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Browsing by Author "Deb, Devdutta"

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    Application of alignment-free bioinformatics methods to identify an oomycete protein with structural and functional similarity to the bacterial AvrE effector protein
    Deb, Devdutta; Mackey, David; Opiyo, Stephen O.; McDowell, John M. (PLOS, 2018-04-11)
    Diverse plant pathogens export effector proteins to reprogram host cells. One of the most challenging goals in the molecular plant-microbe field is to functionally characterize the complex repertoires of effectors secreted by these pathogens. For bacterial pathogens, the predominant class of effectors is delivered to host cells by Type III secretion. For oomycetes, the predominant class of effectors is defined by a signal peptide that mediates secretion from the oomycete and a conserved RxLR motif. Downy mildew pathogens and Phytophthora species maintain hundreds of candidate RxLR effector genes in their genomes. Although no primary sequence similarity is evident between bacterial Type III effectors (T3Es) and oomycete RXLR effectors, some bacterial and oomycete effectors have convergently evolved to target the same host proteins. Such effectors might have evolved domains that are functionally similar but sequence-unrelated. We reasoned that alignment-free bioinformatics approaches could be useful to identify structural similarities between bacterial and oomycete effectors. To test this approach, we used partial least squares regression, alignment-free bioinformatics methods to identify effector proteins from the genome of the oomycete Hyaloperonospora arabidopsidis that are similar to the well-studied AvrE1 effector from Pseudomonas syringae. This approach identified five RxLR proteins with putative structural similarity to AvrE1. We focused on one, HaRxL23, because it is an experimentally validated effector and it is conserved between distantly related oomycetes. Several experiments indicate that HaRxL23 is functionally similar to AvrE1, including the ability to partially rescue an AvrE1 loss-of-function mutant. This study provides an example of how an alignment-free bioinformatics approach can identify functionally similar effector proteins in the absence of primary sequence similarity. This approach could be useful to identify effectors that have convergently evolved regardless of whether the shared host target is known.
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    Elucidating essential roles of oomycee effector proteins in immune suppression and in targeting hormonal pathways in the host plant
    Deb, Devdutta (Virginia Tech, 2013-09-25)
    Effector proteins are exported to the interior of host cells by numerous plant pathogens. Effector proteins have been well characterized in bacteria. However, the mechanisms through which these effectors promote virulence are largely unknown. Bioinformatic analysis of genome sequences from oomycete pathogens Phytophthora sojae, P. ramorum, P. infestans and Hyaloperonospora arabidopsidis (Hpa) have led to the identification of a large number of candidate effector genes. These effector genes have characteristic motifs (signal peptide, RxLR and dEER) that target the effectors into plant cells. Although these effector genes are very diverse, certain genes are conserved between P. sojae and H. arabidopsidis, suggesting that they play important roles in pathogenicity. The goal of my first project was to characterize a pair of conserved effector candidates from Hpa and P. sojae. We hypothesized that these effectors have important conserved roles with regard to infection. We found that the Hpa effector was expressed early during the course of infection of Arabidopsis and triggered an ecotype-specific defense response in Arabidopsis, suggesting that it was recognized by host surveillance proteins. Both the effectors from Hpa and P. sojae respectively could suppress immunity triggered by pathogen associated molecular patterns (PTI) and by effectors (ETI) in planta. They also enhanced bacterial virulence in Arabidopsis when delivered by the Type III secretion system. Similar results were seen with experiments with transgenic Arabidopsis expressing the effectors. My second project showed that a different Hpa effector protein, HaRxL10, targets the Jasmonate-Zim Domain (JAZ) proteins that repressed responses to the phytohormone jasmonic acid (JA). This manipulation activates a regulatory cascade that reduces accumulation of a second phytohormone, salicylic acid (SA) and thereby attenuates immunity. This virulence mechanism is functionally equivalent to but mechanistically distinct from activation of JA-SA crosstalk by the bacterial JA mimic coronatine. These results reveal a new mechanism underpinning oomycete virulence and demonstrate that the JA-SA crosstalk is an Achilles\' heel that is manipulated by unrelated pathogens through distinct mechanisms.
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    PlantSimLab - a modeling and simulation web tool for plant biologists
    Ha, Sook; Dimitrova, ‪Elena; Hoops, Stefan; Altarawy, Doaa; Ansariola, Mitra; Deb, Devdutta; Glazebrook, Jane; Hillmer, Rachel; Shahin, Hossameldin L.; Katagiri, Fumiaki; McDowell, John M.; Megraw, Molly; Setubal, João C.; Tyler, Brett M.; Laubenbacher, Reinhard C. (2019-10-21)
    Background At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. Results This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. Conclusions Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise.