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Microorganisms are all around us. Some of them even live in our body and are essential for our healthy being. Study of microbial communities based on their genetic content has become very popular with the development of new technologies, which enable easy reading of DNA or RNA. The key role of these studies is usually to characterize significant microbial patterns of an environment. However, currently used visualization tools have many drawbacks for such analyses. The subject of this thesis is to design a R/Bioconductor package for simple creation of bipartite graphs from microbial data. This type of visualization brings many advantages for microbiome analysis. Benefits of bipartite graphs are further demonstrated by analysis of main parameters affecting computer processing of microbial data.

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Integrating crop and livestock is being considered to improve soil health by carbon sequestration. A two-year study (2019-2021) at CPBES in Newton, MS was aimed to evaluate soil microbial diversity in the warm, humid regions, specifically southeastern USA. Amplicons targeting bacterial 16S rRNA genes and fungal ITS2 regions were sequenced. Taxonomic assignment and microbial diversity characterization were performed using QIIME2®. Soil fungal diversity showed significant differences (alpha diversity, p = 0.031 in yr. 2020 and beta diversity, p = 0.037 in yr. 2021). Canonical Correspondence Analysis (CCA) and Mantel test showed significant influence on fungal diversity due to carbon (rm = 0.2581, p = 0.022), nitrogen (rm = 0.2921, p = 0.0165) in yr. 2021, and on bacterial diversity due to EE-GRSP (rm = 0.22, p = 0.02) in yr. 2020. Long term study of ICLS can help us better understand the shift in microbiome to improve crop production sustainably.

A thesis submitted to the School of Graduate Studies in partial fulfillment of the requirements for the degree Master of Science / The effect of changing environmental conditions on microbial population structure can be observed at both the species and community level. Within the Escherichia coli species, null mutations in the RpoS stationary phase regulator are commonly selected by growth on poor carbon sources. In contrast, mutations which restore RpoS function may provide a selective advantage for cells exposed to environmental stress. The loss and subsequent restoration of RpoS form a population-level switch for adaptation within poor carbon and high stress environments. To investigate selection for RpoS reversion, we exposed rpoS-deficient E. coli to high salt concentrations and assessed the phenotype of presumptive mutants. 3-9% of salt-resistant mutants contained reversion mutations within rpoS, while in 91-97% the loss of RpoS function was maintained and mutations at alternative gene loci were identified. These results show that RpoS function can be restored in deficient E. coli under selective pressure. At the community level, the application of next-generation sequencing (NGS) technology to characterize environmental microbial diversity can potentially augment traditional water quality monitoring methods. To investigate the use of NGS in identifying microbial taxa within the Niagara Region, we collected water samples from Lake Erie, Lake Ontario, and nearby areas and examined the metagenome of microbial communities. A QIIME (Quantitative Insights Into Microbial Ecology) analysis of sequence data identified significant differences in relative microbial abundance with respect to sample metadata (e.g. location and subtype), significant correlations between relative abundance and quantitative parameters (e.g. Escherichia coli counts and fecal DNA markers), and detected pathogen-containing taxa at a relative abundance of 0.1-1.5%. These results show that sequence-based analyses can be used in conjunction with traditional identification methods to profile the metagenomic community of environmental samples and predict water quality. Both within-species and community-wide analyses thus offer insight into how microbial populations respond and adapt to environmental fluctuations. / Thesis / Master of Science (MSc) / The effect of changing environmental conditions on microbial population structure can be observed at both the species and community level. Within the Escherichia coli species, we investigated reversion of loss of function mutations in the RpoS protein regulator in high salt conditions and identified RpoS restoration under selective pressure. At the community level, we examined the microbial DNA of water samples from the Niagara Region under select environmental conditions and assessed the viability of next-generation sequencing in augmenting traditional water quality monitoring methods. Both within-species and community-wide analyses offer insight into how microbial populations respond and adapt to environmental fluctuations.