Insights into glacial metagenome and sequence biases in comparative metagenomics

• Main Author: Choudhari, Sulbha
• Format: Text
• Language: unknown
• Published: No Publisher Supplied 2015
• Subjects: Arctic; Arctic Lake; Ice Lakes
• Online Access: https://dx.doi.org/10.7282/t3zc84qp; https://rucore.libraries.rutgers.edu/rutgers-lib/47136/

Of the land surface in the world, 25% is classified as a cold environment that is a large reservoir of microbial activity, such as glaciers and ice lakes. However, most of the resident organisms on glaciers are single celled and unculturable; therefore, the best way to gain insight into their community structure is by a metagenomics approach. Metagenomics by next generation sequencing has become an important tool for interrogating complex microbial communities, and has made it possible to study uncultured microbes. We analyzed the microbial diversity of an Alaskan glacier using 16S rRNA sequencing, and determined the functional potential of these communities by whole metagenomic sequencing. A rich and diverse microbial population of more than 2,500 species was revealed, including several species of Archaea that have been identified for the first time in the glaciers of the Northern hemisphere. A comparative analysis of the community composition and bacterial diversity present in Alaskan glacier with other environments showed a large overlap with an Arctic soil than with a high Arctic lake, indicating patterns of community exchange, and suggesting that these bacteria may play an important role in soil development. The metabolic potential of glacial ice metagenome showed a high versatility for different substrate at a low-nutrient environment. Numerous genes encoding for synthesis of unsaturated fatty acids and cryoprotectants were detected, which are the characteristics for metabolic adaptations at sub zero temperatures. Also, many sequences showed similarities to genes for methane, nitrogen, and sulfur metabolism. Though advancements in sequencing technology have made it possible to study metagenomes, they introduce different biases, which significantly affect the nucleotide distribution in a sequence. We formulated a method to detect sequence composition biases in the data generated by two different platforms, which efficiently detected sequencing based similarities and differences in the data. PCA analysis and phylogenetic heatmaps provided a compact visual image of the biases. It was found that the bias in the sequence is not only platform-specific, but other processes, like DNA-extraction protocols and experimental framework, also contribute to the differences. Therefore, caution should be exercised when interpreting the results of comparative metagenomics studies.