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Parasitic helminth infections in humans modulate Trefoil Factor levels in a manner dependent on the species of parasite and age of the host.

Babatunde AdewaleJonathan R HeintzChristopher F PastoreHeather Lynn RossiLi-Yin HungNurudeen O RahmanJeff BethonyDavid J DiemertAyorinde Babatunde JamesDe'Broski R Herbert
Published in: PLoS neglected tropical diseases (2021)
Helminth infections, including hookworms and Schistosomes, can cause severe disability and death. Infection management and control would benefit from identification of biomarkers for early detection and prognosis. While animal models suggest that Trefoil Factor Family proteins (TFF2 and TFF3) and interleukin-33 (IL-33) -driven type 2 immune responses are critical mediators of tissue repair and worm clearance in the context of hookworm infection, very little is known about how they are modulated in the context of human helminth infection. We measured TFF2, TFF3, and IL-33 levels in serum from patients in Brazil infected with Hookworm and/or Schistosomes, and compared them to endemic and non-endemic controls. TFF2 was specifically elevated by Hookworm infection in females, not Schistosoma or co-infection. This elevation was correlated with age, but not worm burden. TFF3 was elevated by Schistosoma infection and found to be generally higher in females. IL-33 was not significantly altered by infection. To determine if this might apply more broadly to other species or regions, we measured TFFs and cytokine levels (IFNγ, TNFα, IL-33, IL-13, IL-1β, IL-17A, IL-22, and IL-10) in both the serum and urine of Nigerian school children infected with S. haematobium. We found that serum levels of TFF2 and 3 were reduced by infection, likely in an age dependent manner. In the serum, only IL-10 and IL-13 were significantly increased, while in urine IFN-γ, TNF-α, IL-13, IL-1β, IL-22, and IL-10 were significantly increased in by infection. Taken together, these data support a role for TFF proteins in human helminth infection.
Keyphrases
  • immune response
  • rheumatoid arthritis
  • endothelial cells
  • machine learning
  • inflammatory response
  • prognostic factors
  • risk factors