Crude oil is a complex mixture of aromatic and aliphatic hydrocarbons of diverse molecular weight. In spite of its high hydrophobicity and toxicity, crude oil is a rich source of carbon for microorganisms. It has been proposed that microbial metabolism contributes to petroleum physicochemical characteristics, as highly specialized microorganisms are adapted to its extreme conditions. Deciphering these unique microbiomes will allow more in-depth characterization of crude oil and better understand its chemistry. The general aim of this study is to characterize the unique microbial communities of crude oil through a comparative metagenomics approach. I performed a survey of worldwide crude oil metagenomes in literature and databases. I identified 48 metagenomics datasets from five countries. The Comparative analysis of these metagenomes allowed us to identify how Methanogens are predominant in the North-American crude oil, being Methanoculleus and Methanosaeta the dominant genera in Canada and Methanothermococcus the predominant genus in the United States oil fields. In the case of Nigeria crude oil, Marinobacterium and Parvivaculum were the two dominant genera. In the case of Thailand, the dominant genus Thermus reflected the high-temperature environment of that oil field. Finally, metagenomes from China were the most diverse, reflecting the heterogeneity of the oil fields from that country. I generated metagenomics data from 27 Saudi Arabian crude oil samples originated in 6 different oil fields. As no crude oil metagenome has been reported yet for the Arabian Peninsula, the information provided in this dissertation is contributing towards a complete worldwide characterization of crude oils. Two genera, Peanibacilus and Thermospira, are proposed as the taxonomic markers for the set of Saudi crude oil analyzed. In this thesis I elucidated the structure of microbial communities in crude oils globally, suggesting that it may reflect the geological history of crude oils. This study sheds light on the importance of microorganisms for understanding petroleum geobiology. These findings suggest that it is possible to identify the distinctive microbiota associated with specific types of crude oil according to its location. The results presented here set the basis for developing novel methodologies for crude oil identification based on a microbial fingerprinting approach.

The application of second generation sequencing technology to the characterization of complex microbial communities has profoundly affected our appreciation of microbial diversity. The explosive growth of microbial sequence data has also necessitated advances in bioinformatic methods for profiling microbial communities. Data aggregation strategies should allow the relation of metagenomic sequence data to our understanding of microbial taxonomy, while also facilitating the discovery of novel taxa. For eukaryotes, a method has been established that links DNA sequences to the identification of organisms: DNA Barcoding. A similar approach has been developed for prokaryotes using target genic regions as markers for species identification and to profile communities. A key difference in these efforts is that within DNA barcoding there is a formalized framework for the evaluation of barcoding targets, whereas for prokaryotes the 16S rRNA gene target has become the de facto barcode without formal evaluation. Using the framework developed for evaluating DNA barcodes in eukaryotes, a study was undertaken to formally evaluate 16S rRNA and cpn60 as DNA barcodes for Bacteria. Both 16S rRNA and cpn60 were found to meet the criteria for DNA barcodes, with cpn60 a preferred barcode based on its superior resolution of closely related taxa. The high resolution of cpn60 enabled a method of sequence data aggregation through sequence assembly: microbial profiling using metagenomic assembly (mPUMA). The scoring of metagenomic assemblies in terms of sensitivity and specificity of the operational taxonomic units formed was used to evaluate and optimize the assembly of cpn60 barcodes. Using optimized parameters, mPUMA was demonstrated to faithfully reconstruct a synthetic community in terms of richness and abundance. To facilitate the use of mPUMA, a software package was developed and released under an open source license. The utility of mPUMA was further examined through the characterization of the epiphytic seed microbiomes of Triticum and Brassica species. A microbiome shared across both crop genera including fungi and bacteria was detected: a particularly important observation as it implies that seeds may serve as a vector for microbes that could include both pathogenic and beneficial organisms. The relative abundances of taxa identified by mPUMA were confirmed by qPCR for multiple cases of both fungal and bacterial taxa. By culturing isolates of both bacteria and fungi from the seed surfaces it was demonstrated that mPUMA faithfully assembled consensus sequences for OTUs that were 100% identical to isolated fungi and bacteria. Patterns observed in the relative abundances of the shared microbiome OTUs were used to generate the hypothesis that an Pantoea-like bacterium and an Alternaria-like fungus had an antagonistic relationship, since sequences corresponding to these organisms showed reciprocal abundance patterns on Triticum and Brassica seeds. Studies of the interactions of cultured isolates revealed fungistatic interactions that could account for their reciprocal abundances. These interactions could be directly relevant to plant health, given that Alternaria-like fungi are linked to grain spoilage in wheat, and diseases in canola. Taken together, results of this thesis demonstrate the superiority of the cpn60 universal target as a barcode for Bacteria, forming the basis for an assembly-based strategy for microbial profiling of bacterial and eukaryotic microbial communities that can lead to the discovery of novel taxa and microbial interactions.

The current ecological hypothesis states that the soil type (e.g., chemical and physical properties) determines which microbes occupy a particular soil and provides the foundation for soil provenance studies. As human profiles are used to determine a match between evidence from a crime scene and a suspect, a soil microbial profile can be used to determine a match between soil found on the suspect’s shoes or clothing to the soil at a crime scene. However, for a robust tool to be applied in forensic application, an understanding of the uncertainty associated with any comparisons and the parameters that can significantly influence variability in profiles needs to be determined. This study attempted to address some of the most obvious uncertainties of soil provenance applications such as spatial variability, temporal variability, and marker selection (i.e., taxa discrimination). Pattern analysis was used to validate the ecological theories driving the soil microbial biogeography. Elucidating soil microbial communities’ spatial and temporal variability is critical to improve our understanding of the factors regulating their structure and function. Microbial profiling and bioinformatics analyses of the soil community provided a rapid method for soil provenance that can be informative, easier to perform, and more cost effective than approaches using traditional physico-chemical data. This study also showed that stable profiles may allow comparison between evidence and a possible crime scene despite the time lapse (4 years) between sample collections, however, this is dependent on the analysis method, site, vegetation, and level of disturbance. Marker selection was also an important consideration for profiling. Even though Fungi look promising for single taxon soil discrimination, the additional markers can help discriminate between a wide variety of soil types. As in human identification, the more DNA markers queried the greater the discrimination power. Lastly, this study illustrated a novel method to query the iron relating genes and ability to design a novel marker that can easily be used to profile the functional diversity of a soil community to enhance soil classification. Overall this research demonstrated the potential and effectiveness of using microbial DNA from soil, not just for comparison, but also for intelligence gathering to pinpoint the geographic origin of the soil.