Liuzzi, Anna Rita, McLaren, James Edward  ORCID: https://orcid.org/0000-0002-7021-5934, Price, David  ORCID: https://orcid.org/0000-0001-9416-2737 and Eberl, Matthias  ORCID: https://orcid.org/0000-0002-9390-5348
      2015.
      
      Early innate responses to pathogens: pattern recognition by unconventional human T-cells.
      Current Opinion in Immunology
      36
      
      , pp. 31-37.
      
      10.1016/j.coi.2015.06.002
    
  
  
         | 
      
| 
            
PDF
 - Published Version
   Available under License Creative Commons Attribution. Download (632kB)  | 
        
Abstract
Although typically viewed as a feature of innate immune responses, microbial pattern recognition is increasingly acknowledged as a function of particular cells nominally categorized within the adaptive immune system. Groundbreaking research over the past three years has shown how unconventional human T-cells carrying invariant or semi-invariant TCRs that are not restricted by classical MHC molecules sense microbial compounds via entirely novel antigen presenting pathways. This review will focus on the innate-like recognition of non-self metabolites by Vγ9/Vδ2 T-cells, mucosal-associated invariant T (MAIT) cells and germline-encoded mycolyl-reactive (GEM) T-cells, with an emphasis on early immune responses in acute infection.
| Item Type: | Article | 
|---|---|
| Date Type: | Publication | 
| Status: | Published | 
| Schools: | Schools > Medicine Research Institutes & Centres > Systems Immunity Research Institute (SIURI)  | 
      
| Subjects: | Q Science > QR Microbiology > QR180 Immunology | 
| Additional Information: | Available online 13 July 2015 | 
| Publisher: | Elsevier | 
| ISSN: | 0952-7915 | 
| Date of First Compliant Deposit: | 30 March 2016 | 
| Last Modified: | 21 Nov 2024 13:26 | 
| URI: | https://orca.cardiff.ac.uk/id/eprint/74724 | 
Citation Data
Cited 26 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
![]()  | 
              Edit Item | 

							



 Altmetric
 Altmetric