METAGENOMIC ANALYSIS OF FOREST SOIL FROM THE DEPARTMENT OF ITAPUA, PARAGUAY

GONZALEZ CORIA, J.; BICH G. A.; CASTRILLO M. L.

Congreso; 1 ST Latin American Congress of Women in Bioinformatics & Data Science LA.; 2020

Latin American Congress of Women in Bioinformatics & Data Science LA.

Paraguay is facing up great losses of biodiversity due to deforestation caused mainly by the agricultural sector. In this context, it is important to assess the biological differences in ecosystems and identify the impact that ecosystem restoration generates and determine land management strategies they may have on them. The advent of next-generation sequencing (NGS) platforms makes it feasible to conduct more detailed analyses of the biotic composition of environmental soil samples than could have occurred previously. Metagenomics allows understanding the taxonomic and functional diversity of soils, that is, knowing the genes that are involved in biogeochemical processes. The objective of this study was to determine the diversity index of the microbial communities in the soil under study and the genes related to metabolic pathways through the application of Omic Technologies. Soil random samples were collected from Cambyreta, Itapúa Department, Paraguay (S27°22,738’–W055° 41,631’). For a total of six samples with three subsamples were homogenized in order to obtain a sample pool. Metagenomic DNA was extracted using the Power Soil DNA isolation Kit following the manufacturer’s instructions. Thereafter, a metagenomic analysis of these samples was performed. The raw sequences of the metagenomes were uploaded to the MG-RAST server. After quality control was performed using MG-RAST. The metagenomic analyses revealed that bacteria (98,54%) dominated the forest soil samples, followed by Archaea (0,65%) and Eukaryota (0,55%). The other sequences corresponded to unclassified sequences (0,22%), viruses (0,04%) and other sequences (0,00%). Also showed that 1,785 sequences (0%) contain ribosomal RNA genes, 1,353,160 sequences (44.71%) contain predicted proteins with known functions, and 1,671,367 sequences (55.23%) contain predicted proteins with unknown function. This study demonstrates that metagenomic approaches can be used to build a predictive understanding of how microbial diversity and function vary across terrestrial biomes.