BBSP Faculty

We are interested in determining the mechanisms involved in the beneficial modulation of the gut microbiota by prebiotics (functional foods that stimulate growth of gut native beneficial bacteria) and probiotics (live bacteria that benefit their host). Specifically, we aim to develop prebiotic and probiotic interventions as alternatives to traditional treatments for microbiota-health related conditions, and to advance microbiota-based health surveillance methods.

My Lab focuses on studies of neural circuits underlying attention, executive function and stress response in the human brain, as well as the breakdown in these functions in neuropsychiatric and neurodevelopment disorders such as schizophrenia, substance abuse, depression and autism. We are particular interested in identifying neural precursors and predictors of illness in high-risk adolescents, including risk for psychosis, mood disorders and substance abuse. Our research uses multimodal imaging integrating functional magnetic resonance imaging, electrophysiological scalp recording, experimental psychology and neuropsychological assessment techniques to explore the behavioral and neurophysiological dimensions of information processing. Much of the current work focuses on mapping the role of stress neurobiology in predicting severity of anxiety and anhedonia in adolescents and procuring risk for severe psychiatric developmental disorders.

My research group is broadly interested in the application of sequencing technologies in medical genetics and genomics, using a combination of wet lab and computational approaches.  As a clinician, I am actively involved in the care of patients with hereditary disorders, and the research questions that my group investigates have direct relevance to patient care.  One project uses genome sequencing in families with likely hereditary cancer susceptibility in order to identify novel genes that may be involved in monogenic forms of cancer predisposition.  Another major avenue of investigation examines the use of genome-scale sequencing in clinical medicine, ranging from diagnostic testing to newborn screening, to screening in healthy adults.

A growing body of work in the biomedical sciences generates and analyzes omics data; our lab’s work contributes to these efforts by focusing on the integration of different omics data types to bring mechanistic insights to the multi-scale nature of cellular processes. The focus of our research is on developing systems genomics approaches to study the impact of genomic variation on genome function. We have used this focus to study genetic and molecular variation in both natural and engineered cellular systems and approach these topics through the lens of computational biology, machine learning and advanced omics data integration. More specifically, we create methods to reveal functional relationships across genomics, transcriptomics, ribosome profiling, proteomics, structural genomics, metabolomics and phenotype variability data. Our integrative omics methods improve understanding of how cells achieve regulation at multiple scales of complexity and link to genetic and molecular variants that influence these processes. Ultimately, the goal of our research is advancing the analysis of high-throughput omics technologies to empower patient care and clinical trial selections. To this end, we are developing integrative methods to improve mutation panels by selecting more informative genetic and molecular biomarkers that match disease relevance.

Our lab is trying to understand the mechanisms by which long noncoding RNAs orchestrate the epigenetic control of gene expression. Relevant examples of this type of gene regulation occur in the case of X-chromosome inactivation and autosomal imprinting. We specialize in genomics, but rely a combination of techniques —  including genetics, proteomics, and molecular, cell and computational biology — to study these processes in both mouse and human stem and somatic cell systems.

Molecular evolution and mechanistic enzymology find powerful synergy in our study of aminoacyl-tRNA synthetases, which translate the genetic code. Class I Tryptophanyl-tRNA Synthetase stores free energy as conformational strain imposed by long-range, interactions on the minimal catalytic domain (MCD) when it binds ATP. We study how this allostery works using X-ray crystallography, bioinformatics, molecular dynamics, enzyme kinetics, and thermodynamics. As coding sequences for class I and II MCDs have significant complementarity, we also pursuing their sense/antisense ancestry. Member of the Molecular & Cellular Biophysics Training Program.

The Chung lab is engineering immune cells, particularly T cells, to achieve maximum therapeutic efficacy at the right place and timing. We explore the crossroads of synthetic biology, immunology, and cancer biology. Particularly, we are employing protein engineering, next-gen sequencing, CRISPR screening, and bioinformatics to achieve our objectives:

(1) Combinatorial recipes of transcription factors for T cell programming.

(2) Technologies for temporal regulation and/or rewiring of tumor and immune signal activation (chemokine, nuclear, inhibitor receptors).

(3) Synthetic oncolytic virus for engineering tumor-T cell crosstalk.

Our lab studies brain network connectivity in the healthy brain and in neurological and neuropsychiatric patient populations. We focus on the organizational, dynamical, and computational properties of large-scale brain networks and determine how these properties contribute to human behavior in health and disease. We strive to advance the basic understanding of brain structure and function, while making discoveries that can be translated to clinical practice.

My lab studies how genes function within the three-dimensional context of the nucleus to control development and prevent disease. We combine genomic approaches (ChIP-Seq, ChIA-PET) and genome editing tools (CRISPR) to study the epigenetic mechanisms by which transcriptional regulatory elements control gene expression in embryonic stem cells.  Our current research efforts are divided into 3 areas: 1) Mapping the folding pattern of the genome 2) Dynamics of three-dimensional genome organization as cells differentiate and 3) Functional analysis of altered chromosome structure in cancer and other diseases.

In the Ferris lab, we use genetically diverse mouse strains to better understand the role of genetic variation in immune responses to a variety of insults. We then study these variants mechanistically. We also develop genetic and genomic datasets and resources to better identify genetic features associated with these immunological differences.

Our primary research is in the area of computational systems biology, with particular interest in the study of biological signaling networks; trying to understand their structure, evolution and dynamics. In collaboration with wet lab experimentalists, we develop and apply computational models, including probabilistic graphical and multivariate methods along with more traditional engineering approaches such as system identification and control theory, to current challenges in molecular biology and medicine. Examples of recent research projects include: prediction of protein interaction networks, multivariate modeling of signal transduction networks, and development of methods for integrating large-scale genomic data sets.

Gordon-Larsen’s work integrates biology, behavior, and environment to understand, prevent and treat obesity, cardiovascular and cardiometabolic diseases. She works with biomarker, microbiome, metabolome, genetic, weight, diet, and environment data using multilevel modeling and pathway-based analyses. She works with several longitudinal cohorts that span more than 30 years. Most of her work uses data from the US and China. Her research teams include a wide variety of scientists working in areas such as genetics, medicine, bioinformatics, biostatistics, microbiology, nutrition, and epidemiology.

My group develops and deploys computational tools to predict physiological function and dysfunction. We are interested in a range of applications in medicine and biology, but our primary focus is the cardiovascular system. My group is actively developing fluid-structure interaction (FSI) models of the heart, arteries, and veins, and of cardiovascular medical devices, including bioprosthetic heart valves, ventricular assist devices, and inferior vena cava filters. We are also validating these models using in vitro and in vivo approaches. We also model cardiac electrophysiology and electro-mechanical coupling, with a focus on atrial fibrillation (AF), and aim to develop mechanistically detailed descriptions of thrombosis in AF. This work is carried out in collaboration with clinicians, engineers, computer and computational scientists, and mathematical scientists in academia, industry, and regulatory agencies.

Research in the Hicks lab focuses on development and implementation of mass spectrometric approaches for protein characterization including post-translational modifications, as well as the identification of bioactive peptides/proteins from plants. Keywords: proteins / peptides, proteomics, PTM, enzymes, analytical chemistry, mass spectrometry, separations / chromatography, plants, algae.

My research interest is in genomic characterization and integrative genomic approaches to better understand cancer. My group is part of the NCI Genome Data Analysis Center focused on RNA expression analysis. We have a number of ongoing projects including developing molecular classifications for potential clinical utility, developing methods for deconvolution to understand bulk tissue heterogeneity, analysis of driver negative cancers, and analysis of ancestry markers with cancer features.

Our preclinical research is based on the concept that drugs of abuse gain control over behavior by hijacking molecular mechanisms of neuroplasticity within brain reward circuits. Our lab focuses on three main research questions: (1) Discover the neural circuits and molecular mechanisms that mediate the reinforcing and pleasurable subjective effects of alcohol and other drugs, (2) Identify the long-term effects of cocaine and alcohol abuse during adolescence, (3) Identify novel neural targets and validate pharmacological compounds that may be used to treat problems associated with alcohol and drug abuse. The lab culture is collaborative and dynamic, innovative, and team-based. We are looking for colleagues who share an interest in understanding how alcohol hijacks reward pathways to produce addiction.

The Jones lab is interested in heterotrimeric G protein-coupled signaling and uses genetic model systems to dissect signaling networks.  The G-protein complex serves as the nexus between cell surface receptors and various downstream enzymes that ultimately alter cell behavior. Metazoans have a hopelessly complex repertoire of G-protein complexes and cell surface receptors so we turned to the reference plant, Arabidopsis thaliana, and the yeast, Saccharomyces cerevisiae, as our models because these two organisms have only two potential G protein complexes and few cell surface receptors.  Their simplicity and the ability to genetically manipulate genes in these organisms make them powerful tools.  We use a variety of cell biology approaches, sophisticated imaging techniques, 3-D protein structure analyses, forward and reverse genetic approaches, and biochemistries.

The Laederach Lab is interested in better understanding the relationship between RNA structure and folding and human disease. We use a combination of computational and experimental approaches to study the process of RNA folding and in the cells. In particular, we develop novel approaches to analyze and interpret chemical and enzymatic mapping data on a genomic scale. We aim to fundamentally understand the role of RNA structure in controlling post-transcriptional regulatory mechanisms, and to interpret structure as a secondary layer of information (http://www.nature.com/nature/journal/v505/n7485/full/505621a.html).  We are particularly interested in how human genetic variation affects RNA regulatory structure. We investigate the relationship between disease-associated Single Nucleotide Polymorphisms occurring in Human UTRs and their effect on RNA structure to determine if they form a RiboSNitch.

The Yun Li group develops statistical methods and computational tools for modern genetic, genomic, and epigenomic data. We do both method development and real data applications. The actual projects in the lab vary from year to year because I am motivated by real data problems, and genomics is arguably (few people argue with me though) THE most fascinating field with new types and huge amount of data generated at a pace more than what we can currently deal with. For current projects, please see: https://yunliweb.its.unc.edu/JobPostings.html

Statistical machine learning and data mining, nonparametric statistics and functional estimation, bioinformatics, design and analysis of experiments

Traditionally, basic science has sought to enter the translational pipeline through what can be referred to as “Bottom-Up” science, that is, studies that start with a hypothesis in the lab and aim to develop clinical relevance of the findings. In some cases, notably in conventional antibiotic development, this has worked well – but it assumes one-size fits all solutions that are only as good as our assumptions about the biology of many infectious diseases such as tuberculosis. By contrast, my research focuses on a “Top-Down” approach, leveraging the power of bacterial population genomics to identify bacterial processes important for Mtb success in people and to then employ cutting-edge experimental techniques to mechanistically dissect these processes with the goal of leveraging them using new translational tools.

In my work to date, I have applied this “Top-Down” strategy to define bacterial determinants of treatment outcomes and transmission success, as evident in first-author/corresponding author publications in prestigious journals such as Science, Nature Ecology Evolution, Cell Host Microbe, Science Advances, Genome Biology, PNAS, etc. My work combines expertise in evolutionary biology and bacterial genomics, cutting-edge bacterial genetics and high-throughput experimental phenotyping.

In my own lab, I will use these tools to (1) define the biological mechanisms that enable Mtb to survive antibiotic treatment; (2) identify bacterial determinants of TB transmission success; and (3) elucidate the evolutionary mechanisms underlying the emergence of new bacterial pathogens.

The Love Lab uses statistical models to infer biologically meaningful patterns in high-dimensional datasets, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we often collaborate with groups in the Genetics Department and the Lineberger Comprehensive Cancer Center, studying how genetic variants relevant to diseases are associated with changes in molecular and cellular phenotypes.

My research philosophy is summed up by a quote from Nobelist Albert Szent-Gyorgyi: “Discovery is to see what everybody has seen and to think what nobody has thought.” My lab studies the molecular and physical mechanisms of cell shape change during cytokinesis and tissue biogenesis during development. Specifically, we are defining how cells ensure proper alignment and sliding of cytoskeletal filaments, and determining the shape of the cell throughout division. To do so, we combine developmental biology, cell biology, biochemistry, and quantitative image analysis.

We are a biological oceanography lab that performs inquiry-based science by combining physiological and molecular approaches in laboratory isolates and natural communities to investigate how marine microorganisms are affected by their environment and in turn, influence ocean biogeochemistry and ecosystem dynamics. Particular interests include studying trace metals, such as iron, that are essential for the nutrition of phytoplankton and predicting the effects of future climate changes on phytoplankton distribution and abundance.  We implement the use of environmental genomic approaches (e.g. RNA-seq) to ascertain the ways in which marine microbes have adapted and acclimate to varying environmental conditions.

Research in the lab focuses on how a single genome gives rise to a variety of cell types and body parts during development. We use Drosophila as an experimental system to investigate (1) how transcription factors access DNA to regulate complex patterns of gene expression, and (2) how post-translational modification of histones contributes to maintenance of gene expression programs over time. We combine genomic approaches (e.g. CUT&RUN/ChIP, FAIRE/ATAC followed by high-throughput sequencing) with Drosophila genetics and transgenesis to address both of these questions.

Our laboratory is focused on translating novel molecular biomarkers into clinical oncology practice, with the overarching goal of improving the care and survival of patients with cancer. Our group is highly collaborative and applies genomic, genetic, bioinformatic, informatic, statistical, and molecular approaches. Current projects in the laboratory include:

1. Correlative genomic testing to support clinical trials
2. Expanded clinical applications of RNA sequencing
3. Development and application of cell-free circulating tumor nucleic acid assays

We identify genetic variants that influence common human traits with complex inheritance patterns, and we examine the molecular and biological mechanisms of the identified variants and the genes they affect. Currently we are investigating susceptibility to type 2 diabetes and obesity, and variation in cholesterol levels, body size, body shape, and metabolic traits. We detect allelic differences in chromatin structure and gene expression and examine gene function in human cell lines and tissues. In addition to examining the primary effects of genes, the lab is exploring the interaction of genes with environmental risk factors in disease pathogenesis. Approaches include genome-wide association studies, molecular biology, cell biology, genetic epidemiology, sequencing, and bioinformatic analysis of genome-wide data sets.

Our lab seeks to better understand the maturation and regulation of a group of human lipases.  We aim to uncover how these lipases properly fold and exit the ER, and how their activity is subsequently regulated.  We study the membrane-bound and secreted proteins that play a role in lipase regulation.  Our research can potentially impact human health as biochemical deficiencies in lipase activity can cause hypertriglyceridemia and associated disorders, such as diabetes and atherosclerosis.  We are an interdisciplinary lab and aim to address these questions using a variety of techniques, including membrane protein biochemistry, enzymology, and structural and molecular biology.

The Nguyen lab develops the next generation of effective and safe biotherapeutics for life-threatening diseases such as cancer and myocardial infarction. We engineer novel immunomodulatory carriers based on genetically encoded materials and lipids that home to the site of disease, respond to changes in the microenvironment, and effectively deliver nucleic acids and drugs.

Non-Mendelian genetics including, meiotic drive, parent-of-orifin effects and allelic exclusion.

Pecot Lab: Therapeutic RNAi to Teach Cancer how to “Heal” and Block Metastatic Biology

Synopsis: The Pecot lab is looking for eager, self-motivated students to join us in tackling the biggest problem in oncology, metastases. An estimated 90% of cancer patients die because of metastases. However, the fundamental underpinnings of what enables metastases to occur are poorly understood. The Pecot lab takes a 3-pronged approach to tackling this problem: 1) By studying the tumor microenvironment (TME), several projects are studying how cancers can be taught to “heal” themselves, 2) By studying how cancers manipulate non-coding RNAs (micro-RNAs, circle RNAs, snoRNAs, etc) to promote their metastatic spread, and 3) We are investigating several ways to develop and implement therapeutic RNA interference (RNAi) to tackle cancer-relevant pathways that are traditionally regarded as “undruggable”. Students joining the lab will be immersed in the development of novel metastatic models, modeling and studying the TME both in vitro and in vivo, using bioinformatic approaches to uncover mechanistic “roots”, and implementation of therapeutic approaches

The focus of my lab is to characterize the biological diversity of human tumors using genomics, genetics, and cell biology, and then to use this information to develop improved treatments that are specific for each tumor subtype and for each patient. A significant contribution of ours towards the goal of personalized medicine has been in the genomic characterization of human breast tumors, which identified the Intrinsic Subtypes of Breast Cancer. We study many human solid tumor disease types using multiple experimental approaches including RNA-sequencing (RNA-seq), DNA exome sequencing, Whole Genome Sequencing, cell/tissue culturing, and Proteomics, with a particular focus on the Basal-like/Triple Negative Breast Cancer subtype. In addition, we are mimicking these human tumor alterations in Genetically Engineered Mouse Models, and using primary tumor Patient-Derived Xenografts, to investigate the efficacy of new drugs and new drug combinations. All of these genomic and genetic studies generate large volumes of data; thus, a significant portion of my lab is devoted to using genomic data and a systems biology approach to create computational predictors of complex cancer phenotypes.

It is estimated that less than 2% of the human genome codes for a functional protein.  Scattered throughout the rest of the genome are regulatory regions that can exert control over genes hundreds of thousands of base pairs away through the formation of DNA loops.  These loops regulate virtually all biological functions but play an especially critical role in cellular differentiation and human development. While this phenomenon has been known for thirty years or more, only a handful of such loops have been functionally characterized.  In our lab we use a combination of cutting edge genomics (in situ Hi-C, ATAC-seq, ChIP-seq), proteomics, genome editing (CRISPR/Cas), and bioinformatics to characterize and functionally interrogate dynamic DNA looping during monocyte differentiation.  We study this process both in both healthy cells and in the context of rheumatoid arthritis and our findings have broad implications for both cell biology as well as the diagnosis and treatment of human disease.

The Popov Lab develops inventive, cutting-edge approaches to solve problems in modern computational structural biology and drug discovery. Their computational research, in collaboration with experimental screening and medicinal chemistry efforts in the Center for Integrative Chemical Biology and Drug Discovery enables the identification of novel chemical probes and drug candidates to advance understanding of biological processes.

We are interested in the links between epigenetics and gene regulation. Our primary focus is on understanding how changes to the composition of chromatin remodeling complexes are regulated, how their disruption affects their function, and contributes to disease. We focus on the SWI/SNF complex, which is mutated in 20% of all human tumors. This complex contains many variable subunits that can be assembled in combination to yield thousands of biochemically distinct complexes. We use a variety of computational and wet-lab techniques in cell culture and animal models to address these questions.

Our research is focused on RNA-binding proteins and their physiopathological roles. An understudied aspect of human disease is gene regulation by modulation of mRNA function. In our research lab we investigate functional connections between the RNA-binding protein Zinc Finger Protein 36 Like-2 (ZFP36L2 or L2) and human diseases. L2 is a member of the Tris-Tetra-Proline or Zinc Finger Protein 36 (TTP/ZFP36) family of RNA-binding proteins that bind Adenine-uridine-Rich Elements (AREs) in the 3’ untranslated regions of target mRNAs. Upon binding, L2 accelerates mRNA target degradation and/or inhibits mRNA translation, ultimately decreasing the protein encoded by the L2-target mRNA.

We have three particular goals:

• Determine new specific L2-mRNA targets involved in human diseases.
• Determine the mechanism(s) by which L2 modulates these novel RNA targets.
• Determine the physiological consequences of L2 ablation in specific cells types using mouse models and CRISPR/Cas9-mediated knockout system.

Heart failure is an increasingly prevalent cause of death world-wide, but the genetic and epigenetic underpinnings of this disease remain poorly understood. Our laboratory is interested in combining in vitro, in vivo and computational techniques to identify novel markers and predictors of a failing heart. In particular, we leverage mouse populations to perform systems-level analyses with a focus on co-expression network modeling and DNA methylation, following up in primary cell culture and CRISPR-engineered mouse lines to validate our candidate genes and identify potential molecular mechanisms of disease progression and amelioration.

We are interested in unraveling the molecular basis for human disease and discover new treatments focused on human and microbial targets. Our work extends from atomic-level studies using structural biology, through chemical biology efforts to identify new drugs, and into cellular, animal and clinical investigations. While we are currently focused on the gut microbiome, past work has examined how drugs are detected and degraded in humans, proteins designed to protect soldiers from chemical weapons, how antibiotic resistance spreads, and novel approaches to treat bacterial infections. The Redinbo Laboratory actively works to increase equity and inclusion in our lab, in science, and in the world. Our lab is centered around collaboration, open communication, and trust. We welcome and support anyone regardless of race, disability, gender identification, sexual orientation, age, financial background, or religion. We aim to: 1) Provide an inclusive, equitable, and encouraging work environment 2) Actively broaden representation in STEM to correct historical opportunity imbalances 3) Respect and support each individual’s needs, decisions, and career goals 4) Celebrate our differences and use them to discover new ways of thinking and to better our science and our community

I work on predicting the determinants of adaptive immune responses. Most of my work has focused on T-cell epitope prediction for mutant antigens derived from cancer. I have collaborated closely with clinical groups to translate this work in personalized cancer vaccine trials. More recently I have also been working on joint T-cell and B-cell prediction for viral pathogens. The technologies and techniques applied across all of my projects are at the intersection of computational immunology, genomics, and machine learning.

I am a surgeon-scientist specialized in head and neck cancers. My goal is to address translationalquestions with genomic data and bioinformatic methods, as well as benchtop experimentation. My clinical practice as a head and neck cancer surgeon also influences my research by helping me seek solutions to problems that will directly inform gaps in the current treatment protocols.

I have developed a strong interest in HPV genomics as well as HPV/host genome integrations, as these factors are intrinsically related to transcriptional diversity and patient outcomes in HPV-associated head and neck cancers. Our work has helped to demonstrate that a novel mechanism of HPV-mediated oncogenesis requiring NF-kB activation is present in nearly 50% of oropharyngeal tumors. In this vein, we are aggressively investigating the cellular interplay between the NF-kB pathway and persistent HPV infection, tumor radiation response, NRF2 signaling, and more.

Another outgrowth of this work has been investigating APOBEC3B and its non-canonical roles in regulating transcription. Our preliminary work has demonstrated that APOBEC3B has surprisingly strong transcriptional effects in HPV+ HNSCC cells and may promote oncogenesis and tumor maintenance by suppressing the innate immune response and influencing the HPV viral lifecycle.

Our group also have a strong interest in translational genomic studies. Our group is working to develop methods that will make gene expression-based biomarkers more successful in the clinic, as well as studying many aspects of genomic alterations that contribute to the development of squamous cell carcinomas.

The Schrider Lab develops and applies computational tools to use population genetic datasets to make inferences about evolutionary history. Our research areas include but are not limited to: characterizing the effects natural selection on genetic variation within species, identifying genes responsible for recent adaptation, detecting genomic copy number variants and other weird types of mutations, and adapting machine learning tools for application to questions in population genetics and evolution. Study organisms include humans, the fruit fly Drosophila melanogaster and its relatives, and the malaria vector mosquito Anopheles gambiae.

The Shiau Lab is integrating in vivo imaging, genetics, genome editing, functional genomics, bioinformatics, and cell biology to uncover and understand innate immune functions in development and disease. From single genes to individual cells to whole organism, we are using the vertebrate zebrafish model to reveal and connect mechanisms at multiple scales. Of particular interest are 1) the genetic regulation of macrophage activation to prevent inappropriate inflammatory and autoimmune conditions, and 2) how different tissue-resident macrophages impact vertebrate development and homeostasis particularly in the brain and gut, such as the role of microglia in brain development and animal behavior.

We are a computational biology lab jointly located between the department of computer science and the computational medicine program. We develop new methods for automated, efficient, and unbiased analysis of immune profiling data, such as, flow cytometry, mass cytometry, and imaging mass cytometry. Our work specifically seeks to link particular immune cell-types and their functional responses to clinical or experimental phenotypes. Application areas of interest include, vaccine development, T-cell differentiation and designing more effective immunotherapies, neurodegenerative diseases, sexually transmitted diseases, and pregnancy. To design scalable and automated tools for these data, we develop and apply new methods using machine learning and graph signal processing.

We are a lab exploring how variations in the genome change the structure and development of the brain, and in doing so, create risk for neuropsychiatric illness. We study genetic effects on multiple aspects of the human brain, from macroscale phenotypes like gross human brain structure measured with MRI to molecular phenotypes like gene expression and chromatin accessibility measured with genome-sequencing technologies. We also use neural progenitor cells as a modifiable and high fidelity model system to understand how disease-associated variants affect brain development.

I study complex traits using linkage, association, and genetic epidemiological approaches.  Disorders include schizophrenia (etiology and pharmacogenetics), smoking behavior, and chronic fatigue.

Dr. Troester’s research focuses on stromal-epithelial interactions, genomics of normal breast tissue, breast cancer microenvironment, and molecular pathology of breast cancer progression. She is a Co-Investigator on the Carolina Breast Cancer Study (CBCS), a resource including breast tumors from thousands of African American women, and she is PI of the Normal Breast Study (NBS), a unique biospecimen resource of normal tissue from women undergoing breast surgery at UNC Hospitals. Dr. Troester has extensive experience in integrating multiple high dimensional data types. She is chair of the Normal Breast Committee for the Cancer Genome Atlas Project where she is leading coordination of histology, copy number, mutation, methylation, mRNA and microRNA expression data. She has more than a decade of experience working with genomic data and molecular biology of breast cancer progression and has published many papers in the area of breast cancer subtypes, breast microenvironment, and stromal-epithelial interactions. She has trained four postdocs, 12 predoctoral students and several undergraduates.

The major area of our research is Biomolecular Informatics, which implies understanding relationships between molecular structures (organic or macromolecular) and their properties (activity or function). We are interested in building validated and predictive quantitative models that relate molecular structure and its biological function using statistical and machine learning approaches. We exploit these models to make verifiable predictions about putative function of untested molecules.

We are a quantitative genetics lab interested the relationship between genes and complex disease. Most of our work focuses on developing statistical and computational techniques for the design and analysis of genetic experiments in animal models. This includes, for example: Bayesian hierarchical modeling of gene by drug effects in crosses of inbred mouse strains; statistical methods for identifying quantitative trait loci (QTL) in a variety of experimental mouse populations (including the Collaborative Cross); computational methods for optimal design of studies on parent of origin effects; modeling of diet by gene by parentage interactions that affecting psychiatric disease; detection and estimation of genetic effects on phenotypic variability. For more information, visit the lab website.

The Vincent laboratory focuses on immunogenomics and systems approaches to understanding tumor immunobiology, with the goal of developing clinically relevant insights and new cancer immunotherapies.  Our mission is to make discoveries that help cancer patients live longer and better lives, focusing on research areas where we feel our work will lead to cures. Our core values are scientific integrity, continual growth, communication, resource stewardship, and mutual respect.

Our lab uses computational and molecular tools to study the evolution of genome organization, primarily in the flowering plants. Areas of
investigation include the origin and consequences of differences in gene order within populations and between species, the evolutionary and functional diversification of gene families (phytome.org), and the application of genomics to evolutionary model organisms (mimulusevolution.org).  We also are involved in a number of cyberinfrastructure initiatives through the National Evolutionary Synthesis Center (nescent.org), including work on digital scientific libraries (datadryad.org), open bioinformatic software development (e.g. gmod.org) and the application of semantic web technologies to biological data integration (phenoscape.org).

Our research focuses on long-read (single-molecule) sequencing and informatics. We develop novel methods to enable more efficient *omic analysis and apply carefully architected high-performance computing approaches to improve the utility of genomics in studies of human diseases, including infectious disease, cancer, and GI. Ongoing work includes genomic epidemiology of SARS-CoV-2, MPXV, and antibiotic resistance; classification of pediatric leukemias and solid tumors in low-resource settings using nanopore transcriptome sequencing; and metagenomics/metataxonomics of mucosa-associated microbiota in inflammatory bowel diseases.

We are actively engaged in multiple research arenas centered around understanding the associations between environmental exposures (primarily air pollution) and health outcomes. We use large clinical cohorts and electronic health records to understand associations between air pollution and health outcomes such as cardiovascular disease, metabolic disease, and aging. We use metabolomics and epigenetic data (primarily DNA methylation) to investigate molecular mechanisms, and highlight the integration of ‘omics data in a systems biology framework to better understand dysregulated pathways. Finally, we have projects centered around methods development and causal analyses to improve our understanding of the biology central to environmental health effects.

One of the most amazing discoveries of recent years has been the profound role of RNA in regulating all areas of biology. Further, the functions of many RNA molecules require that an RNA fold back on itself to create intricately and complexly folded structures. Until recently, however, we had little idea of the broad contributions of RNA structure and function because there simply did not exist rigorous methods for understanding RNA molecules in cells and viruses. The vision of our laboratory is therefore, first, to invent novel chemical microscopes that reveal quantitative structure and function interrelationships for RNA and, second, to apply these RNA technologies to broadly important problems in biology. Mentoring and research in the lab are highly interdisciplinary. Students learn to integrate ideas and concepts spanning chemical and computational biology, and technology development, and extending to practical applications in virology, understanding biological processes in cells, and discovery of small molecule ligands targeted against medically important RNAs. Each student has a distinct project which they drive to an impactful conclusion, but do so as part of the lab team which, collectively, has shown an amazing ability to solve big problems in RNA biology. The overarching goal of mentoring in the lab is to prepare students for long-term leadership roles in science.

Our lab uses human genetics to identify new mechanisms driving coronary artery disease (CAD). Starting with findings from genome-wide association studies (GWAS) of CAD, we identify the causal gene at a given locus, study the effect of this gene on cellular and vessel wall biology, and finally determine the molecular pathways by which this gene influences CAD risk. Within this framework, we use complex genetic mouse models and human vascular samples, single-cell transcriptomics/epigenomics and high-throughput CRISPR perturbations, as well as traditional molecular biology techniques.

We investigate mechanisms in blood coagulation and diseases that intersect with abnormal blood biomarkers and function, including cardiovascular disease (heart attack, stroke, deep vein thrombosis, pulmonary embolism), bleeding (hemophilia), inflammation, obesity, and cancer. We also investigate established drugs and new drugs in preclinical development to understand their role in reducing and preventing disease. Our studies use interdisciplinary techniques, including in vitro, ex vivo, and in vivo mouse models and samples from humans in translational studies that span clinic to bench. Our lab emphasizes a culture of diversity, responsibility, independence and collaboration, and shared excitement for scientific discovery. We are located in the UNC Blood Research Center in the newly-renovated Mary Ellen Jones building.

We try to bridge the gap between genetic risk factors for psychiatric illnesses and neurobiological mechanisms by decoding the regulatory relationships of the non-coding genome. In particular, we implement Hi-C, a genome-wide chromosome conformation capture technique to identify the folding principle of the genome in human brain. We then leverage this information to identify the functional impacts of the common variants associated with neuropsychiatric disorders.

Our group develops novel statistical bioinformatics tools and applies them in biomedical research to help understanding the precision medicine for cancer (e.g., breast cancer and lung cancer) subtypes, the disease associated integrative pathways across multiple genomic regulatory levels, and the genetics based drug repurposing mechanisms. Our recent focus includes pathway analysis, microbiome data analysis, data integration and electronica medical records (EMR). Our application fields include cancer, stem cell, autoimmune disease and oral biology. In the past, we have developed gene set testing methods with high citations, in the empirical Bayesian framework, to take care of small complex design and genewise correlation structure. These have been widely used in the microarray and RNAseq based gene expression analysis. Contamination detection for data analysis for Target DNA sequencing is work in progress. Recently, we also work on single cell sequencing data for pathway analysis with the local collaborators.

We are a translational cancer research lab. The overall goal of our research is to find therapeutic targets and biomarkers for patients with pancreatic cancer and to translate our results to the clinic. In order to accomplish this, we analyze patient tumors using a combination of genomics and proteomics to study the patient tumor and tumor microenvironment, identify and validate targets using forward and reverse genetic approaches in both patient-derived cell lines and mouse models. At the same time, we evaluate novel therapeutics for promising targets in mouse models in order to better predict clinical response in humans.