Simulation of the dynamics of primary immunodeficiencies in CD4+ T-cells

Abstract

Primary immunodeficiencies (PIDs) form a large and heterogeneous group of mainly rare disorders that affect the immune system. T-cell deficiencies account for about one-tenth of PIDs, most of them being monogenic. Apart from genetic and clinical information, lots of other data are available for PID proteins and genes, including functions and interactions. Thus, it is possible to perform systems biology studies on the effects of PIDs on T-cell physiology and response. To achieve this, we reconstructed a T-cell network model based on literature mining and TPPIN, a previously published core T-cell network, and performed semi-quantitative dynamic network simulations on both normal and T-cell PID failure modes. The results for several loss-of-function PID simulations correspond to results of previously reported molecular studies. The simulations for TCR PTPRC, LCK, ZAP70 and ITK indicate profound changes to numerous proteins in the network. Significant effects were observed also in the BCL10, CARD11, MALT1, NEMO, IKKB and MAP3K14 simulations. No major effects were observed for PIDs that are caused by constitutively active proteins. The T-cell model facilitates the understanding of the underlying dynamics of PID disease processes. The approach confirms previous knowledge about T-cell signaling network and indicates several new important proteins that may be of interest when developing novel diagnosis and therapies to treat immunological defects.

Simulation of the Dynamics of Primary Immunodeficiencies in B Cells

“…Then we constructed reaction equations for the primary BCR activation and response and converted them into a Boolean network model (Table S1 in Supplementary Material). These interactions were manually defined as Boolean equations using the sum-of-product form similar to our previous study on T-cell networks ( 22 ). Proteins were treated as Boolean variables in the hypergraph.…”

“…New PIDs are still being discovered, but due to their large number, rarity and overlapping symptoms, their diagnosis may be late, challenging and costly. Several classifications of PIDs have been introduced ( 9 , 10 ), and candidate genes and proteins have been suggested ( 22 , 62 – 65 ). Proteins that affect several pathways and are captured by the signaling loops are likely involved in PIDs.…”

“…The analyses and simulation protocol were essentially similar to what we used to investigate T-cell PIDs ( 22 ). Briefly, in the Boolean model the nodes represent N signaling molecule or protein variables, X 1 , X 1 , …, X N .…”

“…This study is part of a published doctoral thesis ( 21 ). This study was funded by Vetenskapsrådet and the Alfred Österlunds stiftelse.…”