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5.1.1 Building Leadership Skills

This study provided me with numerous opportunities for growth as a project manager and research supervisor. I took on several new responsibilities that I once took for granted. As a lab assistant, my main concern was in learning how to efficiently run lab protocols. As a project lead, I was responsible for developing these protocols and for asking the “what”, “why”, and “who” questions, in addition to “how”. I spent considerable time conducting literature reviews to optimize our protocols and in managing logistics. This transition into a leadership position was particularly difficult due to significant turnover of our lab team during my first semester, leaving few individuals with experience in managerial tasks.

I was responsible for supervising and mentoring graduate and undergraduate students, many of whom had little to no experience in the lab. My initial mentoring approach was to make

that this was impossible for a doctoral student with a full load of courses, teaching a course, conducting research, and studying for the competency exam. I learned to create a strict schedule to keep myself on track while maximizing my availability to students. More importantly, I realized my initial mentoring approach might be hindering critical thinking. While simply giving the answer to a student’s question may have been the easiest solution, I found the best long-term solution was to help students reach the answers themselves through active discussion and encouragement.

5.1.2 Building Research Skills

The multi-disciplinary nature of my dissertation project required me to develop a new set of research skills through courses at various departments, mentors and colleagues with specific expertise, and self-education. One of the goals of this project was to develop a bioinformatic and analytic pipeline, which could be used in future microbiome studies. Although our research group had prior experience with microbiome analysis, we used the QIIME program, conventional distance-based taxonomic units, and statistical models that only examined one taxon at a time. My first decision was to use mothur, software developed here at the University of Michigan. This decision doubled my work load as I attempted to learn this software on top of a unix-based coding language and a high-performance computer cluster. However, I feel this decision allowed our group to be proficient in two of the mostly commonly used microbiome programs. It took us two years to establish a pipeline which involves quality filtering of raw sequences in mothur or QIIME, oligotyping to create taxonomic units with improved resolution, and Dirichlet multinomial mixture modeling for community typing. As a team, we established an annotated pipeline which can be

used to train new students in microbiome analysis and serve as a foundation for new and improved methods as they are continually developed.

In addition to bioinformatics, I learned new statistical models that took into account clustered and longitudinal data. Mixed effects models were used to examine continuous and binomial outcomes while accounting for clustering by household. Accelerated failure time (AFT) models were used to examine time-to-event outcomes while accounting for interval and right censored data. AFT models were further expanded using a generalized estimating equation approach to account for clustering by household. I was only able to discover and apply these unique methods through the guidance of my committee members.

I have made significant efforts in learning the core concepts of immunology and using this knowledge to interpret epidemiologic results. As the association between influenza virus infection and the microbiome is likely mediated through the host immune response, I took courses to untangle the jumble of immunology acronyms and pathways that were discussed in various microbiome studies and animal experiments. Although my knowledge is still limited, it allows me to better understand the underlying biological mechanisms that may be driving what I observed in epidemiologic studies. Further, it spurs new research questions that I hope to investigate in the near future.

Lastly, I learned to develop strategies for scientific writing. Although I enjoyed sharing my results through presentations, I struggled with writing them down on paper. In the lab, I was taught to be extremely detail-oriented as minor measurement errors could lead to failed experiments. Instinctively, I was applying the same strategy in writing, which resulted in countless revisions of a single paragraph and, in some cases, a single sentence. By the end of the day, the paragraph had somehow remained unchanged. I have learned to fight this impulse and to begin the writing process

with an outline of unrefined statements. This approach helped me focus my time and energy into developing a clear story rather than contemplate trivial details, which could be improved at a later time. In addition, I identified unique strategies that personally worked for me, such as writing through dictation and learning to give my writing time to “settle” especially for sections that were particularly challenging.

5.1.3 Surrounding Yourself with Good People

One of the most important lessons I learned during the PhD program was to surround myself with good people who shared my passion and interests. I would not have reached this point without compatible and supportive mentors. Dr. Betsy Foxman is an expert in infectious disease epidemiology and microbial ecology, with years of experience in training doctoral students. Dr. Aubree Gordon is an incredible resource in influenza epidemiology and continues to be a role model in global health. My co-advisors helped me establish a foundation needed to explore the relationship between influenza virus and the host microbiome.

I learned the importance of identifying my weaknesses and finding mentors who could help me overcome them. Dr. Kerby Shedden is one of the few statisticians who has explored numerous approaches to analyzing microbiome data in epidemiologic studies. Dr. Marie Griffin is an infectious disease epidemiologist with clinical expertise in respiratory infections. Dr. Sophia Ng is an expert in household studies and influenza transmission modeling. This multi-disciplinary group of mentors provided me with invaluable advice, critiques, and perspectives in analyzing complex data.

I also learned to rely on fellow students who shared my interests in integrating molecular techniques to infectious disease epidemiology. This project has often felt like an expedition in

uncharted territories, largely due to my personal inexperience in microbiome analysis and the lack of any established methods in the literature. As a team, we worked together to explore and decipher new methods. This ultimately led to a bioinformatic and analytic pipeline, which we now use for other microbiome studies.

5.1.4 Learning from Hardships

Two hardships stand out the most. The first was when my initial dissertation project was terminated. I started the PhD program with a specific study in mind and had received permission to use archival samples from the principal investigator, former project lead, and study collaborators. I spent considerable time exploring potential research questions that could be answered using this study design. However, the project fell apart due to miscommunication and other factors that are still unknown to me. The main lesson I learned from this experience was the importance of bouncing back after difficult circumstances and learning to keep moving forward as there will always be other important research questions that both fascinate me and can contribute to improving public health.

A second notable hardship was as a research supervisor. An important component of my dissertation project involved using qPCR to quantify the absolute abundance of select bacterial species. I had trained one of my research assistants in qPCR in hopes of having her run plates while I away in Nicaragua for a summer research trip. Unfortunately, the majority of qPCR runs had poor efficiencies and had to be scrapped. This experience taught me the importance of quality control checks throughout any research process and for providing adequate support my research assistants.