STBnet

Network of the Septoria tritici blotch scientific community

Protocols

Protocols for various manipulations and experiments with Z. tritici in the lab, greenhouse and field will be collected on the website protocols.io under group “Zymoseptoria community protocols (STBnet)”.

You can add you own protocols there by creating an account in protocols.io and joining the STBnet group. You can also comment on protocols of other authors, ask questions and suggest improvements. If you don’t want to create an account but want to support the community, you can send your protocol to petteri.karisto (at) usys.ethz.ch. Petteri will then add the protocol with author attribution.

Photographs

(to be completed)

These photos are provided for use under CC BY -license: they can be used freely mentioning the source of the photo.

Source of the photos below: G.H.J. Kema, Wageningen University & Research

Source of the photos below: B.A. McDonald and P. Karisto, ETH Zurich

Automated image analysis

(to be completed)

Quite a few papers on quantitative resistance of Z. tritici used recently automated image analysis macro of ImageJ for measuring host damage and pathogen reproduction on wheat leaves [for example, 1, 2, 3]. The method was developed by Ethan Stewart in 2014 [4], improved in 2016 [5], and further developed by us in 2018 [3]. We recently added a few unpublished improvements.

Since the introduction of the method, there have been changes in the operating systems, software and packages that the method is dependent on. Furthermore, the method was originally developed with and for MacOS only. For these reasons, people have faced challenges in installation and usage of the method. As we have developed the method further and succeeded in setting it up on Linux and Windows, we decided to put together our knowledge and tips on the installation and usage. Currently, instructions and installation files are available for MacOS (Sierra) and Windows (10). We also provide the most up-to-date version of the macro here.

We hope that these instructions will help the adoption of the method among scientists globally. Please report any issues and/or success in installing and using it, so that we can either help you resolving the issues or celebrate together with you.

This is a link to the folder containing instructions, necessary files, and some sample images for testing. Please cite [3] as the most recent published version, if using the method.

With best wishes, Petteri Karisto and Alexey Mikaberidze

  1. Stewart EL, Croll D, Lendenmann MH, Sanchez‐Vallet A, Hartmann FE, Palma‐Guerrero J, Ma X, Mcdonald BA. Quantitative trait locus mapping reveals complex genetic architecture of quantitative virulence in the wheat pathogen Zymoseptoria tritici. Molecular plant pathology. 2018; 19:201-16. doi:10.1111/mpp.12515

  2. Meile L, Croll D, Brunner PC, Plissonneau C, Hartmann FE, McDonald BA, Sánchez‐Vallet A. A fungal avirulence factor encoded in a highly plastic genomic region triggers partial resistance to septoria tritici blotch. New Phytologist. 2018; 219:1048-1061. doi:10.1111/nph.15180

  3. Karisto P, Hund A, Yu K, Anderegg J, Walter A, Mascher F, McDonald BA, Mikaberidze A. Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis. Phytopathology. 2018; 108:568-81. doi: 10.1094/PHYTO-04-17-0163-R. Open Access in bioRxiv, doi: 10.1101/129353.

  4. Stewart EL, McDonald BA. Measuring quantitative virulence in the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis. Phytopathology. 2014; 104:985-92. doi: 10.1094/PHYTO-11-13-0328-R

  5. Stewart EL, Hagerty CH, Mikaberidze A, Mundt CC, Zhong Z, McDonald BA. An improved method for measuring quantitative resistance to the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis. Phytopathology. 2016; 106:782-8. doi: 10.1094/PHYTO-01-16-0018-R

Unpublished knowledge

Introduction

Continuous reproduction of the same unpublished failures is one of the most deleterious phenomena of research communities. It’s tightly connected to bias towards publishing only positive results. Being careful not to show any mistakes and/or possible future projects and giving hints of one’s research interests may give a secure feeling but will lead to waste of time on community level. On the other hand, as having lots of good ideas is necessary for a good scientist and most of the ideas are not so good, there are always lots of ideas that didn’t work out. However, ideas that didn’t work for you might be viable seeds for thoughts of another scientist and having somebody else to think it may lead to necessary development of the idea.

Let’s begin to be the change everyone is claiming to be important, but nobody is carrying out! Please support the community by releasing your hidden knowledge and resources.

These can include ideas that you never developed further and probably lost your interest, unpublished projects and associated knowledge that you will not work on anymore, failed projects that you don’t want anyone to recreate, possible unpublished data, knockouts, materials and experiences from published projects and so on. When publishing this kind of information try to be as helpful to others as possible. Indicate clearly the most challenging steps, possible weak parts of experimental designs, the failures you made and important things you learned, so that somebody else can learn from your unpublished experience and in best case, develop the project further and complete it with success.

In case of developing abandoned projects further, please contact the original designer of the project and suggest collaboration, to enhance openness, good communication and fruitful collaboration within the community.

Send your experiences to petteri.karisto (at) usys.ethz.ch to have them published here.

(Un)published knowledge

Strain specific primers for two strains (Petteri Karisto)

I created primers for being able to differentiate two (1A5 and 3D7) out of our four lab strains (1A5, 1E4, 3D1 and 3D7). There are four primer pairs specific to each two strains, and the specificity holds within these four strains. The target regions are in different chromosomes shown in the names: 1A5.5 (chromosome five, specific to 1A5), 1A5.6, 1A5.9, 1A5.10, 3D7.2, 3D7.6, 3D7.9, 3D7.10. The primes produce amplicons of size 123bp-146bp and are suitable for qPCR and normal PCR. They can be used in the same PCR-cycler with the same annealing temperature.

Preliminary analysis indicates that some of the primers have reasonable specificity to the target strain even within natural population (eg. 1A5.5: four false positives out of 37 strains from the field). Using them in combination would allow even more specific detection, as all of the primer pairs would amplify the target strain. If you are interested in using them for your project, contact Petteri Karisto.

Effects of light quality on Z.tritici infection (Petteri Karisto)

While studying infection efficiency of Zymo, we noticed that our infections were a lot more successful in one greenhouse chamber than in another. One obvious difference was lighting of the chambers. We tested the idea briefly, by replacing the lights in “the bad chamber” with the lights of “the good chamber” and got much better success. Difference in the infection efficiency between the two chambers/lights was roughly ten-fold.

The good lights are Philips Master TL-D 36W/830, and the bad ones Eye Clean Ace MT400 DL/BH. I found the spectra online (Philips, Eye) and made a little comparison (below). The upper panel is the spectrum of Philips bulbs and the lower Eye’s. Eye bulbs’ spectrum is relatively close to daylight, while Philips bulbs’ spectrum consists of few peaks. Picture In another occasion we had surprisingly low levels in infection in an experiment. We realized that the neighbouring chamber (behind a transparent wall) had those “bad lights” and by accident they were on 24h a day. So, they might have had an effect on our experiment either due to light quality or absence of dark period (usually 8h dark).

Brainstorming about these, we thought that light quality & light period length might have ecological/applied relevance. If certain wavelengths are specifically good/bad for Zymo, then differences in the atmospheric compounds might have a broad effect on the disease prevalence (SO2?). If energy was not a problem, (pulses of) certain type of light could be used for controlling Zymo, a bit like blue LEDs preserving food. And finally, if length of the daylight period matters a lot, it might have consequences for populations in southern/northern Europe.

We are not going to investigate this further, but we hope that this information would be useful for Zymo-people. If you have similar experiences and/or anecdotal evidence of light quality effects, it would be nice to collect them all together here, and try to summarize our knowledge of the best greenhouse conditions for Zymo. Eventually, it would be nice if someone made actual research on this topic.