Medicago truncatula quantitative resistance to a new strain of Verticillium alfalfae from Iran revealed by a genome-wide association study.

Verticillium wilt is a major threat to many crops, among them alfalfa (Medicago sativa). The model plant Medicago truncatula, a close relative of alfalfa was used to study the genetic control of resistance towards a new Verticillium alfalfae isolate. The accidental introduction of pathogen strains through global trade is a threat to crop production and such new strains might also be better adapted to global warming. Isolates of V. alfalfae were obtained from alfalfa fields in Iran and characterized. The Iranian isolate AF1 was used in a genome-wide association study (GWAS) involving 242 accessions from the Mediterranean region. Root inoculations were performed with conidia at 25°C and symptoms were scored regularly. Maximum Symptom Score and Area under Disease Progess Curve were computed as phenotypic traits to be used in GWAS and for comparison to a previous study with French isolate V31.2 at 20°C. This comparison showed high correlation with a shift to higher susceptibility, and similar geographical distribution of resistant and susceptible accessions to AF1 at 25°C, with resistant accessions mainly in the western part. GWAS revealed 30 significant SNPs linked to resistance towards isolate AF1. None of them were common to the previous study with isolate V31.2 at 20°C. To confirm these loci, the expression of nine underlying genes was studied. All genes were induced in roots following inoculation, in susceptible and resistant plants. However, in resistant plants induction was higher and lasted longer. Taken together, the use of a new pathogen strain and a shift in temperature revealed a completely different genetic control compared to a previous study that demonstrated the existence of two major QTLs. These results can be useful for Medicago breeding programs to obtain varieties better adapted to future conditions.

Temperature increase modifies susceptibility to Verticillium wilt in Medicago spp and may contribute to the emergence of more aggressive pathogenic strains.

Global warming is expected to have a direct impact on plant disease patterns in agro-eco-systems. However, few analyses report the effect of moderate temperature increase on disease severity due to soil-borne pathogens. For legumes, modifications of root plant-microbe interactions either mutualistic or pathogenic due to climate change may have dramatic effects. We investigated the effect of increasing temperature on the quantitative disease resistance to Verticillium spp., a major soil-borne fungal pathogen, in the model legume Medicago truncatula and the crop M. sativa. First, twelve pathogenic strains isolated from various geographical origin were characterized with regard to their in vitro growth and pathogenicity at 20°C, 25°C and 28°C. Most of them exhibited 25°C as the optimum temperature for in vitro parameters, and between 20°C and 25°C for pathogenicity. Second, a V. alfalfae strain was adapted to the higher temperature by experimental evolution, i.e. three rounds of UV mutagenesis and selection for pathogenicity at 28°C on a susceptible M. truncatula genotype. Inoculation of monospore isolates of these mutants on resistant and susceptible M. truncatula accessions revealed that at 28°C they were all more aggressive than the wild type strain, and that some had acquired the ability to cause disease on resistant genotype. Third, one mutant strain was selected for further studies of the effect of temperature increase on the response of M. truncatula and M. sativa (cultivated alfalfa). The response of seven contrasted M. truncatula genotypes and three alfalfa varieties to root inoculation was followed using disease severity and plant colonization, at 20°C, 25°C and 28°C. With increasing temperature, some lines switched from resistant (no symptoms, no fungus in the tissues) to tolerant (no symptoms but fungal growth into the tissues) phenotypes, or from partially resistant to susceptible. Further studies in greenhouse evidence the reduction in plant fitness due to disease in susceptible lines. We thus report that root pathogenic interactions are affected by anticipated global warming, with trends towards increased plant susceptibility and larger virulence for hot-adapted strains. New threats due to hot-adapted strains of soil-borne pathogens, with possibly wider host range and increased aggressiveness, might occur.

Integrated sRNA-seq and RNA-seq Analyses Reveal a microRNA Regulation Network Involved in Cold Response in Pisum sativum L.

(1) Background: Cold stress affects growth and development in plants and is a major environmental factor that decreases productivity. Over the past two decades, the advent of next generation sequencing (NGS) technologies has opened new opportunities to understand the molecular bases of stress resistance by enabling the detection of weakly expressed transcripts and the identification of regulatory RNAs of gene expression, including microRNAs (miRNAs). (2) Methods: In this study, we performed time series sRNA and mRNA sequencing experiments on two pea (Pisum sativum L., Ps) lines, Champagne frost-tolerant and Térèse frost-sensitive, during a low temperature treatment versus a control condition. (3) Results: An integrative analysis led to the identification of 136 miRNAs and a regulation network composed of 39 miRNA/mRNA target pairs with discordant expression patterns. (4) Conclusions: Our findings indicate that the cold response in pea involves 11 miRNA families as well as their target genes related to antioxidative and multi-stress defense mechanisms and cell wall biosynthesis.

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants.

The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method's prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.