Eomes-dependent mitochondrial regulation promotes survival of pathogenic CD4+ T cells during inflammation.

The mechanisms whereby Eomes controls tissue accumulation of T cells and strengthens inflammation remain ill-defined. Here, we show that Eomes deletion in antigen-specific CD4+ T cells is sufficient to protect against central nervous system (CNS) inflammation. While Eomes is dispensable for the initial priming of CD4+ T cells, it is required for long-term maintenance of CNS-infiltrating CD4+ T cells. We reveal that the impact of Eomes on effector CD4+ T cell longevity is associated with sustained expression of multiple genes involved in mitochondrial organization and functions. Accordingly, epigenetic studies demonstrate that Eomes supports mitochondrial function by direct binding to either metabolism-associated genes or mitochondrial transcriptional modulators. Besides, the significance of these findings was confirmed in CD4+ T cells from healthy donors and multiple sclerosis patients. Together, our data reveal a new mechanism by which Eomes promotes severity and chronicity of inflammation via the enhancement of CD4+ T cell mitochondrial functions and resistance to stress-induced cell death.

Sunflower Hybrids and Inbred Lines Adopt Different Physiological Strategies and Proteome Responses to Cope with Water Deficit.

Sunflower is a hybrid crop that is considered moderately drought-tolerant and adapted to new cropping systems required for the agro-ecological transition. Here, we studied the impact of hybridity status (hybrids vs. inbred lines) on the responses to drought at the molecular and eco-physiological level exploiting publicly available datasets. Eco-physiological traits and leaf proteomes were measured in eight inbred lines and their sixteen hybrids grown in the high-throughput phenotyping platform Phenotoul-Heliaphen. Hybrids and parental lines showed different growth strategies: hybrids grew faster in the absence of water constraint and arrested their growth more abruptly than inbred lines when subjected to water deficit. We identified 471 differentially accumulated proteins, of which 256 were regulated by drought. The amplitude of up- and downregulations was greater in hybrids than in inbred lines. Our results show that hybrids respond more strongly to water deficit at the molecular and eco-physiological levels. Because of presence/absence polymorphism, hybrids potentially contain more genes than their parental inbred lines. We propose that detrimental homozygous mutations and the lower number of genes in inbred lines lead to a constitutive defense mechanism that may explain the lower growth of inbred lines under well-watered conditions and their lower reactivity to water deficit.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure.

Introduction: The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction. Methods: The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort. We used unsupervised and supervised machine learning to classify patients into homogenous UP clusters and to predict 3-year KF risk with UP, respectively. The predictive performance of UP was compared with the KF risk equation (KFRE), and evaluated in an external cohort of 326 patients. Results: More than 1000 peptides classified patients into 3 clusters with different CKD severities and etiologies at baseline. Peptides with the highest discriminative power for clustering were fragments of proteins involved in inflammation and fibrosis, highlighting those derived from α-1-antitrypsin, a major acute phase protein with anti-inflammatory and antiapoptotic properties, as the most significant. We then identified a set of 90 urinary peptides that predicted KF with a c-index of 0.83 (95% confidence interval [CI]: 0.81-0.85) in the case-cohort and 0.89 (0.83-0.94) in the external cohort, which were close to that estimated with the KFRE (0.85 [0.83-0.87]). Combination of UP with KFRE variables did not further improve prediction. Conclusion: This study shows the potential of UP analysis to uncover new pathophysiological CKD progression pathways and to predict KF risk with a performance equal to that of the KFRE.

Long-Read Genome Sequence of the Sugar Beet Rhizosphere Mycoparasite Pythium oligandrum.

Pythium oligandrum is a soil born free living oomycete able to parasitize fungi and oomycetes prey, including important plant and animals pathogens. Pythium oligandrum can colonize endophytically the root tissues of diverse plants where it induces plant defenses. Here we report the first long-read genome sequencing of a P. oligandrum strain sequenced by PacBio technology. Sequencing of genomic DNA loaded onto six SMRT cells permitted the acquisition of 913,728 total reads resulting in 112X genome coverage. The assembly and polishing of the genome sequence yielded180 contigs (N50 = 1.3 Mb; L50 = 12). The size of the genome assembly is 41.9 Mb with a longest contig of 2.7 Mb and 15,007 predicted protein-coding genes among which 95.25% were supported by RNAseq data, thus constituting a new Pythium genome reference. This data will facilitate genomic comparisons of Pythium species that are commensal, beneficial or pathogenic on plant, or parasitic on fungi and oomycete to identify key genetic determinants underpinning their diverse lifestyles. In addition comparison with plant pathogenic or zoopathogenic species will illuminate genomic adaptations for pathogenesis toward widely diverse hosts.

A Genomic Map of Climate Adaptation in Arabidopsis thaliana at a Micro-Geographic Scale.

Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome-environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances.