these TFs helps in maintaining immune tolerance (Yu et al., 2015). Here, we predict the novel phenotypes that can activate three TFs (Tbet-GATA3-Foxp3, Tbet-RORγt-Foxp3) and all four TFs (Tbet-GATA3-RORγt-Foxp3). However, in these phenotypes Tbet and GATA3 have the higher activity levels than the activity levels of Foxp3.
By analyzing all possible input compositions, we obtained the minimal and maximal inputs for each phenotype. The minimal composition include minimum number of inputs that can stimulate a phenotype, on the
IBD is the result of an abnormal innate repsonce to antigens in the intestinal flora, thus activating the adaptive immune repsinse; a process in which genome wide stidues have implicated several genes such as NOD2 and ATG16L1, both involved in the intracellular processing of bacterial antigens. PSC has shown similar mechanisms characterized by Th1 cytokines and structuring. However, PSC has aslo shown close association with classic autoimmune diseases such as type 1 diabetes, rheumatoid arthiritis, and others; has shown a strong association with the human leukocyte antigen(HLA) DRB1*0301 haplotype; and has shown a variety of autoantigens to neutrophils and biliary epithelial cells (BEC). (1) Thus suggesting the immunopathogenesis of PSC is complicated and may have more than one cause.
The human immune system plays a major role in every person’s body. It protects the body against foreign particles that could lead to diseases, such as bacteria and viruses. A white blood cell protein, called CD4, play a key role in the human immune system. Without the help of CD4 proteins, bodies would not be able to receive the signal to activate the body’s immune response against foreign contaminants entering the body.
Some T lymphocytes (T cells), unlike B cells are able to attack targeted antigens directly and neutralise their effect on the body. Some send chemical messages to the rest of the immune system which help produce effective defences against the bacteria or virus, and some T cells even help B cells produce antibodies.
I understand how create representations and models to describe immune responses. There are two main types of immune responses; primary and secondary. Primary is the first time a response occurs in the presence of an antigen while secondary refers to a response after the initial. Both may be modeled by showing examples of an antigen and the respective secondary/primary response. (Campbell 719-720)
Previous studies have shown that killer T cells that protects us from bacterial damage plays a major role in the
In 1995, Yao et al, was the first to discover IL-17A (Yao et al., 1995). Since then it has been the most studied member of the IL-17 cytokine family. IL-17A shares the highest sequence homology (50%) with IL-17F (Moseley et al., 2003). Thus, these two molecules can
If you are thinking that it might have something to do with the number 4, you are correct. A __tetramer__ is a large molecule made of four subunits. The next term, __major histocompatibility complexes (MHCs)__, is a bit more complicated. I mean, usually, when the word complex is involved, it means it is complicated. Here is where the part about taking advantage of components found in our bodies comes in. The components used in tetramer analysis are called MHCs, which are proteins on the surface of most of your cells that help T-cells fight diseases and cancer. MHCs tell T-cells what’s going on inside the cell by showing bits of stuff from inside the cells. And T-cell will decide whether the cell is healthy or if it needs to be destroyed. An easy way to think this interaction is that T-cells are the body’s hall monitors and MHC molecules are hall passes carried by the cells. A cell will get be destroyed by the T-cell “hall monitor” or other immune cells if it tries to get away showing something that shouldn’t be in your body in its MHC or using a “faulty hall
This model shows the frequency of cellular morphology disruptions throughout the various types of cells. The most common pattern of cellular disruption was observed in natural killer cells. The hallmark of this figure was proving the diversity of rank responsive genes even when using a highly specific analysis method.
The CD4+ T cells follows a separate pathway depending on cytokines milieu. In the presence of interleukin -12 (IL-12) the CD4+ T cell differentiate into interferon-γ (IFNγ) which secret Th1 helper cell. In the presence of interleukin-23 (IL-23), he CD4+ T cell differentiate into interleukin-17 (IL-17) which secret Th17 cell. In normal physiological conditions, the function of Th1 cells is mediate defenses against intracellular pathogen, whereas Th17 cells are implicated in(3).
T-cells are one of the key factors in the adaptive immune system which specifically recognize the virus infected and tumors cells. These cells recognize antigens (epi-topes or tumor antigens) using protein known as T-cells receptor TCR which is heterodimer and is consisted of an alpha (α) and beta (β) chains. The fully matured T-cells population expressing αβ T-cells receptors (TCR) composed of the T-cells which expresses CD4 or CD8, and the CD4- or CD8- γδ expressing T-cells receptor cells. The specificity of the CD4+ and CD8+ T-cells is directed by the expression of the co-receptors, CD4 and CD8. The ligand for CD4 is the β2 domain of the (MHC) class II molecule however for the CD8 the ligand is α3 domain of MHC class I moluecule. Therefore,
Similarly, phenotypic variability is influenced by environmental factors, genetic factors, and relation of an environment with genetic components. These factors can overall lead to changes in a population. Changes in an organism genes can lead to adaptation to increasing the organism survival and reproductive success. The environment will apply pressure to forcing an organism to develop ways to cope. The environment is able to affect individuals in a population even in the absence of genetic differences. The possibility of knowing the genotype of an organism has allow mathematic model to compute value for traits to predict the phenotype. Quantitative traits are represented by values cause by one gene or group of genes. The book indicates that it is confusing to think of a trait of having only one gene because multiple genes can produce that trait. And therefore quantitative traits can be deceptive in giving sign for blending inheritance. These traits are affected by many alleles which cause them to change continuously in many
The classical view of Th1/Th2 paradigm argues that T cell polarization is determined by the nature of the pathogen and their infectious cycle. Therefore Th1 response is triggered by intracellular pathogen while Th2 response is triggered by helminth infections. Although microbes and local microbial environment does affect T cell differentiation into Th1 or Th2, this is not the entire story. In this essay, I will explain how the host can determine T cell polarization. Hormonal factors as well as the nature of antigen presenting cells have an effect in preferential T cell differentiation.
TLRs are primary transmembrane proteins of immune cells, that contain leucine repeats in their extracellular domains and a cytoplasmic tail that contains a conserved region called the Toll / IL1 receptor (TIR) domain.
Experimental evidence supports that Flt3-L is involved the homeostatic feedback loop between DC and Treg.25 Researchers investigated the effect of Treg depletion on DC development using a Foxp3DTR mouse model.26 These mice express the diphtheria toxin receptor (DTR) under the control of the Foxp3 promoter and treatment of these mice with diphtheria toxin (DT) results in depletion of Foxp3+ Treg. After Foxp3+ Treg depletion in these mice, the frequency of CD11chiI-Ab+CD11b+ cDC increased, but the PDCA-1+ pDC decreased in the lymph nodes or spleen. The level of expression of DC maturation markers including CD80 and CD40 was upregulated on cDC but not on PDCA-1+ pDC, indicating that Treg restrain cDC maturation.26 Treg-depleted
(e.g. Th17/22 > in Asian populations) [77]. Although, the pathogenic role of each of these additional Th-460 subgroups has to be fully clarified, it is possible to speculate that a better understanding of the different 461 player could lead to better treatment plan. The central role of Th2 inflammation has been known for a long 462 time in human allergic diseases, but it has been further confirmed by the positive effect of treatment with 463 biologics targeting the Th2 axis (e.g. Dupilumab). 464