Identification and characterization of specific motifs in effector proteins of plant parasites using MOnSTER
Authors: Calia Giulia , Porracciolo Paola , Chen Yongpan , Kozlowski Djampa , Schuler Hannes , Cestaro Alessandro , Quentin Michaël , Favery Bruno , Danchin Etienne G.J. , Bottini Silvia 
Published: July 2023

AbstractPlant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Identifying and characterizing pathogens effectors is crucial towards their improved control. Because of their poor sequence conservation, effector identification in protein sequences predicted from genomes is challenging and current methods generate too many candidates without indication for prioritizing further experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Although bacterial, fungal and oomycetes effectors have been studied extensively and conserved characteristic motifs have been identified, research on plant-parasitic nematode effectors (PPN) identified some enriched degenerate motifs in only one species so far. The different lifestyles of PPNs might reflect effectors with different functions according to the nematode’s specific needs, thus presenting a high variety of characteristic motifs.To circumvent these limitations, we have developed MOnSTER a novel tool that identifiesclusters ofmotifs ofproteinsequences (CLUMPs). MOnSTER can be fed with motifs identified byde novotools or from databases such as Pfam and InterProScan. The advantage of MOnSTER is the reduction of motif redundancy by clustering them and associating a score. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in candidate parasitism proteins of plant-pathogenic oomycetes. We showed the reliability of MOnSTER by identifying five CLUMPs that correspond to the known motifs: RxLR, -dEER and LxLFLAK-HVLVxxP. Consequently, we applied MOnSTER on PPN candidate parasitism proteins and identified peculiar motifs in their sequences. We identified six CLUMPs in about 60% of the known nematode candidate parasitism proteins. Furthermore, we found that specific co-occurrences of at least two CLUMPs are present in PPN candidate parasitism protein sequences bearing protein domains important for invasion and pathogenicity.The potentiality of this tool goes beyond the candidate parasitism proteins and can be used to easily cluster motifs and calculate the CLUMP-score on any set of protein sequences.Authors summaryPopulation growth, environmental degradation and climate change are already bringing harm to human communities and the natural world that needs to be addressed rapidly. Ensuring food security for a population that will exceed 9 billion people by 2050 while preserving the environment and biodiversity is a major challenge. Agricultural pathogens, to cause the infection, secrete effector proteins that promote colonization of the host plant. Identifying and characterizing pathogens’ effectors is crucial towards understanding how they manipulate the plant and better combat them. Because of their poor sequence conservation, effector identification in protein sequences predicted from genomes is challenging and current methods generate too many candidates without indication for prioritizing further experimental studies. To address these challenges, we have developed a novel tool called MOnSTER, that identifies and scoreclusters ofmotifs ofproteinsequences (CLUMPs). MOnSTER is an easy to use tool that can be included in any pipeline needing motif calling and will be of great use to accelerate both computational and experimental studies relating to protein motif discovery. Altogether our findings provide improvements in the understanding of the mechanisms set up by the pathogens to infect the plant and can elucidate important signatures to block the development of plant-pathogen interactions and allow to engineer of durable disease resistance.