Highly immunosuppressive myeloid cells correlate with early relapse after allogeneic stem cell transplantation.

BACKGROUND: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curative treatment for myeloid malignancies such as some acute myeloid leukemias (AML) and high-risk myelodysplastic syndromes (MDS). It aims to eradicate the malignant clone using immunocompetent donor cells (graft-versus-leukemia effect, GVL). Unfortunately, relapse is the primary cause of transplant failure mainly related on HLA loss or downregulation and upregulation of inhibitory ligands on blasts which result in donor immune effector dysfunctions. METHODS: Between 2018 and 2021, we conducted a monocentric prospective study including 61 consecutive patients transplanted for AML or high-risk MDS. We longitudinally investigated immune cells at days + 30, + 90 and + 180 post-transplant from bone marrow and peripheral blood. We assessed the dynamics between myeloid derived suppressor cells (MDSCs) and T-cells. RESULTS: Among the 61 patients, 45 did not relapse over the first 12 months while 16 relapsed during the first year post-transplant. Through months 1 to 6, comparison with healthy donors revealed an heterogenous increase in MDSC frequency. In all recipients, the predominant MDSC subset was granulocytic with no specific phenotypic relapse signature. However, in relapsed patients, in vitro and in vivo functional analyses revealed that MDSCs from peripheral blood were highly immunosuppressive from day + 30 onwards, with an activated NLRP3 inflammasome signature. Only circulating immunosuppressive MDSCs were statistically correlated to circulating double-positive Tim3+LAG3+ exhausted T cells. CONCLUSION: Our simple in vitro functional assay defining MDSC immunosuppressive properties might serve as an early biomarker of relapse and raise the question of new preventive treatments targeting MDSCs in the future. Trial registration NCT03357172.

Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids.

KEY MESSAGE: This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution.

The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought. Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives, including numerous extremophile species. Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences and required single-molecule real-time sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade and a sunflower-specific whole-genome duplication around 29 million years ago. An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs.