On May 9, 2023, at 10:00 a.m. CT, Adam Heuberger and Janak Joshi will present Protease Inhibitors from Solanum chacoense Inhibit Pectobacterium Virulence by Reducing Bacterial Protease Activity and Motility—an MPMI Editor’s Pick that investigates how wild potato from South America can tolerate Pectobacterium-caused diseases.

They revealed in a previous study that wild potato produces virulence-reducing metabolites. In the authors’ latest research, they compared the protein profiles of wild and domestic potato, discovering protease inhibitors that also prevent bacterial malignance.

Heuberger comments, “Our finding is the second resistance mechanism we observe in Solanum chacoense M6. This supports the idea that wild plant species have evolved with multiple resistance factors, or their own ‘pyramid’ of traits, that can be translated into our food and ornamental plant industries.”

Hear the authors talk more about this discovery and their goals to breed a generation of potato varieties that resists pathogens durably and independently. Register for the free virtual seminar today!​

Catch Up on Recently Recorded Seminars

Previous seminars are available for viewing. Learn more​.

Thomas Griebel presents “Cold Exposure Memory Reduces Pathogen Susceptibility in Arabidopsis Based on a Functional Plastid Peroxidase System.”

Watch now!

Emma Gachomo presents “Bradyrhizobium japonicum IRAT FA3 Alters Arabidopsis thaliana Root Architecture via Regulation of Auxin Efflux Transporters PIN2, PIN3, PIN7, and ABCB19.”

Watch now!

Two Candidate Meloidogyne javanica Effector Genes, MjShKT and MjPUT3: A Functional Investigation of Their Roles in Regulating Nematode Parasitism

As one of the most destructive plant parasites, Meloidogyne root-knot nematodes infect thousands of plant species. Anil Kumar et al. used transcriptomic data from M. ja​vanica on tomato to identify two candidate M. javanica effectors potentially involved in nematode infection. Functional characterization of these effectors showed that MjShKT is involved in the inhibition of programmed cell death and that MjPUT3 may be involved in modifying root morphology—both of which improve the in planta fitness of the pathogen. This work provides another step toward understanding the important interaction between plants and nematodes.

Comparative Genomic Analysis of 31 Phytophthora Genomes Reveals Genome Plasticity and Horizontal Gene Transfer

Plant pathogens in the genus Phytophthora cause major economic losses globally. Brent Kronmiller and colleagues evaluated the relationships among 31 newly sequenced individual Phytophthora species using comparative genomics and transcriptomics and found variation in effector compliments, as well as predicted genes, that were likely involved in horizontal gene transfer events. This work provides a substantial resource for future studies involving Phytophthora 

Effectors from a Bacterial Vector-Borne Pathogen Exhibit Diverse Subcellular Localization, Expression Profiles, and Manipulation of Plant Defense

To combat the phloem-limited ‘Candidatus Liberibacter solanacearum’ pathogen, Paola Reyes Caldas and colleagues determined its effector repertoire by predicting proteins secreted via the general secretory pathway across four different haplotypes. Additionally, they investigated effector localization in planta and profiled effector expression in the vector and host. Their results reveal that ‘Candidatus Liberibacter solanacearum’ effectors possess complex expression patterns, target diverse host organelles, and the majority are unable to suppress host immune responses. This research opens the door for identifying novel targets of these effectors to manage vector-borne diseases.​

The IS-MPMI Board of Directors (BOD) is accepting nominations for two Directors to join the BOD beginning at the IS-MPMI Congress in July. The term lengths of these positions are four years. Nominations (self-nominations are welcome) for these positions will be​ accepted until May 1. Voting by the general membership will take place before the Congress.

Requirements/duties of Director positions:

• Maintain membership in IS-MPMI.
• Contribute to society leadership, decision making, and Congress planning by participating in monthly or bimonthly BOD video conferences and follow-up communications, averaging less than 2 hours per month.​

Nominations should be sent to IS-MPMI President Roger Innes with “ISMPMI BOD Nomination” in the Subject line. Nominations should include:

• Name
• Current position
• Contact information

All nominees will be asked to submit a one-page statement describing their motivation for serving on the BOD and their vision/priorities for IS-MPMI moving forward.

The Nominations Committee is committed to maintaining a diverse BOD and will take into consideration reasonable distribution of geographic location of all nominees.​

We’re excited for you to join us in Providence, Rhode Island, for five days of programming exploring the latest research in plant–microbe interactions. Network with scientists from around the globe and connect with your peers at our first in-person meeting in four years! Register by May 22 to save with advance registration rates. Register ​today!​

To bot​h refine and broaden the mission of MPMI, Editor-in-Chief Tim Friesen and his editorial board have revised the journal’s overview, scope, and newly acceptable content.

New Overview

Molecular Plant–Microbe Interactions (MPMI) publishes peer-reviewed fundamental and advanced applied research on the genetics, genomics, molecular biology, biochemistry, and biophysics of pathological, symbiotic, and associative interactions of microbes, insects, nematodes, or parasitic plants with plants.
New Scope

MPMI publishes cutting-edge research that investigates the molecular mechanisms of plant interactions with other organisms, including pathogens, pests, and beneficial microbes. The journal communicates novel findings that significantly advance our molecular and cellular understanding of these plant-associated interactions.​
New Acceptable Content for MPMI

• Molecular and cell biological analysis of relevant factors involving the plant-associated organism or the plant alone or molecular analysis of components that affect or modulate plant–microbe interaction
• Genetic analysis (fundamental or applied) that advances knowledge of plant and/or plant-associated organism interactions
• Molecular evolution and molecular ecology of interactions between plants and other organisms
• Comparative genomics of organisms associated with a plant–microbe interaction
• Research addressing molecular aspects of symbiotic and associative relationships with plants
• Spatial and temporal analyses of the cell biology of plant–microbe interactions, including studies focused on light and/or electron microscopy
• Technical advances (methodological), if they report important novel advances in technology for studying the molecular aspects of plant–microbe interactions mentioned above

Consider submitting your next paper to MPMI, the official journal of IS-MPMI. IS-MPMI members receive a discount to publish in MPMI. Beyond gold open access publication, MPMI offers virtual presentation opportunities and an official podcast, Microgreens, to enhance reader engagement worldwide. The journal’s association with two scientific societies (MPMI is also a member journal of The American Phytopathological Society) results in dual marketing efforts, which exposes your work to a wider audience.

If you are unsure whether your manuscript is appropriate for MPMI, you are encouraged to submit a presubmission inquiry to Editor-in-Chief Tim Friesen describing the significance of your findings.

View information for authors here.

A Fruitful Symbiosis Between an Undergrad in Computer Science and a Graduate Student in Genetics and Genomics

Question: What was the inspiration for EffectorO?

 Kelsey: When I started my Ph.D. program on oomycete effector genomics in 2013, it seemed like everyone was really only using the same motifs (RXLR or LFLAK) to predict effectors. But as I dove deeper into the genome of the lettuce downy mildew pathogen, I found that there were some real effectors that did not have these motifs and published a paper on my findings (Wood et al., 2020). That got me thinking of alternative ways to predict effectors.

The first way to predict effectors that I thought of was to leverage lineage specificity, which is a characteristic of many effectors from species with narrow host ranges, to find effectors. I realized this would be pretty simple in principle using BLAST. However, the downside would be that there are other lineage-specific genes besides effectors and a lot of misannotated junk from genomes would probably be picked up. It also would depend a lot on what other organisms are sequenced for comparison.

In my search for a more accurate way to predict effectors, I found a paper on EffectorP 2.0 (Sperschneider et al., 2018) that used machine learning to predict effectors from fungi. I tried to use it on my oomycete genome, but it didn’t work and I was sad.

That is, until I met Munir.

Question: How did you two find each other?

Munir: During my undergraduate studies, I was eager to apply what I’ve learned in computer science and bioinformatics classes to help solve research problems. I saw an ad for a bioinformatics intern in the Michelmore lab and applied by email.

Kelsey: Funny story, I posted a few job ads for undergraduate interns for summer 2017 on the Michelmore lab website, and we didn’t take them down even after the job openings had expired. Munir saw the (old) ad for the bioinformatics intern and emailed me right around the time I was wanting to develop a machine-learning pipeline for oomycete effectors, and I interviewed and hired him on the spot. Moral of the story: never update your lab website. And, if you are an undergrad, don’t wait for an official job ad to reach out to labs for internships!

Question: What did you think bioinformatics research would be like versus what it was actually like?

Munir: Bioinformatics research is much more data wrangling than I thought! I think this is also true for the entire field of data science—it typically takes a lot more work to get the data ready than it does to build models and perform analyses!

Kelsey: What Munir said. And, I’m always surprised at how much you have to reperform the same or similar analysis until it’s “done.” Using R for most of the graphics was a life saver because if something ended up changing we could easily rerun the scripts with the new data.

Question: What lessons did you learn during the preparation of this manuscript?

Munir: Write clean code in a reusable manner the first time around. And if you don’t, definitely get to it the second time you use the same code! Research analyses typically get rerun multiple times, as you’re constantly pulling the latest datasets that get released in the research world or tweaking some parameters to compare different models/hypotheses.

Constantly write lab notes and code documentation, since you will often be looking back at analyses you performed and code you wrote several weeks, months, or years ago.

Kelsey: I learned how valuable reviewer feedback could be, even (or especially?) criticism. One reviewer in particular had a lot of excellent critiques that forced me to rewrite several sections, which resulted in a much clearer argument for the manuscript.

Question: How did reviewers help to improve the manuscript?

Kelsey: One very useful suggestion was to perform domain prediction on our effector candidates using Pfam, which I didn’t think would be a good idea because most effector domains are not well studied. This is what the results ended up showing, but the domains that were found were mostly known effector domains, which helped support the conclusions of the paper. Also, there were many reverse-transcription–related domains that I think also support the conclusions, as it is known that effectors live in transposon-rich regions of the genome. The ones with RT-related domains are probably pseudogenes though, so it is another criteria that one could use to refine the list of candidates.

One reviewer also asked if BLE01, which was the Bremia lactucae effector that we predicted with EffectorO and that we found to be an Avr candidate, was also predicted by EffectorP 3.0 (Sperschneider and Dodds, 2021). We found that it was not predicted by their pipeline. This was important because EffectorP 3.0 came out while our paper was under review. However, this showed that the two machine-learning algorithms predict distinct (but overlapping) sets of proteins and, thus, can be used together for prediction of oomycete effectors. Thank you so much Reviewer #2!

Question: Why was the collaboration between you two especially fruitful?

Kelsey: I brought the biology knowledge, and Munir brought the coding skills. I learned Unix, Perl, and R scripting during grad school, but Munir knew how to code really well in Python, which was essential for this project. He was able to write code very quickly and elegantly and came up with the various evaluation metrics used for the machine-learning models. He also spent a long time working on a convolutional neural network model that was more computationally complex, in the end giving us similar results to the simpler Random Forest classifier we ended up using.

Munir: One of the first things I learned while collaborating with Kelsey was how to effectively digest research papers. My first exercise at the lab was summarizing a collection of research articles relevant to our projects, which was immensely invaluable in teaching me how to look in the right places for information. Kelsey’s research background also played a significant role in coming up with fresh hypotheses and methods to test them, and her plant genetics background allowed us to make better sense of the large amount of data we had.

Also, at the end of the EffectorO project, I got the opportunity to do my first PCR! This was really fun for me to do, as computer scientists and bioinformaticians don’t always have much exposure to the wet lab.

Question: What are you excited to see in future MPMI research?

Kelsey: I’m excited to see how advances in protein structure prediction will expand our knowledge of effectors with uncharacterized protein domains. I’m also excited about high-throughput assays for testing predicted effectors.

Munir: Making machine learning more accessible! I think it would be great to standardize self-service model-building interfaces, since training sets are ever expanding. This would be a way to further improve classifiers like EffectorO whenever new effectors are discovered.

Learn more about Munir and Kelsey in their InterConnections article. ​

References

Sperschneider, J., and Dodds, P. N. 2022. EffectorP 3.0: Prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes. Mol. Plant-Microbe Interact. 35:146-156.

Sperschneider, J., Dodds, P. N., Gardiner, D. M., Singh, K. B., and Taylor, J. M. 2018. Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0. Mol. Plant Pathol. 19:2094-2110.

Wood, K., Nur, M., Gil, J., Fletcher, K., Lakeman, K., et al. 2020. Effector prediction and characterization in the oomycete pathogen Bremia lactucae reveal host-recognized WY domain proteins that lack the canonical RXLR motif. PLOS Pathog. 10:e1009012.

All eligible voting members of IS-MPMI have been sent an electronic ballot to vote in this year’s Board of Directors election. Three of the six candidates (Yang Bai, Patricia Baldrich, Xin Li, Wenbo Ma, Ryohei Terauchi, and Xiufang Xin) will be elected to the Board of Directors to serve a 4-year term. Those elected will be installed during the Board meeting at the 2023 IS-MPMI Congress. Meet your candidates and cast your vote by July 6, 2023 (midnight CDT).​​