Eimeria tenella is a eukaryotic, intracellular parasite that, along with six other Eimeria species, causes coccidiosis in chickens. This disease can result in weight loss or even death and is estimated to cause 2 billion euros of damages to the chicken industry each year. While much is known of the life cycle of E. tenella in the chicken, less is known about molecular mechanisms of infection and the chicken immune response. In this study, we produced a pipeline for dual RNA-sequencing analysis of a mixed chicken and E. tenella dataset.  We then carried out an analysis on an in vitro infection of the chicken macrophage HD-11 cell line.  This was followed by a differential expression analysis across six time points, 2, 4, 12, 24, 48, and 72 hours post-infection, in order to elucidate these mechanisms. The results showed clear patterns of expression for the chicken immune genes, with strong down-regulation of genes across the immune system at 24 hours and a repetition of early patterns at 72 hours, indicating that reinfection by a second generation of parasite cells was occurring. Several genes that may have important roles in the immune reaction of the chicken were identified, such as MRC2, ITGB3 and ITGA9, along with genes with known roles, such as TLR15. The expression of surface antigen genes in E. tenella was also examined, showing a clear upregulation in the late stages of merogony, suggesting important roles for merozoites. Finally, a co-expression analysis was carried out, showing considerable co-expression among the two organisms.  One of the gene co-expression networks identified appeared to be enriched with both infection specific genes from E. tenella and chicken immune genes. These results, along with the pipeline, will be used in further studies on E. tenella infections and bring us closer to the eventual goal of a vaccine for coccidiosis.

Soybean crops are vulnerable to a wide range of pathogens that reduce yield and cause extensive losses worldwide. In the United States, the soilborne pathogens Pythium spp., causing soybean damping-off, and Fusarium virguliforme, causing sudden death syndrome (SDS) of soybean, have been among the top diseases that most reduced soybean yields. This study demonstrated that biological control using native fungal antagonists could be a powerful tool to integrate with current management strategies for more efficient control of Pythium damping-off and SDS in soybean. Trichoderma spp. and Clonostachys rosea demonstrated the ability to mycoparasitize and antagonize the pathogens using different mechanisms and exhibited a protective effect on soybean in field conditions. The development of an efficient biological control program for disease management relies on a deep understanding of the BCA-pathogen interaction’s biology. This research also uncovered the molecular mechanisms involved in the F. virguliforme-T. afroharzianum interaction by using a dual RNAseq approach. Significant changes in both fungal organisms’ transcriptomes were discovered at different stages in their interaction. The results provided here can contribute to the future implementation of effective biological control programs for soybean. The benefits may also extend to other crops.

The scope and application of high throughput techniques has expanded from studying a single genome, transcriptome or proteome to understanding complex environments at a greater resolution with the help of novel computational frameworks. Comprehensive structural annotation i.e. description of all functional elements in the genome, is required for measuring genome response accurately, using high throughput methods. Annotation of genome sequences using high throughput data from RNA-seq and proteomics experiments complement computational methods for identifying functional elements and can help validate existing in silico annotation, correct annotation errors, and could potentially identify novel functional elements. Re-annotation studies in recent times have revealed shortcomings of automated methods and the necessity to validate existing annotations using experimental data. This dissertation elucidates re-annotation of Mannheimia haemolytica, Pasteurella multocida and Histophilus somni, bacterial pathogens associated with bovine respiratory disease in cattle. Experimental re-annotation of these bacterial genomes using RNA-seq and proteomics enabled the validation of existing annotation and discovery of novel functional elements that can be utilized in future functional genomics studies. We also addressed the need for developing an automated bioinformatics workflow that is broadly applicable for bacterial genome re-annotation, by developing open source Perl pipeline that can use RNA-seq and proteomics data as input. Simultaneous analysis of host and pathogen gene expression profiling using metatranscriptomics approaches is necessary to improve our understanding of infectious diseases. Traditional methods for analysis of RNA-seq data do not address the impact of cross-mapping of reads to multiple genomes for data originating from a metatranscriptomic study. Analysis of sequence conservation between species can help determine a metric for cross mapping to correct for signal vs. noise. We generated artificial RNA-seq data and evaluated the impact of read length and sequence conservation on cross-mapping. Comparative genomics was used to identify a core and pan-genome for quantifying gene expression. Our results show that cross mapping between genomes can directly be related to evolutionary distance between these genomes and that an increase in RNA-seq read length tends to negate cross mapping.

Leishmania spp. undergo substantial adaptations from being promastigotes, found in sandflies, to being amastigotes, residing in parasitophorous vacuoles within mammalian macrophages. In the past, microarray studies have sought to elucidate these adaptations using axenic amastigote systems or amastigotes purified from host-cells, raising the question whether the observed transcriptomic signatures were a true reflection of intracellular amastigotes. Moreover, with ever-improving genome annotations being available, it is clear that these studies failed to address the transcriptomic behaviour of a considerable number of transcripts. In the work presented herein, I employed RNA-sequencing to obtain transcriptomic profiles of Leishmania mexicana axenic promastigotes (PRO), axenic amastigotes (AXA) and intracellular amastigotes (AMA) in murine bone-marrow derived macrophages. The intracellular amastigotes were not purified from host cells, but instead sequencing reads assigned to a hybrid L. mexicana - Mus musculus genome and the transcriptomes separated in silico. We were able to map pre-mRNA processing sites, thereby defining transcript boundaries, proposing 184 truncations and 1253 extensions of existing gene models as well as discovering 936 novel genes. Mass-spectrometric evidence was obtained for both proposed extended and novel proteins. Using this improved genome annotation, we generated gene expression profiles for AMA, AXA and PRO, identifying 3832 differentially expressed transcripts between PRO and AMA as well as 2176 between PRO and AXA and 1234 between AXA and AMA. Transcripts differentially expressed between AMA and PRO correlated well with previous reports, were enriched for novel transcripts identified in this study and contained an unprecedented wealth of yet uncharacterised transcripts. Guided by these data, I performed a GFP-tagging screen identifying two proteins which may play an important role in L. mexicana biology, LmxM.16.0500, a member of a small, divergent, amastin-derived gene family, which appears to be released from the cell body of PRO, and LmxM.09.1330 a specific marker of the amastigote flagellar pocket.