Research
Molecular processes are carefully orchestrated and remarkably resilient to a wide variety of changes in the environment.
Genetic variations encode inherited material (i.e., nature), epigenetic variations encode responses to the environment (i.e., nurture), and gene expression produces the functional gene products of these two processes.
We are interested in understanding the relative contribution of each of these processes singly and in combination to common symptoms (e.g., fatigue, neuropathy) or treatment failure (i.e., antiretroviral (ARV) therapy failure to treat HIV).
We are proud of our track-record of successful collaborations and mentorships with transdisciplinary research teams. We believe this approach is needed to develop future symptom scientists who work with genomics and big data.
A selection of publications are featured below.
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Molecular Mechanisms Underlying Cancer-related Symptoms (i.e., Fatigue and Neuropathy)When molecular processes are perturbed, patient symptom severity can change. These symptoms can have a negative impact on patient's ability to tolerate treatments and on their quality of life. We are particularly interested in cancer-related fatigue (CRF) and chemotherapy-induced peripheral neuropathy (CIPN). The goals of this research are to identify mechanism contributing to cancer related symptoms and to identify patients who are at greatest risk for the most severe symptom burden (i.e., CRF, CIPN). From a phenotypic perspective, this work has focused on the use of novel statistical approaches (e.g., hierarchical linear modeling, latent class analysis) to identify patients who have the highest symptom burden. From a molecular perspective, our efforts are focused on the mechanisms that underlie these common symptoms in oncology patients and survivors, as well as the mechanisms that contribute to the high-risk symptom phenotype by applying bioinformatic and systems biology approaches using high-throughput genomics (i.e., genomics, epigenomics, transcriptomics, and metabolomics). Cancer- and Chemotherapy-Related Fatigue
Chemotherapy-Induced Peripheral Neuropathy
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Prediction Models for Symptom SeverityEffective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments. CRF has a negative impact on the patients’ ability to tolerate treatments and on their quality of life. One of the limitations to effective treatment of CRF is the availability of a valid and reliable model to predict the severity of CRF. Our research aims to integrate demographic, clinical, and molecular data to develop accurate models to predict symptom severity over the course of chemotherapy treatment. This type of predictive model can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions. We use machine learning approaches to develop and evaluate models predicting symptoms in oncology patients.
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Viral Genomics and HIV Disease ProgressionHighly active antiretroviral treatment (HAART) is a highly effective treatment for HIV infection which aims to suppress viral replication. Despite its effectiveness, many patients experience virologic failure during HAART, where detectible viral loads are observed. Early virologic failure in the setting of HAART increases the risk of subsequent virological failure and disease progression. The presence of drug resistant mutations and suboptimal adherence are independent predictors of virologic failure. Genotypic resistance is one of the most important predictors of antiviral response and incomplete adherence is a strong predictor of treatment failure and viral rebound. Importantly, adherence early on in treatment is of critical importance as improved adherence has little impact on some treatments once the first mutations have emerged. Our research aims to reliably measure viral genome variation to quantify changes in the intra-patient HIV population over time by sampling each patient across time-points. These methods will be used in future studies investigating the evolution of HIV, including the identification of sites that influence virologic failure during HAART. Recently, we collaborated on a project evaluating if patterns of evolution in HIV-1 gag and env-gp120 were associated with biological sex differences.
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Molecular EvolutionThis basic science research, initiated by my doctoral research and funded by an NSF Doctoral Dissertation Improvement Grant (DEB-1011061), is centered on the theoretical underpinnings of molecular evolutionary genetics through the use of full genome scale data (i.e., sequencing, assembly, annotations) and the sea urchin model system. Firstly, this work provided a comparative genomics dataset and phylogeny which serves as the basis for modern research using the sea urchin model system. Next, we found that mutational bias has played a minor role in determining codon bias in the sea urchin and that preferred codon usage may be heterogeneous across different genes and subject to different forms of natural selection. Finally, we confirmed the widespread action of positive selection across sea urchin genomes, and allowed us to reject the possibility that annotation and alignment errors (including paralogs) were responsible for creating false signals of adaptive molecular divergence. These findings have broad implications for interpreting the role of synonymous codon usage and molecular adaptation in other species, including humans, as well as our understanding of the mechanisms involved in human disease where natural selection is active in shaping the biological conditions (i.e., cancer, HIV infection).
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Photo Credit: This image was generated using the MidJourney AI tool from the terms 'logo, neo-impressionism, network analysis, molecular biology' .