My research interest lies in applying data science for actionable transformation of human health from the bench to bedside. Current research focus areas include cutting edge single-cell sequencing informatics and genomics; precision medicine through integration of multi-omics data types; novel modeling and computational methods for biomarker research; public health genomics. I apply my biomedical informatics and analytical expertise to study diseases such as cancers, as well the impact of pregnancy/early life complications on later life diseases.
The current goal of our research is to learn enough about the physiology and ecology of microbes and microbial communities in the gut that we are able to engineer the gut microbiome to improve human health. The first target of our engineering is the production of butyrate – a common fermentation product of some gut microbes that is essential for human health. Butyrate is the preferred energy source for mitochondria in the epithelial cells lining the gut and it also regulates their gene expression.
One of the most effective ways to influence the composition and metabolism of the gut microbiota is through diet. In an interventional study, we have tracked responses in the composition and fermentative metabolism of the gut microtiota in >800 healthy individuals. Emerging patterns suggest several configurations of the microbiome that can result in increased production of butyrate acid. We have isolated the microbes that form an anaerobic food web to convert dietary fiber to butyrate and continue to make discoveries about their physiology and interactions. Based on these results, we have initiated a clinical trial in which we are hoping to prevent the development of Graft versus Host Disease following bone marrow transplants by managing butyrate production by the gut microbiota.
We are also beginning to track hundreds of other metabolites from the gut microbiome that may influence human health. We use metagenomes and metabolomes to identify patterns that link the microbiota with their metabolites and then test those models in human organoids and gnotobiotic mice colonized with synthetic communities of microbes. This blend of wet-lab research in basic microbiology, data science and in ecology is moving us closer to engineering the gut microbiome to improve human health.
My lab has two main areas of focus: molecular characteristics of head and neck cancer, and the intersection of regulatory genomics and pathway analysis. With head and neck cancer, we study tumor subtypes and biomarkers of prognosis, treatment response, and recurrence. We perform integrative omics analyses, dimension reduction methods, and prediction techniques, with the ultimate goal of identifying patient subsets who would benefit from either an additional targeted treatment or de-escalated treatment to increase quality of life. For regulatory genomics and pathway analysis, we develop statistical tests taking into account important covariates and other variables for weighting observations.
The Aguilar group is focused understanding transcriptional and epigenetic mechanisms of skeletal muscle stem cells in diverse contexts such as regeneration after injury and aging. We focus on this area because there are little to no therapies for skeletal muscle after injury or aging. We use various types of in-vivo and in-vitro models in combination with genomic assays and high-throughput sequencing to study these molecular mechanisms.
Our laboratory focuses on (1) the biology of cancer metastasis, especially bone metastasis, including the role of the host microenvironment; and (2) mechanisms of chemoresistance. We explore for genes that regulate metastasis and the interaction between the host microenvironment and cancer cells. We are performing single cell multiomics and spatial analysis to enable us to identify rare cell populations and promote precision medicine. Our research methodology uses a combination of molecular, cellular, and animal studies. The majority of our work is highly translational to provide clinical relevance to our work. In terms of data science, we collaborate on applications of both established and novel methodologies to analyze high dimensional; deconvolution of high dimensional data into a cellular and tissue context; spatial mapping of multiomic data; and heterogenous data integration.
Our research aims to address fundamental problems in both biomedical research and computer science by developing new tools tailored to rapidly emerging single-cell omic technologies. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease. As computational method developers, we seek to both employ and advance the methods of machine learning, particularly for unsupervised analysis of high-dimensional data. We have particular expertise in manifold learning, matrix factorization, and deep learning approaches.
Obesity promotes type 2 diabetes (T2D) the 3rd leading cause of death in the United States and accounts for $237 billion healthcare cost. T2D and obesity promote infections that cause sepsis, the leading cause of mortality in the intensive care unit following traumatic injury. Despite advances in supportive care, sepsis mortality remains 25% and escalates over time. Survival from sepsis depends on emergency myelopoiesis of macrophages (MΦ) to clear bacteria and deescalate inflammation. Resolving inflammation requires MΦ polarization from pro-inflammatory M1 to anti-inflammatory MΦ states. The MΦ ability to polarize depends on the intrinsic plasticity inherited from hematopoietic stem and progenitor cells (HSPCs) during emergency myelopoiesis. Our published data in trauma and sepsis in mice and humans demonstrates that obesity and T2D alter HSPC myelopoiesis, inhibit MΦ plasticity and prevent M1Φ polarization to other functional MΦ states. However, the impact of altered MΦ myelopoiesis and restricted M1Φ polarity on sepsis pathogenesis is unknown. A critical need exists to understand the mechanisms by which obesity and T2D alter myelopoiesis, inhibit MΦ plasticity and prevent MΦ polarity to promote bacterial sepsis mortality. We hypothesize that obesity and T2D prime HSPC myelopoiesis to produce dysfunctional M1Φs incapable of bacterial clearance, and effective polarization which hinder inflammation resolution during sepsis and cause mortality. We will test the hypothesis with the following aims that, when completed, will fill the current knowledge void and improve sepsis survival. In Aim 1 we will determine the mechanisms in T2D and obesity that alter myelopoiesis in bacterial sepsis. The findings will reveal how obesity and T2D prime HSPCs and alter myelopoiesis to prevent MΦ polarity in mice and humans with sepsis. In Aim 2 we will identify the functional consequences of obese, T2D M1Φs unable to polarize to other activation states during bacterial sepsis. We will explore how restricted MΦ polarity effects immune cell responses and cytokine production to define how T2D and obesity impede inflammation resolution. The data generated will identify new pathways that promote aberrant myeloid production, restricted MΦ plasticity and prevent inflammation resolution during bacterial sepsis. Novel targeted therapies can then be developed for clinical implementation for bacterial eradication, wound healing and survival from sepsis in obesity and T2D.
Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.