Significant IgG subclass heterogeneity in HLA-specific antibodies: Implications for pathogenicity, prognosis, and the rejection response

Lowe, David; Higgins, Rob; Zehnder, Daniel; Briggs, David

doi:10.1016/j.humimm.2013.01.008

Cited by 56 publications

(50 citation statements)

References 14 publications

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“…Similarly to previous studies, 12,23,24,40 noncomplementfixing IgG2 and/or IgG4 DSA alone had a low prevalence in our cohort (4.0%). In contrast, 44.0% of our patients exhibited a mixture of complement-fixing and noncomplement-fixing DSA.…”

Section: Discussionsupporting

confidence: 89%

IgG Donor-Specific Anti-Human HLA Antibody Subclasses and Kidney Allograft Antibody-Mediated Injury

Lefaucheur

Viglietti

Bentlejewski

et al. 2016

Journal of the American Society of Nephrology

243

234

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Antibodies may have different pathogenicities according to IgG subclass. We investigated the association between IgG subclasses of circulating anti-human HLA antibodies and antibody-mediated kidney allograft injury. Among 635 consecutive kidney transplantations performed between 2008 and 2010, we enrolled 125 patients with donorspecific anti-human HLA antibodies (DSA) detected in the first year post-transplant. We assessed DSA characteristics, including specificity, HLA class specificity, mean fluorescence intensity (MFI), C1q-binding, and IgG subclass, and graft injury phenotype at the time of sera evaluation. Overall, 51 (40.8%) patients had acute antibody-mediated rejection (aABMR), 36 (28.8%) patients had subclinical ABMR (sABMR), and 38 (30.4%) patients were ABMR-free. The MFI of the immunodominant DSA (iDSA, the DSA with the highest MFI level) was 67246464, and 41.6% of patients had iDSA showing C1q positivity. The distribution of iDSA IgG1-4 subclasses among the population was 75.2%, 44.0%, 28.0%, and 26.4%, respectively. An unsupervised principal component analysis integrating iDSA IgG subclasses revealed aABMR was mainly driven by IgG3 iDSA, whereas sABMR was driven by IgG4 iDSA. IgG3 iDSA was associated with a shorter time to rejection (P,0.001), increased microcirculation injury (P=0.002), and C4d capillary deposition (P,0.001). IgG4 iDSA was associated with later allograft injury with increased allograft glomerulopathy and interstitial fibrosis/tubular atrophy lesions (P,0.001 for all comparisons). Integrating iDSA HLA class specificity, MFI level, C1q-binding status, and IgG subclasses in a Cox survival model revealed IgG3 iDSA and C1q-binding iDSA were strongly and independently associated with allograft failure. These results suggest IgG iDSA subclasses identify distinct phenotypes of kidney allograft antibody-mediated injury.

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Section: Discussionsupporting

confidence: 89%

IgG Donor-Specific Anti-Human HLA Antibody Subclasses and Kidney Allograft Antibody-Mediated Injury

Lefaucheur

Viglietti

Bentlejewski

et al. 2016

Journal of the American Society of Nephrology

243

234

View full text Add to dashboard Cite

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“…Auto-antibodies and anti-endothelial cell antibodies are often IgG2 and IgG4 (95, 96), while HLA-specific IgG in serum is polyclonal and may be a mix of all four subclasses (4, 25, 26, 33, 97, 98). The current study focused on the effect of IgG1 and IgG2, which are most prevalent in serum, but the functions of IgG3 and IgG4 in this system have yet to be determined.…”

Section: Discussionmentioning

confidence: 99%

Monocyte Recruitment by HLA IgG-Activated Endothelium: The Relationship Between IgG Subclass and FcγRIIa Polymorphisms

Valenzuela

Trinh

Mulder

et al. 2015

American Journal of Transplantation

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It is currently unclear which donor specific HLA antibodies confer the highest risk of antibody-mediated rejection (AMR) and allograft loss. In this study, we hypothesized that two distinct features (HLA IgG subclass and Fcγ receptor (FcγR) polymorphisms), which vary from patient to patient, influence the process of monocyte trafficking to and macrophage accumulation in the allograft during AMR in an interrelated fashion. Here, we investigated the contribution of human IgG subclass and FcγR polymorphisms in monocyte recruitment in vitro by primary human aortic endothelium activated with chimeric anti-HLA I human IgG1 and IgG2. Both subclasses triggered monocyte adhesion to endothelial cells, via a two-step process. First, HLA I crosslinking by antibodies stimulated upregulation of P-selectin on endothelium irrespective of IgG subclass. P-selectin-induced monocyte adhesion was enhanced by secondary interactions of IgG with FcγRs, which was highly dependent upon subclass. IgG1 was more potent than IgG2 through differential engagement of FcγRs. Monocytes homozygous for FcγRIIa-H131 adhered more readily to HLA antibody-activated endothelium compared with FcγRIIa-R131 homozygous. Finally, direct modification of HLA I antibodies with immunomodulatory enzymes EndoS and IdeS dampened recruitment by eliminating antibody-FcγR binding, an approach that may have clinical utility in reducing AMR and other forms of antibody-induced inflammation.

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“…The clinical focus of the model was to investigate how pre-treatment donor specific antibody (DSA) Immunoglobulin G (IgG) subclass levels, measured using Median Fluorescence Intensity (MFI) cytometry techniques, affect early outcome of transplantation when accounted for multiple baseline characteristics (Lowe et al 2013). The following 15 parameters, measured before operation, were considered as input variables for the predictive model:…”

Section: Classification Dt For Prediction Of Early Kidney Rejectionmentioning

confidence: 99%

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

et al. 2015

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Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

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Significant IgG subclass heterogeneity in HLA-specific antibodies: Implications for pathogenicity, prognosis, and the rejection response

Cited by 56 publications

References 14 publications

IgG Donor-Specific Anti-Human HLA Antibody Subclasses and Kidney Allograft Antibody-Mediated Injury

IgG Donor-Specific Anti-Human HLA Antibody Subclasses and Kidney Allograft Antibody-Mediated Injury

Monocyte Recruitment by HLA IgG-Activated Endothelium: The Relationship Between IgG Subclass and FcγRIIa Polymorphisms

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

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