Analysis of cytokine immune response profile in response to inflammatory stimuli in mice with genetic defects in fetal and adult hemoglobin chain expression

Analysis of cytokine immune response profile in response to inflammatory stimuli in mice with genetic defects in fetal and adult hemoglobin chain expression

Abstract

Injections of a crude fetal sheep liver extract (FSLE) containing fetal hemoglobin, MPLA, and glutathione (GSSH) reversed cytokine changes in aged mice. To investigate the role of fetal hemoglobin we derived mice with homzygous deletions for either of the two major βchains, HgbβmaKO or HgbβmiKO. Hgbβmi is the most prominent fetal Hgbβ chain, with Hgbβma more prominent in adult miceMice lacking another fetal Hgb chain, HgbεKO, died in utero. CHO cells transfected with cloned Hgb chains were used to produce proteins for preparation of rabbit heteroantibodes. Splenocytes from HgbβmaKO mice stimulated in vitro with Conconavalin A showed a higher IL-2:IL-4 ratio than cells from HgbβmiKO mice. Following immunization in vivo with ovalbumin in alum, HgbβmaKO mice produced less IgE than HgbβmiKO mice, suggesting that in the absence of HgbβmiKO mice had a predeliction to heightened allergic-type responses. Using CHO cells transfected with cloned Hgb chains, we found that only the fetal Hgb chain, Hgbε, was secreted at high levels. Secretion of Hgbβma or Hgbβmi chains was seen only after genetic mutation to introduce the two N-linked glycosylation sites present in Hgbε, but absent in the Hgbβ chains. We speculated that a previously unanticipated biological function of a naturally secreted fetal Hgb chain may be partly responsible for the effects reported following injection of animals with fetal, not adult, Hgb. Mice receiving injections of rabbit anti-Hgbε but not either anti-Hgbβma or anti-Hgbβmi from day 14 gestation also showed a bias towards the higher IL-2:IL-4 ratios seen in HgbβmiKO mice.

Authors: Khatri I1, Alexander C2, Brandenburg K2, Chen Z1, Heini A3, Heumann D3, Mach JP4, Mazzoli V1, Rietschel E5, Terskikh A6, Ulmer A2, Yu K1, Zähringer U2, Gorczynski R7.

Influence Factor: 3.815

Citation: Pharmacogenomics J 18, 546-555 (2018).

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