Characterization of Bacterial and Fungal Communities Reveals Novel Consortia in Tropical Oligotrophic Peatlands.

Despite their importance for global biogeochemical cycles and carbon sequestration, the microbiome of tropical peatlands remains under-determined. Microbial interactions within peatlands can regulate greenhouse gas production, organic matter turnover, and nutrient cycling. Here we analyze bacterial and fungal communities along a steep P gradient in a tropical peat dome and investigate community level traits and network analyses to better understand the composition and potential interactions of microorganisms in these understudied systems and their relationship to peatland biogeochemistry. We found that both bacterial and fungal community compositions were significantly different along the P gradient, and that the low-P bog plain was characterized by distinct fungal and bacterial families. At low P, the dominant fungal families were cosmopolitan parasites and endophytes, including Clavicipitaceae (19%) in shallow soils (0-4 cm), Hypocreaceae (50%) in intermediate-depth soils (4-8 cm), and Chaetothyriaceae (45%) in deep soils (24-30 cm). In contrast, high- and intermediate-P sites were dominated by saprotrophic families at all depths. Bacterial communities were consistently dominated by the acidophilic Koribacteraceae family, with the exception of the low-P bog site, which was dominated by Acetobacteraceae (19%) and Syntrophaceae (11%). These two families, as well as Rhodospirillaceae, Syntrophobacteraceae, Syntrophorhabdaceae, Spirochaetaceae, and Methylococcaceae appeared within low-P bacterial networks, suggesting the presence of a syntrophic-methanogenic consortium in these soils. Further investigation into the active microbial communities at these sites, when paired with CH4 and CO2 gas exchange, and the quantification of metabolic intermediates will validate these potential interactions and provide insight into microbially driven biogeochemical cycling within these globally important tropical peatlands.

Progress: simultaneous searching of protein databases by sequence and structure.

We consider the problem of similarity searches on protein databases based on both sequence and structure information simultaneously. Our program extracts feature vectors from both the sequence and structure components of the proteins. These feature vectors are then combined and indexed using a novel multi-dimensional index structure. For a given query, we employ this index structure to find candidate matches from the database. We develop a new method for computing the statistical significance of these candidates. The candidates with high significance are then aligned to the query protein using the Smith-Waterman technique to find the optimal alignment. The experimental results show that our method can classify up to 97% of the superfamilies and up to 100% of the classes correctly according to the SCOP classification. Our method is up to 37 times faster than CTSS, a recent structure search technique, combined with Smith-Waterman technique for sequences.