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The understanding of the molecular mechanisms that affect the adaption of microbial species at different temperatures is essential to appreciate how biodiversity originates and how it can be maintained in a constantly changing environment. The Saccharomyces genus is an ideal group to study interspecific ecological traits, especially temperature, since it consists of a monophyletic group of species with high levels of sequence similarity within the Ascomycota phylum. The Saccharomyces genus is composed of eight species with a wide range of optimal growth temperatures and can be divided into thermo-tolerant, generalist and cold-tolerant. Saccharomyces species that share the same habitat (sympatry) may have optimum growth temperature, suggesting a partitioning of thermal niche possibly to avoid exploitative competition. In this study, the cellular role of a group of genes identified as key elements to provide optimal growth at cold temperatures by computational modelling and large-scale studies were investigated. Homozygote mutants of ADH3, GUT2, NMA1, YND1, ADH5 and FAA1, in all eight species belonging to the Saccharomyces genus were created. Upon deletion of these genes, S. kudriavzevii and S. arboricola displayed the most compromised fitness when compared with the other species. The level of expression of these genes in their native species at different temperature was determined. A significantly higher expression at cold for S. paradoxus, S. kudriavzevii and S. jurei compared to S. cerevisiae was observed. Different combinations of double mutants were also constructed, and the fitness scored in the different species. The double mutants ΔADH3/ΔADH3 and ΔYND1/ΔYND1 in S. paradoxus, S. uvarum and S. kudriavzevii, showed the greatest fitness impairment at cold. Interestingly, most of the genetic interactions in the double mutants showed positive and negative epistasis in S. cerevisiae and S. kudriavzevii, respectively. Also, interactions of ncRNAs with the genes under study to infer epigenetic mechanisms that are involved in cold-tolerance were evaluated. To tease apart the role of promoters (i.e. gene expression) from the allele type (i.e. genetic) in cold-adaptation, either the ADH3 and YND1 alelles or their promoters in S. cerevisiae, S. paradoxus, S. jurei and S. eubayanus with alelles or promoters belonging to the cold-tolerant species S. kudriavzevii were replaced independently. Fitness improvement was detected at cold temperature in the strains carrying S. kudriavzevii promoter for both ADH3 and YND1, whereas fitness was lower when the alleles were swapped, suggesting strength of expression is crucial in maintaining growth at cold temperatures over the type of allele. Additionally, different conditions for isolating yeast wild strains were explored, finding that cold-tolerant yeast species had higher fitness over thermotolerant and generalist in oak (Quercus) and spruce (Picea) infused media, and at low temperature S. eubayanus was the species which showed the best performance. Field studies were carried out to attempt to isolate S. eubayanus. The location of Scottish Highlands Forest was chosen based on maximum probability model based on climate data. No Saccharomyces species were isolated, instead species belonging to the genus Nakazawea were the most representative among the yeast isolates. Overall, this study contributes to understanding the importance of a non-essential group of genes in adaptation to cold temperatures and the role of promoter and allele type on cold adaptation in Saccharomyces species. It also, sheds light on the type of epistasis among the candidate genes in different species background.

Two of the major bottlenecks that currently hinder the study of plant specialized metabolism are the structural elucidation of metabolites and the functional characterization of their associated genes. Although modern analytical instruments (typically mass spectrometers (MS) coupled with liquid chromatography (LC), or LC-MS) are capable of detecting up to thousands of metabolites in plant extracts, no comparable methods exist for the high-throughput structural elucidation of these metabolites. Similarly, the functions of specialized metabolic genes can be difficult to elucidate as they often belong to large gene families that undergo frequent gene duplication. While the most common method of gene function prediction – the transfer of known gene functions through related species – is easily implementable, it may yield inaccurate conclusions for metabolic genes. The goal of this dissertation is to contribute to the alleviation of both bottlenecks. Chapter 1 reviews current methodologies and algorithms for the elucidation of metabolite structure and gene function – especially in relation to plant specialized metabolism. Chapter 2 describes the development of machine learning algorithms to structurally annotate metabolites based on LC-MS data, and the evaluation of their performance with plant metabolites. Chapters 3 and 4 detail the analysis of an integrative transcriptomics and metabolomics experiment undertaken in the model grass species Brachypodium distachyon, designed to both elucidate metabolic functions of genes and characterize stress-induced shifts in metabolism. Chapter 3 explores the utilization of emerging algorithms for i) partial elucidation of the structures of plant metabolites, ii) quantification of the impacts of different environmental conditions on the plant metabolome. Chapter 4 implements integrative-omics analyses to characterize B. distachyon genes involved in facilitating arbuscular mycorrhizal symbiosis, and investigates the activity of one functionally unknown gene. Collectively, these studies find that structural annotation of plant metabolites facilitates the understanding of i) how plants adapt to changing environments, and ii) metabolic activities of plant genes. Such knowledge is essential for alleviation of major bottlenecks in the study of plant specialized metabolism.

Most broadly, this study aimed to develop a better understanding of how organisms evolve novel functions and traits, and examine how seemingly complex adaptive trait syndromes can convergently evolve. As an ideal example of this, the carnivorous plants were chosen. This polyphyletic grouping contains taxa derived from multiple independent evolutionary origins, in at least five plant orders, and has resulted in striking convergence of niche and morphology. First, a database study was performed, with the goal of understanding the evolutionary trends that impact carnivorous plants as a whole. Using carnivorous and non-carnivorous plant genomes available from GenBank. An a priori list of Gene Ontology-coded functions implicated in plant carnivory by earlier studies was constructed via literature review. Experimental and control samples were tested for statistical overrepresentation of these functions. It was found that, while some functions were significant in some taxa, there was no overall shared signal of plant carnivory, with each taxon presumably having selected for a different subset of these functions.Next, analyses were performed that targeted Sarracenia alata specifically. A reference genome for S. alata was assembled using PacBio, Illumina, and BioNano data and annotated using MAKER-P with additional preliminary database filtration. From these, it was found that Sarracenia alata possesses significant and substantial overrepresentation of genes with functions associated with plant carnivory, at odds with the hypothesis that the plant primarily relies on symbioses. Finally, pitcher fluid was collected from S. alata in the field. RNA was extracted from the fluid, sequenced via Illumina, and assembled with Trinity. Sequences were sorted into host plant and microbiome based on BLAST match to the S. alata reference genome. It was found that, while S. alata contributes two-thirds of the transcripts, these encode no digestive enzymes and a very limited set of transport channel proteins; however, these functions were identified in microbe-originated transcripts. A large portion of S. alata’s transcripts were instead found to encode anti-microbial peptides (AMPs). These short proteins are known to play a role in modulating gut microflora in animals, and while they are documented in plants, their role had never been addressed in carnivorous plants.From these findings, I have concluded that there are a large number of evolutionary paths that lead to highly similar adaptive strategies; specific relevant functions may be identified, but the subset of these used by a given lineage will vary greatly. In Sarracenia alata specifically, it appears that at one point in time there was strong selection favoring the retention of genes associated with prey digestion. However, at present these do not appear to be expressed, with microbial symbioses instead responsible for the bulk of the digestive process. Instead, the plant has likely evolved to specialize in a regulatory role, modulating the microbial composition of its fluid via the production of AMPs. This shows that not only do lineages evolve via different pathways, but that the same lineage may change its adaptive specialization at different points in its history.

Down syndrome is one of the most common genetic disorders, resulting in a range of neurological and neuromuscular disabilities. Although the presence of specific disabilities varies among individuals with Down syndrome, all individuals with Down syndrome are born with hypotonia (low muscle tone) and over half with congenital heart defects. Later in life, all individuals demonstrate intellectual disabilities to varying degrees, while many also develop early-onset Alzheimer’s disease. While the cause of Down syndrome is known to be a triplication of the 21st chromosome, it is unknown how this extraneous genetic material causes the development of these phenotypes. We have begun research into the biological basis of these disabilities using the tiny nematode, Caenorhabditis elegans as a genetic model. We used the technique RNA interference (RNAi), which allows us to study the in vivo function of genes by knocking down their expression one at a time in a living, behaving animal. We have used this technique to systematically study the in vivo function for genes involved in Down syndrome. To this end, we identified and knocked down C. elegans genes with sequence similarity to 67% of genes on the human 21st chromosome genes. We used these RNAi-treated worms to investigate the neuromuscular function of human chromosome 21 gene equivalents by assaying locomotion and pharyngeal pumping in a blinded screen. We used locomotion as a measure of neurological and neuromuscular function, while we used pharyngeal pumping as a model for cardiac function. We also performed an aldicarb screen to examine the role of some of these genes in the function of the synapse. Our experiments have provided valuable insight into the in vivo function of the vast majority of genes on the human 21st chromosome. This will be vital to identify genes that are potentially involved in eliciting Down syndrome-related phenotypes, laying the groundwork for further studies into the neurobiology of Down Syndrome.

The Genome Reverse Compiler (GRC) is an annotation tool for prokaryotic genomes. Its name and philosophy are based on analogy with a high-level programming language compiler. In this analogy, the genome is a program in a certain low-level language that humans cannot understand. Given the sequence of any prokaryotic genome, GRC produces its corresponding "high-level program"--its annotation. GRC works in a completely automatic manner, using standard input and output formats. The goal is to provide an open-source, easy-to-run, very efficient annotation program.

Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.