The next 9 blog posts will summarize my reading assignments for the EBIO 3rd semester exam. The exam is scheduled for 3 hours and involves my four committee members asking me questions about anything at all! I was required to put together a reading list covering 4 main topics: biological soil crusts & drylands, microbial ecology, ecosystem services, and community, restoration, and disturbance-succession ecology. Obviously, I actually have 7 topics, which I managed to squeeze into "4". The reading list is a guide for the exam. To help me through this exam preparation process, I will use these blogs to summarize what I am learning over the next 9 weeks.
My readings this week were fun! We are now in the microbial ecology section of the Reading List. I probably spent more time reading papers linked to the ones on my list than I should have, but I assume that exploring additional works is part of the process. My advisor for this section said I should develop a good understanding of microbiology and soil microbial ecology which includes reviewing nutrient cycles and dominant soil taxa (who they are and what they do). I should also understand the limitations of this knowledge. There are several layers to these questions. First, broadly understanding the most common soil microorganisms. Second, drylands are likely different from the global average, so I should know how arid places are different. Third, I should understand how biological soil crusts are different from a typical desert soil. Starting with the broadest question, Janssen (2006) found in their survey of global soils (16S rRNA sequencing) that dominant phyla include Proteobacteria (40%), Acidobacteria (20%), Actinobacteria (12%), Verrucomicrobia (8%), with Bacteroidetes, Chloroflexi, Planctomycetes, Gemmatimonadetes, and Firmicutes making up the rest. In a different study also using 16S rRNA genes across many different biomes (Fierer et al. 2012), dominant taxa were similar. Archaea are relatively rare in soils. The Fierer paper notes that Cyanobacteria and Archaea increase in desert soils. Maestre et al. (2015) note that for global aridlands, the dominant bacterial phyla are Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes. As you increase in aridity, Chloroflexi and alpha-Proteobacteria increase while Acidobacteria and Verrumicrobia decline. For fungi, Basidomycota dominate (~56%) globally, but in aridlands, Ascomycota dominate (the Basidiomycota decline to ~22%). In biological soil crusts in particular, we see higher abundances of Acidobacteria, Chloroflexi, Planctomycetes, and Verrumicrobia (Maier et al. 2018). Findings from various papers seem to align fairly well. There are particular phyla that respond well to increasing stress associated with higher temperatures and decreasing water availability. Those who have adaptations to survive aridity are the ones we find in drylands. If you are a lucky microbe and are able to find a biocrust to live in, you may be able to thrive in those harsh conditions that otherwise would limit your growth (I am thinking here about the Acidobacteria). My next tasks is to delve into review papers about these phyla to learn their broad functions or physiology. The papers this week also included quite a lot on nitrogen, so I took some time reviewing the nitrogen cycle. In particular, in drylands, people research HONO gas production which contributes to hydroxyl radical formation and the production of tropospheric ozone. NO gas production is also important for its role in acid rain and stratospheric ozone depletion. Many of the microbial ecology papers are also concerned with nitrification (ammonium -> nitrite -> nitrate), denitrification (nitrate to stable nitrogen gas), and nitrogen fixation, which are all microbially mediated processes. I've been thinking through these processes in relation to last week's reading about nitrogen deposition being one of the main drivers of change in global drylands. My reading included one paper on best practices for microbiome research (Knight et al. 2018), an overview of how aridity impacts soil microbial diversity and abundance (Maestre et al. 2015), two papers specific to biological soil crusts (Garcia-Pichel et al. 2013 & Maier et al. 2018), two papers on statistical methods of analyzing microbial data (Blagodatskaya et al. 2013 & Faust et al. 2018), and one paper on how microbial communities maintain stability in spite of climate change (de Vries et al. 2013). I have read the Knight paper before for my recent metagenomics class with Dr. Fierer. In general, the paper advocates for using exact sequence variants instead of OTUs (which are groups of sequences with 97% similarity) which were traditionally used in the field. The paper also advocates for using metagenomic approaches instead of marker gene sequencing (the technique that I will be using) when possible since you can more information from the data. They also offer advice for dealing with compositional data which we commonly discussed in our class as being a major challenge for microbiome data (it must be treated differently from a statistical perspective). Since I am doing a marker gene analysis, the paper warns of 4 sources of bias in this approach: 1) primers do not have equal affinity for all taxa, 2) there is bias in selection of the variable region you are going to target, 3) amplicon size, 4) the number of PCR cycles. In addition, you are often limited to genus-level taxonomic resolution. As practice for describing a complete microbiome study with marker genes...you would design an experiment with the proper replication and controls. You would then take soil samples in a sterile way, homogenize the material, and when possible store the samples at -80C. You then need to extract DNA using a standard method. At this point you can use spike ins or perhaps deal with relic DNA if necessary. One DNA is extracted, you have to amplify the marker genes of interest with a known sequence tag so that you can identify the sample that it came from. You then pool all the samples together and have the DNA sequenced. For data analysis, you separate taxa into groups by their sample tags, remove the tags and primer sequences, and then group sequences into exact sequence variants. You would then align the sequences to some database (Greengenes or SILVA) which will tell you the taxonomy of the sequence. Some common analyses that you might try would be to count the number of taxa identified in each sample (richness) and then determine any similarities or differences in the community as a whole using ordination or differential abundance tests. If interested, one might use qPCR to determine the absolute abundance of genes in each sample and pair this to the relative abundance data described above. You could also include metadata or 'omics data. With these, you would try traditional correlation methods with Mantel statistical tests, networks, or regressions. Paper 2 discussed aridity and soil microorganisms. The most powerful part of this paper was their use of structural equation models (SEM) to determine that plant cover, soil pH, and organic carbon content together strongly affect the diversity and abundance of soil microorganisms with increasing aridity. They determine, most importantly, that aridity negatively affect soil carbon content and that soil organisms are carbon limited. Once water is available, organisms that are able to quickly regain activity and make use of available carbon do well. The authors make some predictions about the negative relationship they also found between the diurnal temperature range and bacterial/fungal abundances. The predict that large daily temperature swings cause increased physiological stress, which result in negative effects on plant cover which causes reduced carbon inputs into soil. They have some uncertainties associated with this prediction because other research has shown that plants increase their water use efficiency as carbon dioxide levels increase. This may mitigate any impacts due to aridity and result in stable microbial communities (though a second paper refutes this). The two papers on biological soil crusts were badass. The first was written in 2013 by the king of molecular approaches to biocrusts, Ferran Garcia-Pichel. In this work, the authors assess continental scale trends in biocrusts based on 16S rRNA diversity related to soil type, geochemistry, soil texture, and climate. At high taxonomic levels, there were no differences across the continent. But because a lot of prior work has been done for the Cyanobacteria, they were able to dig into cyanos specifically. They show that cold deserts are dominated in Microcoleus vaginatus. Hot deserts shift to Microcoleus steenstrupii. Using ordination and regression, they show that this trend is correlated to temperature (and not precipitation or geochemical factors which are important drivers for vegetation and moss). These two Microcoleus taxa are not closely related. They both are filamentous, non-heterocystous and they both produce rope-like cellular forms (a convergent trait). They follow up with physiological testing to show that all strains survived freezing, M. vaginatus could not survive hot temperatures, M. steenstrupii could survive at high temperatures. The authors then predict based on current climate change models that there will be complete replacement in 50 years to M. steenstrupii (no dispersal limitation is expected). The second biocrust paper focused on photoautotrophs in biocrusts asking whether there are heterotrophic organisms change as succession progresses. We know that the photoautorophs shift from bare soil to cyanobacteria to lichens or mosses, but what about the non-photosynthetic component? The answer is yes with a corresponding change in some functions. For instance, HONO and NO are produced at high levels in cyanobacteria crusts but not in the lichen or moss crusts. Carbon and nitrogen are higher in biocrusted soils, diversity is higher, and pigment production is higher. The most surprising finding for me was that there were a minimum of 5000 ASVs in bare dryland soils and up to 11000 in biocrusted soils. This is a huge number to have to deal with statistically, so I see why the authors chose ordination to show the differences between bare soils and those with biocrusts of different successional stages. The two statistical papers were more challenging. Faust et al. 2018 dealt with time series data in microbial studies with an emphasis on 4 different types of noise and how you could use the noise profiles to filter data to a more manageable size. The other paper, Blagodatskaya & Shade (2013) dealt with measuring the active microorganisms instead of the total microbial pool, which might include dormant and potentially active organisms. For me, this is less important because I assume all of my organisms are dormant for most of the year with periodic activity during rainfall events. This would be important to think about if I were to bring biocrusts into the greenhouse to work on microbial function. It is important to keep in mind that in soil generally, only about 2% of the total are active microbes, 40% are potentially active, and the rest are dormant. Some random facts to remember from this paper include: 1) oligonucleotide probes can bind to soil organic matter, 2) you can use FISH on a polyethylene terephtalate film buried in soil to do microbial imaging, 3) generally there is low RNA yield and poor protein extraction from soils, 4) ATP is decomposed within several hours after its release into the soil, 5) PLFA content is twice as large in G(-) cells compared to G(+) cells, 6) microbial cells in soil are smaller than in pure culture, 7) microbes can be active and "starving", 8) changes between the 4 physiological states of microbes in soil are common due to the spatial and temporal variability of substrates, 9) a shift to active state follows the sequence: respiration increase, ATP and enzyme production, DNA/RNA/PLFA synthesis, and growth. People usually study the growth phase. The final paper this week was about controls on microbial community stability. Stability is the ability to resist and recover from disturbances and it depends on 1) your functional traits, 2) your life-history strategy, 3) the soil environment. This paper predicts that you can use life history strategies to determine how a microbial community will respond to disturbance. An r-strategy is to have a high growth rate and low resource use efficiency. A k-strategy is to have a low growth rate with a high resource use efficiency. K-strategists are likely less resilient to disturbance, but also more resistant. In addition, the authors show how higher trophic levels can affect resilience of a microbial community through preferential grazing and dispersal, how plant presence will increase the resilience of the community, and how moisture availability increases resilience. Some things to think about are adaptation, how a series of disturbances may change the response, the role of evolutionary changes vs. community structure, and the role of routine successional trajectories (like seasonality). See you next time for another microbial ecology post. References Blagodatskaya, E. & Kuzyakov, Y. 2013. Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biology & Biochemistry 67, 192-211. https://doi.org/10.1016/j.soilbio.2013.08.024 de Vries, F. & A. Shade. 2013. Controls on soil microbial community stability under climate change. Frontiers in Microbiology, 4(265). https://doi.org/10.3389/fmicb.2013.00265 Faust, K. et al. 2018. Signatures of ecological processes in microbial community time series. Microbiome 6:120. https://doi.org/10.1186/s40168-018-0496-2 Fierer, N. et al. 2012. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. PNAS 109(52), 21390-21395. www.pnas.org/cgi/doi/10.1073/pnas.1215210110 Garcia-Pichel, F. et al. 2013. Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science 340, 1574-1577. DOI: 10.1126/science.1236404 Janssen, P.H. 2006. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Applied and Environmental Microbiology 72(3), 1719-1728. Knight, R. et al. 2018. Best practices for analysing microbiomes. Nature Rev Micro. https://doi.org/10.1038/s41579-018-0029-9 Maestre, F. et al. 2015. Increasing aridity reduces soil microbial diversity and abundance in global drylands. PNAS 112(51), 15684-15689. https://doi.org/10.1073/pnas.1516684112 Maier, S. et al. 2018. Photoautotrophic organisms control microbial abundance, diversity, and physiology in different types of biological soil crusts. The ISME Journal 12, 1032-1046. https://doi.org/10.1038/s41396-018-0062-8
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AuthorSierra is a graduate student in the Barger Lab at CU Boulder studying microbial ecology for dryland restoration. Archives
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