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Osburn, M. R.

Publications and source records attributed to Osburn, M. R..

2 recordsLinked to original sources

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface

Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how two component systems (TCS) vary across metagenomes in the Deep Mine Microbial Observatory (DeMMO). TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity (r2 = 0.81). Among the geochemical parameters measured, HK frequency correlated the strongest with variance in dissolved organic carbon (DOC) (r2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean). IMPORTANCEThe ability to detect environmental conditions is a fundamental property of all life forms. However, organisms do not maintain the same environmental sensing abilities during evolution. To better understand the controls on microbial sensor abundance, which remain poorly understood, we evaluated how two-component sensor systems evolved within the deep Earth across sampling sites where abiotic and biotic properties vary. We quantify the relative abundances of sensor proteins and find that sensor systems remain abundant in microbial consortia as depth below the Earths surface increases. We also observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties across the DeMMO sites. These results suggest that multiple environmental properties drive sensor protein evolution and diversification and highlight the importance of studying metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.

microbiology↗

A metagenomic view of novel microbial and metabolic diversity found within the deep terrestrial biosphere

The deep terrestrial subsurface is a large and diverse microbial habitat and a vast repository of biomass. However, in relation to its size and physical heterogeneity we have limited understanding of taxonomic and metabolic diversity in this realm. Here we present a detailed metagenomic analysis of samples from the Deep Mine Microbial Observatory (DeMMO) spanning depths from the surface to 1.5 km deep in the crust. From these eight geochemically and spatially distinct fluid samples we reconstructed [~]600 metagenome assembled genomes (MAGs), representing 50 distinct phyla and including 18 candidate phyla. These novel clades include many members of the Patescibacteria superphylum and two new MAGs from candidate phylum OLB16, a phylum originally identified in DeMMO fluids and for which only one other MAG is currently available. We find that microbes spanning this expansive phylogenetic diversity and physical space are often capable of numerous dissimilatory energy metabolisms and are poised to take advantage of nutrients as they become available in relatively isolated fracture fluids. This metagenomic dataset is contextualized within a four-year geochemical and 16S rRNA time series, adding another invaluable piece to our knowledge of deep subsurface microbial ecology.

microbiology↗