Almost all the nitrogen in soil is locked in complex organic molecules like DNA, proteins and cell walls. Plants can only use inorganic N like ammonium and nitrate. Soil bacteria can mediate between the two sides, degrade organic to inorganic N and, being the great negotiators they are, benefit themselves in the process. This is what this recently published paper is about. The bottom line is that we identified specific bacterial taxa that specialise in degradation of proteins near roots of wild oat, and because wild oat is related to domesticated oat, they may be good candidates for future bioaugmentation to reduce the use of fertilisers. But this is a "behind the paper" blog, and I'd like to spend time talking about the evolution of postdocs, datasets and rejection.
In the winter of 2018 I was a starry eyed recent PhD graduate starting a postdoc under not one but two extremely fabulous scientists, my first female advisors. I was hired to work on a specific dataset generated by tracing stable isotopes into the genomes of soil bacteria. Having worked with metagenomics and stable isotopes during my PhD, I was so excited to combine the two. It was quite shocking to discover that the data didn't exist yet, and I was left lost and stressed, convinced I would be let go any moment. I did the only thing I could think of: invented a side project. It worked out quite nicely, and I got to collaborate with wonderful people who became mentors and friends.
While in the process of writing up that paper, Dr. Erin Nuccio, formerly from the same lab, presented her analysis of a dataset of metatranscriptomes generated through root development. She showed that certain bacteria taxa formed functional guilds that degraded carbohydrates near the roots and near dead roots (see Nuccio et al., 2020, DOI:10.1038/s41396-019-0582-x). Evan Starr, now the lovely Dr. Starr, used the same dataset to show how RNA virus communities change based on roots and root litter (see Starr et al., 2019, DOI:10.1073/pnas.1908291116). It seemed logical to use the same dataset to explore nitrogen cycling in this system, so we did that and submitted the paper back in 2020. I finally felt confident that I justified my pay and earned my keep.
Sadly, it seems that many good researchers still don't understand just how much data is in a set of meta-omics. In the olden days, an experiment would yield one paper. Nowadays, though, an experiment like the one Erin performed yields about a terabyte of data. Writing one paper summarising everything you can conclude from this much data is impossible. Yet, the paper this blog is about was desk rejected from 4 journals because it was claimed to be a "re-analysis" of a published dataset, although there is almost no overlap between the subsets of the data used in previous publications and this one. It's ridiculous to me that editors and reviewers can't seem to fathom that, because huge datasets have been a part of science for many years. Imagine rejecting a paper offering a novel finding based on TARA oceans just because the data from this global expedition spanning multiple size fractions of organisms has been published before. Our system needs to adapt, and it needs to do it fast.
A final word of encouragement to graduate students and postdocs: in academia, things don't always go as planned and positive feedback is scarce. You are all smart and important. Remember to be kind, because you are the next generation of the scientists you look up to now. When you review a paper - acknowledge the effort even if you disagree with some of the science. Room for improvement means that many things were already done well. We can make a difference for each other if we choose empathy over ego.