Written by John-Paul Wilkins. This is the report from a 2020 BSPP Lockdown Bursary. Click here to read more/apply for one yourself.
Spotting the BSPP summer lockdown bursary advert allowed me to contact a lecturer at my home university, Queen’s University Belfast. The Dalzell laboratory is conducting a project exploring the impact of temperature on crop-rhizosphere interactions. High quality transcriptomic datasets had been generated, demonstrating differential expression of microRNAs and their predicted target genes at different temperatures. These changes correlate with significantly different responses of several species of plant parasitic nematodes, as well as pathogenic microbes, to root exudates. We were able to agree upon a project investigating RNA interactions and their implications for tomato crop health.
I completed my 2nd year of a Biological Sciences degree last summer. I am particularly interested in molecular genetics and had learned about the many advantages of studying plants as a model for future insights into these processes. Having spent the previous summer working on grass crop field trials at the Agri-Food and Biosciences Institute in Belfast, helping crop breeders develop and maintain their products, I was keen to get some experience from the academic side. Unfortunately, all undergraduate projects were cancelled due to Covid-19, and the summer was looking unusually empty. Luckily, the BSPP came to the rescue just as restrictions eased slightly and my supervisor was allowed to return to the lab.
MicroRNAs (miRNAs) in plants act to regulate plant gene expression. The most recent tomato genome assembly, SL4.0, identified several thousand predicted long non-coding RNAs (lncRNAs) for the first time. These lncRNAs are hypothesised to function by acting as decoys. Instead of miRNAs cutting complementary sections of messenger RNA (mRNA) targets, they cut the lncRNAs instead. This alleviates miRNA regulation of validated mRNA targets. I was to use the Cleaveland software package to analyse a specific subset of the transcriptomic datasets, called the degradome. The software identifies evidence of miRNA interactions with other RNAs and statistically analyses their significance.
The software package is extremely resource intensive, but though the University was out of bounds to all, I was able to connect to the high-performance computing cluster remotely to analyse the reads. I learned to curate the output and use a variety of bioinformatic packages to provide annotation on known functions of genes, miRNAs or functional groups. Differential expression analysis was performed using R. But, though this evidence was useful, it was the confirmation of interactions provided by the degradome analysis which was the real aim. My analysis identified 19 new lncRNAs, with confirmed miRNA interactions. 7 of these were part of triads, in which the same miRNA was confirmed as interacting with both an lncRNA and an mRNA. Several hundred mRNA-miRNA interactions were confirmed. Many of the genes being regulated are involved in the seedling’s immune system response: disease resistance proteins, ethylene response factors, pentatricopeptide and leucine-rich repeats.
The second half of the project examined the degradome for interactions between trans-acting small interfering RNAs (tasiRNAs) with mRNAs or lncRNAs. tasiRNAs are long transcripts which are chopped into small pieces in the cytoplasm, when they can act like miRNAs. The analysis was performed similarly to the miRNA research but in a reassuringly shorter amount of time! The analysis showed a significant role for tasiRNAs in regulating genes in response to temperature changes.
I would like to thank the BSPP for providing me with this opportunity to work with the Dalzell laboratory and gain an insight into what cutting edge scientific research entails. Having only limited experience in coding, there were long periods of frustration as I trawled the internet to try to overcome the seemingly endless errors I generated. But things did become easier and this BSPP funded project has encouraged me to believe that I can make a contribution in this area. Special thanks go to PhD student Brian Reilly for teaching me the methods, and providing just the right amount of guidance.
John-Paul Wilkins
Queen’s University Belfast
