Location: Commodity Utilization Research
Project Number: 6054-41000-114-003-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2019
End Date: Jul 31, 2022
The goal is to characterize the microbiome of mill muds from different locations and of different ages in order to understand what organisms are present and better predict how they might affect soils to which the muds are applied. Microbial population, gene expression, and transcriptional analysis will be performed as the sequence based analysis in which the DNA or RNA from microbial communities is analyzed to determine their identity, potential function and activity.
The bacterial 16SrRNA (16S) gene and fungal internally transcribed spacer regions (ITS) will be amplified from mill mud DNA using polymerase chain reaction (PCR). These PCR amplicons will be sequenced at the Cornell Biotechnology Institute. Selected samples identified from these amplicon sequencing results will be further investigated using shotgun sequencing of extracted DNA (metagenomics) to get broader assessment of the metabolic potential in the muds of interest. A Bayesian classifier based on the RDP reference database will be used to classify all the sequences and then remove Eukaryotic, Chloroplast, and Mitochondria sequences along with any others that are not classified as 16S rRNA gene sequences from all the samples using a confidence threshold value of 80%. The degree of taxonomic resolution afforded by the quality of the remaining sequences will be then determined, and the alpha and beta diversity analyses will be performed for subsequent assignments to to community types. Publically available compost and other relevant metagenomes in IMG (https://img.jgi.doe.gov) will be made via BLAST with the mud sequences. To deduce more information about uncharacterized organisms in the muds we will attempt to assemble genomes from metagenomic sequences. The metagenome bins will be assembled into a composite genome using Geneious and other open-sourced genomic database. More functional annotations will be completed using the KEGG Automatic Annotation Server (http://www.genome.jp/kegg/kaas) and the Carbohydrate-Active enZymes database (CAZy, http://www.cazy.org). The composite genomes of novel organisms will be further screened in the CAZy database for the assessment of potential genes encoding carbohydrate metabolism.