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Investigating the role of T-cell avidity and killing efficacy in relation to type 1 diabetes prediction.

Khadra A, Pietropaolo M, Nepom GT, Sherman A - PLoS ONE (2011)

Bottom Line: The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells.These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects.Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
During the progression of the clinical onset of Type 1 Diabetes (T1D), high-risk individuals exhibit multiple islet autoantibodies and high-avidity T cells which progressively destroy beta cells causing overt T1D. In particular, novel autoantibodies, such as those against IA-2 epitopes (aa1-577), had a predictive rate of 100% in a 10-year follow up (rapid progressors), unlike conventional autoantibodies that required 15 years of follow up for a 74% predictive rate (slow progressors). The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells. For this purpose, we build a series of mathematical models incorporating first one clone then multiple clones of islet-specific and pathogenic CD8 and/or CD4 T cells, together with B lymphocytes, to investigate the interaction of T-cell avidity with autoantibodies in predicting disease onset. These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects. The model shows that the level and persistence of autoantibodies depends not only on the avidity of T cells, but also on the killing efficacy of these cells. Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies. Such studies may lead to early diagnosis of the disease in high-risk individuals and thus potentially serve as a means of staging patients for clinical trials of preventive or interventional therapies far before disease onset.

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Avidity maturation of the two main clones of T cells considered in Fig. 7.Each clone is reactive to a given autoantigen (either Auto-Ag or Auto-Ag, respectively). The average of the reciprocal of avidity, , is measured for  (black) and  (gray) () for 12 years using the time evolutions of the four subclones in Fig. 7. Here, panel (A) corresponds to the upper panels (A1–A4) of Fig. 7, (B) corresponds to the middle panels (B1–B4) and (C) corresponds to the bottom panels (C1–C4). The eventual decay of the quantity , associated with the two subclones  and , shown in gray in panel (C), to a steady state level lower than the ones reached in panels (A) and (B), indicates increased average avidity relative to (A) and (B), and thus worse impact on beta cells, as demonstrated in panel (C3) of Fig. 7.
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pone-0014796-g009: Avidity maturation of the two main clones of T cells considered in Fig. 7.Each clone is reactive to a given autoantigen (either Auto-Ag or Auto-Ag, respectively). The average of the reciprocal of avidity, , is measured for (black) and (gray) () for 12 years using the time evolutions of the four subclones in Fig. 7. Here, panel (A) corresponds to the upper panels (A1–A4) of Fig. 7, (B) corresponds to the middle panels (B1–B4) and (C) corresponds to the bottom panels (C1–C4). The eventual decay of the quantity , associated with the two subclones and , shown in gray in panel (C), to a steady state level lower than the ones reached in panels (A) and (B), indicates increased average avidity relative to (A) and (B), and thus worse impact on beta cells, as demonstrated in panel (C3) of Fig. 7.

Mentions: We simulate in Fig. 9 the quantity (black) and (gray) using the subclones of Fig. 7, where each panel in Fig. 9 corresponds to the similarly labeled row in Fig. 7 ((A) for top, (B) for middle and (C) for bottom row). In both panels (A) and (B), rises rapidly to an elevated level and remains elevated for 3–4 years, then starts declining over time to its steady state level due to the emergence of the subclone , indicating an increase in the average avidity of Auto-Ag-reactive T-cell clone. (The final rises observed at the end of each simulation is inconsequential because they occur when both subclones are at near-zero levels.) On the other hand, the dominance of subclone , due to its larger -value, in these two cases, causes to rise rapidly to its steady state (i.e. ) with no avidity maturation. The increase in to its steady state level, however, is larger in panel (B) than in panel (A), indicating a population of T cells less effective in destroying beta cells in the former than in the latter. This is consistent with the results in panels (A3) and (B3) of Fig. 7. In panel (C) of Fig. 9, , , exhibit slightly different behaviour. goes to a steady state lower than its initial value after 2 years of transient elevation that is close to that obtained in panels (A) and (B). , on the other hand, initially rises to an elevated level, then declines to its steady state level in about 4 years, due to a reduced suppression of subclone by . (This competition is mediated by a change in stability as described above.) The steady-state level reached in this case is lower than those attained in Figs. 9(A), (B). Thus, after about 5 years, the Auto-Ag-reactive clone has high average avidity () and is also more effective in killing beta cells. This is consistent with panel (C3) of Fig. 7, showing beta-cell number declining below the 30% threshold and exhibiting significantly worse outcomes than those observed in Fig. 9(A). In other words, T-cell competition in the last case led to avidity maturation because of reduction in the avidity of compared to the previous two cases, which increased beta-cell destruction.


Investigating the role of T-cell avidity and killing efficacy in relation to type 1 diabetes prediction.

Khadra A, Pietropaolo M, Nepom GT, Sherman A - PLoS ONE (2011)

Avidity maturation of the two main clones of T cells considered in Fig. 7.Each clone is reactive to a given autoantigen (either Auto-Ag or Auto-Ag, respectively). The average of the reciprocal of avidity, , is measured for  (black) and  (gray) () for 12 years using the time evolutions of the four subclones in Fig. 7. Here, panel (A) corresponds to the upper panels (A1–A4) of Fig. 7, (B) corresponds to the middle panels (B1–B4) and (C) corresponds to the bottom panels (C1–C4). The eventual decay of the quantity , associated with the two subclones  and , shown in gray in panel (C), to a steady state level lower than the ones reached in panels (A) and (B), indicates increased average avidity relative to (A) and (B), and thus worse impact on beta cells, as demonstrated in panel (C3) of Fig. 7.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3091860&req=5

pone-0014796-g009: Avidity maturation of the two main clones of T cells considered in Fig. 7.Each clone is reactive to a given autoantigen (either Auto-Ag or Auto-Ag, respectively). The average of the reciprocal of avidity, , is measured for (black) and (gray) () for 12 years using the time evolutions of the four subclones in Fig. 7. Here, panel (A) corresponds to the upper panels (A1–A4) of Fig. 7, (B) corresponds to the middle panels (B1–B4) and (C) corresponds to the bottom panels (C1–C4). The eventual decay of the quantity , associated with the two subclones and , shown in gray in panel (C), to a steady state level lower than the ones reached in panels (A) and (B), indicates increased average avidity relative to (A) and (B), and thus worse impact on beta cells, as demonstrated in panel (C3) of Fig. 7.
Mentions: We simulate in Fig. 9 the quantity (black) and (gray) using the subclones of Fig. 7, where each panel in Fig. 9 corresponds to the similarly labeled row in Fig. 7 ((A) for top, (B) for middle and (C) for bottom row). In both panels (A) and (B), rises rapidly to an elevated level and remains elevated for 3–4 years, then starts declining over time to its steady state level due to the emergence of the subclone , indicating an increase in the average avidity of Auto-Ag-reactive T-cell clone. (The final rises observed at the end of each simulation is inconsequential because they occur when both subclones are at near-zero levels.) On the other hand, the dominance of subclone , due to its larger -value, in these two cases, causes to rise rapidly to its steady state (i.e. ) with no avidity maturation. The increase in to its steady state level, however, is larger in panel (B) than in panel (A), indicating a population of T cells less effective in destroying beta cells in the former than in the latter. This is consistent with the results in panels (A3) and (B3) of Fig. 7. In panel (C) of Fig. 9, , , exhibit slightly different behaviour. goes to a steady state lower than its initial value after 2 years of transient elevation that is close to that obtained in panels (A) and (B). , on the other hand, initially rises to an elevated level, then declines to its steady state level in about 4 years, due to a reduced suppression of subclone by . (This competition is mediated by a change in stability as described above.) The steady-state level reached in this case is lower than those attained in Figs. 9(A), (B). Thus, after about 5 years, the Auto-Ag-reactive clone has high average avidity () and is also more effective in killing beta cells. This is consistent with panel (C3) of Fig. 7, showing beta-cell number declining below the 30% threshold and exhibiting significantly worse outcomes than those observed in Fig. 9(A). In other words, T-cell competition in the last case led to avidity maturation because of reduction in the avidity of compared to the previous two cases, which increased beta-cell destruction.

Bottom Line: The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells.These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects.Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
During the progression of the clinical onset of Type 1 Diabetes (T1D), high-risk individuals exhibit multiple islet autoantibodies and high-avidity T cells which progressively destroy beta cells causing overt T1D. In particular, novel autoantibodies, such as those against IA-2 epitopes (aa1-577), had a predictive rate of 100% in a 10-year follow up (rapid progressors), unlike conventional autoantibodies that required 15 years of follow up for a 74% predictive rate (slow progressors). The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells. For this purpose, we build a series of mathematical models incorporating first one clone then multiple clones of islet-specific and pathogenic CD8 and/or CD4 T cells, together with B lymphocytes, to investigate the interaction of T-cell avidity with autoantibodies in predicting disease onset. These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects. The model shows that the level and persistence of autoantibodies depends not only on the avidity of T cells, but also on the killing efficacy of these cells. Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies. Such studies may lead to early diagnosis of the disease in high-risk individuals and thus potentially serve as a means of staging patients for clinical trials of preventive or interventional therapies far before disease onset.

Show MeSH
Related in: MedlinePlus