Biology subjects

Michno, J.-M.

Publications and source records attributed to Michno, J.-M..

5 recordsLinked to original sources

bioRxiv2023

Soybean RIN4 represents a mechanistic link between plant immune and symbiotic signaling

The legume-rhizobium symbiosis represents a unique and beneficial interaction between legumes and nitrogen-fixing soil bacteria, called rhizobia. The initiation and development of this symbiosis is complex and begins with recognition of key molecular signals, produced by the plant and its symbiont, which determine symbiotic compatibility. Current data suggest that the invading symbiont initially triggers plant immune responses that are subsequently suppressed. Hence, there is growing evidence that features of plant immunity may be relevant to symbiotic establishment. RIN4 is a key immune regulator in plants, regulating basal immunity and it is also targeted by pathogen effector proteins that either confer susceptibility or resistance, depending on the presence of the appropriate resistance protein. Surprisingly, we found that RIN4 was rapidly phosphorylated upon rhizobial inoculation of soybean root hairs. RNAi silencing and mutant studies indicate that RIN4 expression is essential for effective nodulation of soybean. RIN4 phosphorylation occurs within a fifteen amino acid motif, which is highly conserved within the Fabales (legumes) and Rosales orders, which comprise species capable of nitrogen-fixing endosymbiosis with rhizobia. RIN4 proteins mutated in this conserved phosphorylation site failed to support efficient soybean nodulation. Phosphorylation of this site is mediated by the symbiotic receptor-like kinase, SymRK, a well-studied member of the symbiotic signaling pathway. The data implicate RIN4 phosphorylation as a key mediator of rhizobial compatibility, interconnecting symbiotic and immune signaling pathways. SignificanceThe nitrogen fixing legume-rhizobium symbiosis is a cornerstone of sustainable agriculture, with ongoing efforts to transfer this unique ability to non-leguminous crop plants. Plants are surrounded by a myriad of microbes in the soil, and, therefore, require constant surveillance in order to distinguish between a pathogen or symbiont. Plants monitor for specific molecular signals that indicate pathogen or symbiont presence. We show that RIN4, a key immune regulator, plays an essential role in promoting the development of the symbiotic nitrogen-fixing relationship between soybean and its compatible symbiont Bradyrhizobium japonicum. Therefore, RIN4 is likely a key player in mediating the appropriate response upon infection by friend or foe.

plant biology↗

bioRxiv2021

Identification of candidate susceptibility genes to Puccinia graminis f. sp. tritici in wheat

Wheat stem rust disease caused by Puccinia graminis f. sp. tritici (Pgt) is a global threat to wheat production. Fast evolving populations of Pgt limit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated with Pgt infection in susceptible and resistant Triticum aestivum genotypes as well as the non-host Brachypodium distachyon. We targeted our analysis to genes with differential expression in T. aestivum and genes suppressed or not affected in B. distachyon and report several processes potentially linked to susceptibility to Pgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance to Pgt through removal or modification of putative susceptibility genes.

plant biology↗

bioRxiv2019

Using multiple reference genomes to identify and resolve annotation inconsistencies

BackgroundAdvances in sequencing technologies have led to the release of reference genomes and annotations for multiple individuals within more well-studied systems. While each of these new genome assemblies shares significant portions of synteny between each other, the annotated structure of gene models within these regions can differ. Of particular concern are split-gene misannotations, in which a single gene is incorrectly annotated as two distinct genes or two genes are incorrectly annotated as a single gene. These misannotations can have major impacts on functional prediction, estimates of expression, and many downstream analyses.\n\nResultsWe developed a high-throughput method based on pairwise comparisons of annotations that detect potential split-gene misannotations and quantifies support for whether the genes should be merged into a single gene model. We demonstrate the utility of our method using gene annotations of three reference genomes from maize (B73, PH207, and W22), a difficult system from an annotation perspective due to the size and complexity of the genome. On average, we find several hundred of these potential split-gene misannotations in each pairwise comparison, corresponding to 3-5% of gene models across annotations. To determine which state (i.e. one gene or multiple genes) is biologically supported, we utilize RNAseq data from 10 tissues throughout development along with a novel metric and simulation framework. The methods we have developed require minimal human interaction and can be applied to future assemblies to aid in annotation efforts.\n\nConclusionsSplit-gene misannotations occur at appreciable frequency in maize annotations. We have developed a method to easily identify and correct these misannotations. Importantly, this method is generic in that it can utilize any type of short-read expression data. Failure to account for split-gene misannotations has serious consequences for biological inference, particularly for expression-based analyses.

bioinformatics↗

bioRxiv2018

Identification of candidate genes underlying nodulation-specific phenotypes in Medicago truncatula through integration of genome-wide association studies and co-expression networks

Genome-wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination near each significant interval. Typically, significant intervals span numerous gene models, limiting the ability to resolve high-confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co-expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co-expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well-defined clusters of genes. In this study, we integrated data from GWAS and co-expression networks to pinpoint candidate genes that may be associated with nodule-related phenotypes in Medicago truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3-like), a previously validated gene associated with nodule number.

systems biology↗

bioRxiv2017

Integrating co-expression networks with GWAS detects genes driving elemental accumulation in maize seeds

BackgroundGenome wide association studies (GWAS) have identified thousands of loci linked to hundreds of traits in many different species. However, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in non-human, non-model species where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes.\n\nResultsTo address this issue, we developed a computational approach called Camoco (Co-Analysis of Molecular Components) that systematically integrates loci identified by GWAS with gene co-expression networks to prioritize putative causal genes. We applied Camoco to prioritize candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize seeds. Camoco identified statistically significant subnetworks for the majority of traits examined, producing a prioritized list of high-confidence causal genes for several agronomically important maize traits. Two candidate genes identified by our approach were validated through analysis of mutant phenotypes. Strikingly, we observed a strong dependence in the performance of our approach on the type of co-expression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, maize roots) outperformed other alternatives.\n\nConclusionsOur study demonstrates that co-expression networks can provide a powerful basis for prioritizing candidate causal genes from GWAS loci, but suggests that the success of such strategies can highly depend on the gene expression data context. Both the Camoco software and the lessons on integrating GWAS data with co-expression networks generalize to species beyond maize.

systems biology↗