Cover photo: Flash spring in the Artist Paint Pots thermal area. Image credit: Roland Hatzenpichler.

Ever since the dawn of the molecular era, geothermal features like the ones in iconic Yellowstone National Park have served as model systems for microbial ecology (Barns et al., 1996). Their comparatively low biological complexity makes them ideal systems for developing and testing the next generation of tools targeted at revealing microbial ecophysiology before researchers apply them to other ecosystems (Brock & Brock, 1966). The extreme conditions of hot springs select for specialized and taxonomically distinct archaeal and bacterial populations, most of which have yet evaded cultivation under artificial laboratory conditions.

In our lab, we see this inability to grow microbes in isolation as both a challenge as well as an opportunity. While a microbes’ recalcitrance to cultivation makes standardized and controlled experiments sometimes difficult to conduct, it also gives us a unique opportunity. Rather than studying microorganisms in captivity, shaking them at 200 rpm in a glass beaker, away from their friends, frenemies, and foes, we can study them where and how they really live; as members of complex, metabolically inter-twined communities.

In our recently published study in The ISME Journal (Reichart et al., 2020), we developed and benchmarked a new next-generation physiology approach (sensu Hatzenpichler et al., 2020) to characterize the physiology of yet uncultured microbes under close to in situ conditions. By combining bioorthogonal non-canonical amino acid tagging (BONCAT), a technique still comparatively new to the field of microbial ecology (Hatzenpichler et al., 2014), fluorescence-activated cell sorting (FACS), and 16S rRNA gene amplicon sequencing, we tracked the response of a hot spring microbiome to substrate amendment and changes in oxygen availability in the headspace.

Overall, we tested microbial activity in the presence of 23 different potential energy, carbon, and nitrogen sources or oxygen concentrations in the headspace. The composition of the active portion of the microbiome significantly changed under two conditions, i.e. in the presence of the disaccharide cellobiose and under anoxic headspace conditions (controls were without substrate amendment and atmospheric oxygen levels).

In the beginning this was very surprising to us. Could none of the other tested compounds fuel the metabolism of microbes present in the hot spring? When taking a closer look at which members of the community were active in the un-amended control samples, we realized that despite the harsh conditions of this hot spring, the microbial community was highly active at the time of sampling. This made it hard to disentangle the minute changes induced by substrate addition. In retrospect, we could have pre-incubated samples with substrate before running BONCAT; this might have resulted in a higher signal-to-noise ratio and could have allowed us to better distinguish between cells responding to the amendment and cells active under in situ conditions (without amendment)).

We also found that, as for any experiment in biology, replication is essential to give meaningful answers, especially when working with heterogeneous sample types like sediments. All our incubations were conducted in biological triplicate and for our final conclusions we only considered results that were consistent and statistically supported across all replicates and when compared to all control samples. This precluded us from making more definitive statements on taxa that changed their activity in only two of the three replicates under other incubation conditions. While it might still be worthwhile to follow up on these trends in future experiments, it limited our ability to make definitive statements about microbial activity to only 2 out of 23 conditions. 

While it is important to work as closely as possible to natural conditions, sacrifices had to be made that probably impacted our study. Most importantly, while initial incubation tests were done in situ (vials were incubated in the hot spring), for our eventually published setup we decided to transport biomass back to the lab to work under more controlled conditions than possible in nature. Bison and bears often hinder us from accessing the site; if this would happen during an ongoing incubation, our experiments could be affected. Furthermore, many hot springs, including the Five Sisters hot spring group analyzed in this study, exhibit fluctuations in water level, temperature or gas flux on a regular basis (with intervals from minutes to years). While an in situ incubation would better reflect the conditions microorganisms are experiencing, it makes controlled experiments much harder to conduct.

An obvious next step for improving this approach would be to combine BONCAT-based substrate screening with whole genome sequencing of FACS sorted cells; this would enable researchers to directly link the inferred function and genetic make-up of translationally active cells. We also anticipate that this approach will soon be applied to other, more complex ecosystems. Our group and others have demonstrated that BONCAT-FACS is widely applicable to a variety of taxonomies (reviewed in Hatzenpichler et al., 2020) and ecosystems, including such diverse and structurally complex ecosystems as soil (Couradeau et al., 2019), marine sediments (Hatzenpichler et al., 2016), and the human microbiome (Valentini et al., 2020). Specifically, with support from the US National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA) our lab is currently using this novel approach to characterize activity responses in other hot springs, organic-rich deep-sea sediments, and a salt-marsh. 

Learn more about research in the Hatzenpichler lab at http://www.environmental-microbiology.com/

References

Barns, S.M., Delwiche, C.F., Palmer, J.D., and Pace, N.R. (1996) Perspectives on archaeal diversity, thermophily and monophyly from environmental rRNA sequences. Proc Natl Acad Sci U S A 93: 9188-9193.

Brock, T.D., and Brock, M.L. (1966) Autoradiography as a tool in microbial ecology. Nature 209: 734-736.

Couradeau, E., Sasse, J., Goudeau, D., Nath, N., Hazen, T.C., Bowen, B.P. et al. (2019) Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun 10: 2770-2779.

Hatzenpichler, R., Krukenberg, V., Spietz, R.L., and Jay, Z.J. (2020) Next-generation physiology approaches to study microbiome function at the single cell level. Nat Rev Microbiol 18: 241-256. Download pdf

Hatzenpichler, R., Scheller, S., Tavormina, P.L., Babin, B.M., Tirrell, D.A., and Orphan, V.J. (2014) In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol 16: 2568-2590. Download pdf

Hatzenpichler, R., Connon, S., Goudeau, D., Malmstrom, R.R., Woyke, T., and Orphan, V.J. (2016) Visualizing in situ translational activity for identifying and sorting slow-growing archaeal-bacterial consortia. Proc Natl Acad Sci U S A 113: E4069-E4078. Download pdf

Reichart, N.J., Jay, Z.J., Krukenberg, V., Parker, A., Spietz, R., and Hatzenpichler, R. (2020) Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. The ISME Journal. Download pdf

Valentini, T.D., Lucas, S.K., Binder, K.A., Cameron, L.C., Motl, J.A., Dunitz, J.M., and Hunter, R.C. (2020) Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota. Nat Commun 11: 2287-2297.