Understanding microalgal species composition and contributions in Antarctic glacial melt water through rbcL high throughput sequencing

The McMurdo Dry Valleys (MDV) in Antarctica present unique research opportunities, both because of the understudied biogeochemical impact of their microbial communities, and their sensitivity to climate change. Despite harsh desiccation, pH, and salinity stress, summer glacial melt water supports life in the MDV in the form of algal mats. These mat communities are complex in structure, with a network of dominant cyanobacteria interspersed with heterotrophic diazotrophs, smaller photoautotrophs, and thick extracellular polymeric substances. Due to their complexity, standard microscopy yields a limited understanding of community assemblages. Our previous high throughput sequencing (HTS) approaches focusing on 16S rRNA have profiled communities with understudied photosynthetic phyla such as Acidobacteria, Gemmatimonadetes, and Chloroflexi. To characterize these phototrophic communities, we are interested in (1) understanding their temporal dynamics and how the dominant cyanobacterial species influence community composition, (2) modeling how pH, nutrients, soil wetness, and temperature act as multivariate drivers of community composition, and (3) establishing a pipeline for HTS of the rbcL gene – which encodes the large subunit of the ubiquitous photosynthetic protein RuBisCO. Our initial screening of community DNA from MDV algal mats has shown the presence of Form IA, IB, and IC cbbL (an rbcL ortholog), and Form ID rbcL – indicating a relatively high degree of photoautotrophic diversity. Soil wetness drives anoxic conditions and we see that it shifts overall microbial composition – we expect photoautotrophs to respond similarly. We also expect photoautotrophic assemblages to shift with pH and soil nutrients. Our deep sequencing efforts suggest an inconsistency between indexing primers and algal DNA that could underestimate cyanobacterial and overestimate eukaryotic abundance. Resolving these issues with new approaches will allow us to more fully understand the dynamics of the MDV.