Application Information

• The program is 8-months (September - April).
• Participation is 100% remote.
• Participants will receive a $5000 stipend.
• Participants are expected to commit 10 hours/week.
• Participants will present at a national conference in May, all expenses covered.

Project 1. Beyond the Usual Suspects – Investigating Understudied, Although Common, Urinary Bacteria. We now know that most bladders are not sterile. Instead, they harbor indigenous microbial communities called the urobiota. Bacterial species associated with urinary tract infection (UTI) are not constitutively pathogenic; instead, they are common members of the human microbiome that behave as pathogens in certain contexts. UTIs may be caused by a single bacterial strain but are often polymicrobial, and even when a single microbe is causative (e.g., E. coli), its impact is almost certainly influenced by other members of the urobiota. Unfortunately, most urinary microbes are known only by name. Except E. coli, Klebsiella, Enterococcus and a few other “Usual Suspects,” we know almost nothing of the physiology of >200 different urinary species, especially if they are beneficial symbionts or potentially harmful pathobionts. Without this knowledge, we cannot develop more successful diagnostic tools and treatment algorithms. Two common urinary species are Alcaligenes faecalis (Af) and Oligella urethralis (Ou), both members of the family Alcaligenaceae. We have draft genomes for only 1 Af urinary isolate and only 3 Ou urinary isolates, and we have no complete genomes. Dr. Wolfe’s lab possesses 2 urinary isolates of Af and 23 of Ou, to be sequenced for this project. The project aims to provide a comprehensive learning experience in genome assembly and genome mining, equipping the trainees with critical bioinformatics skills. Mastery of these techniques not only facilitates the discovery of genes and pathways but also drives advancements in fields like microbiology, medicine, and biotechnology.

Project 2: Interpreting the Microbiome: Can We Predict Symptoms/Outcomes from the Urobiome? The microbial contribution to some lower urinary tract symptoms (LUTS) is unknown. While prior research has found bacterial taxa that are more commonly found in the urobiome of individuals with/without symptoms, the interactions between microbes in these communities cannot be captured in such analyses. In this project, we will develop a model capable of predicting microbial communities associated with LUTS. Here we will focus on urinary tract infection (UTI). Because UTIs are commonly caused by the overabundance of a particular bacterial species, it presents a simple case for developing the model. Furthermore, there is a wealth of publicly available urobiome sequence data from individuals with and without UTI symptoms; thus, the model can be rigorously tested. This model will enable us to consider binary predictors and higher order multinominal interactions of these binary predictors between members of the community. The proposed model will consider higher-order interactions and identify the species abundances that collectively predict UTI with the hopes of applying this strategy to other LUTS. Trainees will gain experience with coding, testing predictive models as well as testing the aforementioned method against other approaches, e.g., machine learning, and working with sequence data.

Project 3: Bioinformatics, Biostatistics, and Machine Learning Analyses to Identify Genetic Features Enriched in Stone-Associated Bacteria. One in ten individuals will develop a kidney stone during their lifetime, and the incidence continues to rise. Traditional treatments focus on reducing urinary supersaturation of calcium and oxalate; however, they do not address the biological mechanisms by which crystals grow into complex stone structures. A deeper understanding of these underlying processes is essential for developing novel medical therapies. Bacteria are well known contributors to struvite stones and have more recently been implicated in calcium oxalate (CaOx) stones as well. However, the specific bacterial genes associated with kidney stone formation remain unknown. Students in this project will learn bioinformatics, biostatistics, and machine learning methods to identify genetic features enriched in stone-associated bacteria. The findings may provide molecular insights needed to identify high-risk patients and develop targeted strategies to prevent stone recurrence.

• Applications Open: May 1, 2026
• Application Deadline: June 15, 2026
• Decisions announced by July 10, 2026

Individuals who meet eligibility requirements (below) and who are interested in how data drives medical research and clinical practice and/or urology.

• A brief statement outlining your research interests and prior relevant experience. [5000 character limit]
• A brief description of your educational goals [2000 character limit]
• A brief description of your particular interests in the L.A.U.D. program [5000 character limit]
• Current CV/resume
• Copy of your official or unofficial transcript
• One letter of recommendation from a STEM faculty member

 1. U.S. citizens or permanent residents, AND individuals who meet the enrollment requirement:

• Will be enrolled at least half-time in an accredited college (including community college) or university as an undergraduate as a sophomore, junior or senior during the 2026-2027 academic year.

2. Completed the following coursework:

• Genetics or Cell Biology lecture courses
• Statistics course (which included R programming) or Introductory Programming (Java, C/C#/C++, Matlab, Python, R)

Note, applicants who will graduate prior to May 2027 are not eligible.