Kidney Transplantation - Functional ImmunoGenomics

Project Information

KiT-FIG

Grant agreement ID: 846520

DOI

10.3030/846520

Project closed

EC signature date 5 April 2019

Start date 1 July 2019

End date 30 June 2021

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 196 707,84

EU contribution

€ 196 707,84

Coordinated by

NANTES UNIVERSITE
France

Periodic Reporting for period 1 - KiT-FIG (Kidney Transplantation - Functional ImmunoGenomics)

Reporting period: 2019-07-01 to 2021-06-30

Summary of the context and overall objectives of the project

Chronic renal failure affects ~10% of the world population and can progressively lead to end-stage renal disease (ESRD) requiring replacement therapy (dialysis or transplantation). Kidney transplantation is the best treatment for ESRD. The one-year survival of kidney transplant is now 90% and the graft half-life is about 10 years. However, despite these enormous progresses of immunosuppressive treatments, the host immune response against the graft still leads to rejection in many cases. This public health issue is critical considering that organs are a rare resource. Overall, the triggers leading to rejection are still largely unknown and need to be further studied.
The HLA (human leukocyte antigen) molecule is the immunodominant antigen for both humoral and cellular alloreactivity. The number of HLA allelic differences between donor and recipient (mismatches) is strongly linked to graft survival, hence HLA compatibility between donor and recipient is essential. Nevertheless, mismatches do not explain all rejections and the intrinsic effect of HLA alleles, beyond these mismatches, remains elusive. Altogether, I hypothesize that HLA plays a major role in post-renal transplant complications and graft dysfunction, beyond HLA mismatches. Exploring the HLA diversity and complexity (alleles, haplotypes), but also aspects of regulation/expression and KIR receptors is definitely a present-day research project and will allow us to better understand cellular and humoral rejections and provide new insights in transplanted patients management and graft allocation for example by selecting potential less immunogenic HLA alleles.
The Kidney Transplantation - Functional ImmunoGenomics (KiT-FIG) project was designed to answer 2 main challenges: 1) to develop innovative methods of HLA statistical inference to gain precision and generate additional immunogenomic; 2) to apply these novel biostatistical and bioinformatic methods to the different immunological outcomes related to renal transplantation (chronic rejection, tolerance) to improve graft survival and patient management.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The Kit-FIG project successfully provided major results published in peer review papers publication (Vince et al., Gen Ep, 2020; Vince et al., JACI, 2020; Geffard et al., Bioinf., 2020; Sayadi et al., AICCSA, 2020; Sayadi et al., AINA, 2021), scientific presentation in conferences (8 oral presentations and 7 posters at European Federation of immunogenetics, American Society of Human Genetics, MCAA) and general public outreach (Nantes Utopiales, Researchers’ night).
Specifically, KiT-FIG ensured major advances in HLA imputation. Though HLA imputation methods exist, no unified effort has yet been undertaken to share large and diverse imputation models, or to improve methods. By generating unique reference panels, we highlighted the importance of (a) the number of individuals in reference panels, with a twofold increase in accuracy (from 10 to 100 individuals) and (b) the number of SNPs, with a 1.5‐fold increase in accuracy (from 500 to 24,504 SNPs). Building on these results, we created the SNP‐HLA Reference Consortium (SHLARC) to gather data, enhance HLA imputation and broaden access to highly accurate imputation models for the immunogenomics community. This consortium is funded by a grant obtained from the University of Nantes (NExT project, 400k€, PI: Nicolas Vince) and gather 32 teams across 16 countries.
To go beyond HLA allelic data and provide better biologically relevant in silico functional immunogenomic parameters, we built Easy-HLA (hla.univ-nantes.fr) a web-based software suite designed to facilitate analysis and gain knowledge from HLA typing. Easy-HLA implements a computational and statistical method of HLA haplotypes inference based on published reference populations containing over 600,000 haplotypes. Easy-HLA is freely accessible to all, and we already count 300 users worldwide so far.
With our specific bioinformatic tools we studied the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) as a practical example. We ran the first HLA-centric association study with asthma and specific asthma related phenotypes in this large cohort. We showed that HLA-DRB1*09:01 was associated with increased tIgE levels (P=8.5x10-4 weighted effect size 0.51 [0.15-0.87]). Our study emphasizes that by leveraging powerful computational machine-learning methods, specific/extreme phenotypes, and population diversity, we can explore HLA gene polymorphisms in depth and reveal the full extent of complex disease associations.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In addition to the already published results, we submitted several papers related to the KiT-FIG project. Two supplementary tools already available within Easy-HLA (EasyMatch-R and HLA-Epi) are under peer review process. They are designed to further complement our immunogenomic exploration of kidney transplantation. In parallel, we wrote a review including 2 present-day HLA related topics. Indeed, the vast and rapid spread of the SARS-CoV-2 virus during the current pandemic has triggered numerous efforts to better understand the interactions between host genetics and the severity of COVID-19. We reviewed the latest literature about HLA association with COVID-19 published in this past year of worldwide sanitary crisis. Some major methodological flaws made us realize the genomic community may need strong guidelines to perform robust genetic association between traits and HLA data (SNPs and/or alleles). In our manuscript, we therefore expose the important analytical steps for HLA immunogenetics.
Even if we made important progress on the technical part, we were not immune to the global health crisis. The kidney transplanted patients suffered a high burden due to the SARS-CoV-2 which slowed down samples collection. Hence, the progress on kidney transplantation immunogenomic exploration underwent some genotyping delays. However, now the genetic data for 2300 donor/recipient pairs are available and we expect to get the results published in 2022.
Overall, the KiT-FIG project was very successful in many aspects: first, it allowed me to secure a permanent research position at the French health and medical research institute (INSERM); second, it seeded the development of immunogenomic projects in my institution; third, it became an anchor to obtain additional funding for these projects. The management of this project by mentoring 2 PhD students (one defended in December 2020), by applying to grants (650k€ obtained as PI) and by building an international consortium (SHLARC) contributed to my professional maturity and to my success in the 2021 INSERM competitive recruitment. The impact on the scientific community extends beyond the open access published results, Easy-HLA (hla.univ-nantes.fr) is freely accessible to the public and we plan to grow this website by adding HLA imputation algorithm from the SHLARC project. Results from the kidney transplantation immunogenomic study are expected to greatly advance our understanding of the mechanisms by which HLA modulates renal transplantation outcomes. This will have implications in patients and treatment management and may permit to reduce organ shortage. Finally, KiT-FIG became a stepping stone from which diverse scientific projects will emerge beyond kidney transplantation towards immune related diseases and traits.

The SNP-HLA Reference Consortium (SHLARC) design.

Easy-HLA software presentation.