Transcriptomic analysis of Ustilago maydis infection and improving cassava brown streak disease surveillance in sub-Saharan Africa
Abstract/Contents
- Abstract
- Ustilago maydis is a smut fungus that is distinctive for its ability to infect and form tumors in all aerial organs of corn. Previous research has shown that different sets of fungal effector genes are expressed depending on the tissue and cell type being infected although there is limited information on how the gene expression changes over the course of the infection, particularly in anthers. In this thesis I study early infection of maize tassels using both whole tissue and single cell RNAseq. Problems with being able to select infected tissue and the sc-RNAseq sequencing depth, severely limited the conclusions I was able to draw, and I propose potential improvements for subsequent studies on cell type specific plant infection. Cassava brown streak disease (CBSD) is a looming threat to food security for small holder farmers as it rapidly spread across sub-Saharan Africa. Currently, many countries across sub-Saharan Africa conduct annual surveys on the prevalence of CBSD, and a range of management interventions have been proposed to minimize yield loss in regions where CBSD has become endemic. In this thesis, I developed two stochastic epidemiological models for the spread of CBSD with a field and for conducting country wide surveys that I used to compare the efficacy of different interventions and survey protocols. At the scale of a individual farmer's field, I find that introduction of virus-free 'clean seed' effectively increases yield across a wide range of disease pressures. I evaluated the effectiveness of the current survey protocol at detecting the presence of CBSD within a field, and found that it is relatively effective at low levels of CBSD prevalence and that the probability of detection can be meaningfully increased by surveying later within the growing season, particularly in areas with high vector density. I then extended the field level model to simulate the effectiveness of different protocols for conducting annual country level surveys for CBSD by combining it with a second model of landscape level CBSD spread. I find that with the current protocol, detection of newly infected districts is limited by the small number of infected fields surveyed. Changing the current protocol of uniformly allocating surveys across Nigeria to surveying locations proportional to the density of cassava greatly improves the effectiveness of CBSD detection, and to a lesser extent so does increasing the annual number of surveys conducted. This effect is consistent across multiple districts in Nigeria and multiple ways of quantifying survey efficacy. Finally, with these simulation results I also propose guidelines for practical, actionable recommendations for the deployment of management strategies and survey protocol modifications in sub-Saharan Africa.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ferris, Alex Cameron |
|---|---|
| Degree supervisor | Walbot, Virginia |
| Thesis advisor | Walbot, Virginia |
| Thesis advisor | Bergmann, Dominique |
| Thesis advisor | Fischbach, Michael |
| Thesis advisor | Long, Sharon R |
| Degree committee member | Bergmann, Dominique |
| Degree committee member | Fischbach, Michael |
| Degree committee member | Long, Sharon R |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Alex Ferris. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/nb236ff7500 |
Access conditions
- Copyright
- © 2022 by Alex Cameron Ferris
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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