Life used to be so easy: I stared down my microscope, named the diatoms I could see, counted them and, from these data, made an evaluation of the quality of the ecosystem that I was studying. Along with the majority of my fellow diatomists, I conveniently ignored the fact that I was looking at dead cell walls rather than living organisms. My work on molecular barcodes as an alternative to traditional microscopy has been revelatory as I try to reconcile these two types of data. At one level, what I see down the microscope is a benchmark for what I should expect to see in my barcode output. Yet, at the same time, the differences between the two types of data show up the limitations of traditional data – and the assumptions that underpin the ways that we work.
Take a look at the plate below which shows two of the most common diatoms in UK rivers: Ulnaria ulna is one of the largest that I encounter regularly whilst Achnanthidium minutissimum is often one of the most abundant in my samples, particularly when the level of human pressure is relatively low. When we analyse samples with the light microscope, we record individuals, so both of these score “1” in my data book despite the fact that U. ulna is about 100x larger (by volume) than A. minutissimum.
Specimens of Ulnaria ulna (top) and Achnanthidium minutissimum (bottom). Both are from cultures used for obtaining sequences for the reference library for our molecular barcoding project. Scale bar: 10 µm. Photographs: Shinya Sato, Royal Botanic Gardens, Edinburgh.
When we analyse a sample using Next Generation Sequencing (NGS), we count not cell walls but copies of the rbcL gene, which provides the blueprint for Rubisco, a key photosynthetic enzyme. As I write, there is no clear understanding of how the number of rbcL copies relates to the number of individuals. We know that each chloroplast within a cell will have at least one copy of this gene, and usually several. There is also some evidence that larger chloroplasts have more copies of the gene than smaller ones and there is also likely to be a measure of environmental control. The key message that I try to get across in my talks is that NGS data are different to the data we are used to gathering using microscopy. These differences do not mean that it is wrong, just that we need to leave some of our preconceptions before starting to interpret this new type of data.
However, we could also argue that counting the number of copies of the gene for an important photosynthesis enzyme should be giving us a better insight into the contribution of a species to primary productivity than counting the number of cell walls. In other words (whisper this …), rbcL might not just be different, it might be better, especially if our purpose is to understand the contribution the various species in the biofilm make to primary productivity in stream ecosystem. At the moment there are plenty of problems with the NGS-based method, not least the fact that we often cannot assign half the copies of the rbcL gene in a sample to a species, but the situation is improving all the time …
Some recent work pushes this a little further. Jodi Young and colleagues at Princeton University have demonstrated large variation in the kinetics of Rubisco in diatoms, and in their carbon-concentrating mechanisms (see “Concentrating on carbon …” for more about these). Although their work is focussed on marine phytoplankton, the variation within Rubisco and carbonic anhydrases could go some way to explaining the sensitivity of diatoms to inorganic carbon (see “Ecology in the Hard Rock Café …”). In other words, rbcL is not an irrelevant DNA sequence, as the term “barcode” may imply (in contrast to barcodes based on the ITS region, for example), it is deeply implicated in the reasons why a species lives in particular place.
And yet, and yet, and yet … The same could be argued for morphology, up to a point at least. The shape of a Gomphonema or a Navicula also helps us to understand the organism’s relationship with its environment. The problem is that modern taxonomists tend to focus on a much finer level of detail – on the arrangement and structure of the various pores on the silica frustule, for example – and offer few insights into what these minute differences mean in terms of the ecophysiology of the organisms. Even at the whole-cell scale, information on habit, which is linked to form (Gomphonema tending to live on stalks or short mucilage pads secreted from their foot poles for at least part of their life-cycle, for example) is rarely incorporated into assessment systems. The move from using light microscopy to using NGS, in other words, means replacing an imperfect system with which we are familiar with one that we are still learning to understand. Both offer unique information and the gains from using one approach rather than the other, will be offset by losses of insight.
That leaves us with two big challenges over the couple of years, as UK diatom-based assessments move from light microscopy to NGS. The first is to work harder to understand what NGS outputs are actually telling us about the environment over and above the minimalist ecological status indices that spew out of our “black box” computer programs. The second is to maintain an understanding of the properties of whole organisms and how these interact with one another and with their environments. I guess I should add a third challenge to this pair: persuading middle managers who have at best a sketchy understanding of diatoms and phytobenthos and already-stretched budgets that any of this matters …
Badger, M.R. & Price, G.D. (2003). The role of carbonic anhydrase in photosynthesis. Annual Review of Plant Biology 45: 369-392.
Young, J.N. & Hopkinson, B.M.M. (2017). The potential for co-evolution of CO2-concentrating mechanisms and Rubisco in diatoms. Journal of Experimental Botany doi: 10.1093/jxb/erx130.
Young, J.N., Heureux, A.M.C., Sharwood, R.E., Rickaby, R.E.M., Morel, F.M.M. & Whitney, S.M. (2016). Large variations in the Rubisco kinetics of diatoms reveals diversity among their carbon-concentrating mechanisms. Journal of Experimental Botany 67: 3445-3456.