Pilot Award Projects

Morphogenesis and Virulence in Trypanosoma cruza

Trypanosoma cruzi is a neglected tropical disease that is rarely diagnosed in its acute phase, leading to severe health outcomes later in life. The parasite is currently endemic to Central and South America but is spreading north as climate change increases the range of its triatomine insect vectors. It is estimated that 6 million people in the Americas are currently infected with T. cruzi, including 600,00 people within the US.The broad aim of this proposal is to understand the molecular mechanisms that T. cruzi employs to infect its host.

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Profiling Gene Expression and Mechanophenotype in Circulating Tumor Cells Ex Vivo

Primary tumors shed circulating tumor cells (CTCs) into the bloodstream that metastasize preferentially to distant organs, resulting in 90% of cancer related fatalities. For example, estrogen receptor positive (ER+) breast cancers exhibit high rates of metastasis to bone, with decreased rates to liver and lung. CTCs exhibit heterogeneous gene expression programs and functional phenotypes, which are selected by soluble and mechanical interactions within each metastatic “niche.”

A critical challenge is to predict how patient-specific CTCs disseminate throughout the body and respond to therapeutic treatments. An exciting strategy is to culture CTCs ex vivo for drug screening informed by genomic and transcriptional profiling. We seek to elucidate how CTCs respond to different features of the metastatic niche by engineering controlled interactions with tissue specific extracellular matrix (ECM) and with human primary stromal cells, which may recapitulate disease progression and therapeutic resistance in these microenvironmental contexts. Learn More…

Neuronal Translation*

NAD Metabolism Microbiome*

Osteoarthritis*

Spine Tumors

Spatial omics*

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Host Fungal Commensals*
2024

Aging is a major risk factor for a number of diseases, including neurodegenerative diseases, diabetes, and cancer. However, the mechanisms responsible for aging remain poorly understood. Work from our lab and others has linked a particular family of proteins, known as FOXOs, to healthy aging in various species, including humans. The goal of this project is to understand how the different FOXO family members contribute to cellular maintenance in humans. Understanding the complex interplay between the different FOXOs will significantly advance our understanding of why cell and tissue function declines with age, and in the long term may lead to strategies to combat age-related diseases.

Current antibiotic therapy can lead to microbiome related complications and shifts in microbial populations that can contribute to the spread of antibiotic resistance. The work proposed here will transcriptionally profile the impacts of antibiotics on the composition and function of the oral microbiome in clinical samples. This functional information can identify therapies that more effectively utilize our current arsenal of antimicrobials in order to combat the impending antibiotic resistance crisis.

Epistasis, commonly defined as the interaction between genetic loci, has long been hypothesized to play a key role in defining the genetic architecture underlying complex traits. However, despite the recent strong evidence of pervasive epistasis in many array- and sequence-based genome-wide association studies, statistical methods for powerfully mapping epistatic effects remain in their infancy. Existing epistatic mapping methods explicitly search over all pairwise or higher-order interactions when identifying significant nonlinear effects among genome-wide variants. Consequently, due to a lack of a priori knowledge of epistatic loci and the extremely large combinatory search space these methods have to go through, existing computational approaches often suffer from low statistical power. Here, we propose to further develop upon an alternative statistical strategy for detecting epistatic effects known as the “MArginal ePistasis Test”, or MAPIT. Instead of focusing on identifying pairwise or higher-order interactions, MAPIT estimates and tests for marginal epistatic effects – the combined pairwise interaction effects between a given variant and all other variants. By testing the marginal epistatic effects, MAPIT can identify variants that are involved in epistasis without the need to explicitly search over all possible interactions. This greatly alleviates much of the statistical burden associated with epistasis mapping. The first aim of this project is to integrate the marginal epistasis method within a generalized linear model framework to analyze dichotomous traits. Here, preliminary simulation results suggest that our approach is more powerful than standard exhaustive search methods when detecting epistatic SNPs in case-control studies. The second aim is to make the model amendable to the use of summary statistics. This will allow our method to be applied to many consortium studies where individual-level genotypes and phenotypes are not accessible. The third aim is to perform rigorous data analyses on several large-scale association studies. To maximize our method’s impact on the research community, we will also produce, test, document, and distribute user-friendly software for its implementation.. Learn More…

Cholangiocarcinoma (CCA) is a devastating disease with a 5-year survival rate of 2%. The incidence of CCA has increased almost two-fold over the last three decades in the United States, and as such, there is an urgent need to find effective therapies for this lethal disease. Understanding the molecular pathogenesis of CCA development may lead to strategies for intervention and treatment. Recently, IDH1 and IDH2 mutations have been identified in about 20% of CCA patients. IDH1 and IDH2 mutations, which generate an oncometabolite–2-hydroxyglutarate (2-HG) rather than 2-oxoglutarate (2-OG), have been suggested to be the possible cause for CCA development and progression by altering epigenetic modifications. Several small molecule inhibitors (SMIs) developed aiming to target IDH1 and IDH2 mutations for suppressing 2-HG production have undergone clinical trials in treating CCA and other cancers with these mutations. Nevertheless, there is still no potential therapy for those 80% of CCA patients with wild-type (WT) IDH1/2. Additionally, it has been shown that CCA patients with IDH1/2 mutations have a better prognosis than those with WT IDH1/2, suggesting that the 2-OG-mediated pathway may be required for the CCA malignant progression in the 80% of CCA patients. Our exciting preliminary data show that TET1 is heavily involved in CCA malignant progression by targeting cell growth, apoptosis, and DNA damage in CCA cells in vitro and in vivo. With these strong preliminary data, we highly speculate the involvement of TET1 in the phenotype that CCA patients with IDH1/2 mutations have a better prognosis than those with WT IDH1/2. Thus, our central hypothesis is that the 2-HG produced by IDH1/2 mutation suppresses CCA progression by targeting TET1 enzymatic activity. We propose two aims to validate or refute our hypothesis. We propose two aims to validate or refute our hypothesis. In aim 1, we will investigate the molecular mechanisms by which TET1 modulates CCA malignant progression by using RNA sequencing and ChIP sequencing. In aim 2, we will determine how TET1 is involved in IDH1/2 mutation mediated CCA progression by using the service of Computational Biology core at the Brown University for analyzing RNA sequencing and TCGA data.

Cholangiocarcinoma (CCA) is a devastating disease with a 2% 5-year survival rate if the disease spread outside the liver, suggesting there is an urgent need to develop effective therapies. The IDH1 mutation has been identified in about 20% of CCA patients but those patients have a better prognosis than ones with wild-type IDH1. Investigating how TET1 is involved in the observed phenotype may clarify the how this occurs and identify a potential therapy for CCA patients.

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Candida species are a frequent and serious cause of bloodstream infections in the clinic. Despite access to several antifungal drugs, systemic infections are associated with mortality rates that can exceed 40%. In many patients, the organism persists in the bloodstream during antifungal treatment, despite the fact that recovered isolates are not drug resistant when tested in vitro. The mechanisms responsible for such clinical persistence are unknown, but persistence is critically associated with therapeutic failure, recurrent infection and reduced survival. Our preliminary studies indicate that persistence of Candida albicans infections is associated with the ability of isolates to exhibit cryptic growth at drug concentrations above inhibitory levels, a phenomenon now termed perseverance. We show that perseverance reflects the proportion of the population that can grow at supra-MIC drug concentrations, is concentration-independent, and does not correlate with drug resistance levels. Moreover, isolates that persist in the clinic also harbor subpopulations of cells which display transient but elevated levels of resistance, a phenomenon previously described as heteroresistance.

Here, we propose to use genetic and genomic approaches to define the mechanisms underlying perseverance and heteroresistance in C. albicans. We will first perform a genetic screen and in vitro profiling to determine how these isolates respond to antifungal challenge, and to identify the properties that enable persistent isolates to resist antifungal therapy. To complement this approach, genomics and transcriptional profiling will directly compare sets of C. albicans isolates from persistent and nonpersistent infections. Finally, genomics will also be used to define how heteroresistant cells differ from the majority of cells in the original population. Together, these studies will identify those properties that enable a subset of C. albicans strains to evade drug treatment and prolong systemic infection. These insights can then be used to establish more effective treatment regimens against this pervasive human fungal pathogen.

Gene regulatory mechanisms are critical for proper cellular and protein function, and modern molecular biology techniques have linked numerous pathologies to dysregulation of genes. However, faithful modification of the genome in studies of pathogenic mutations and associated mechanisms have been difficult, rarely leading to effective treatments. The advent of CRISPR-Cas9 (Cas9) as an inexpensive tool for genome editing has created new possibilities for therapeutic gene targeting. Cas9 has potential to permanently correct disease-linked genetic mutations and deconvolute the underlying biology, but to fully harness Cas9 function, the structural underpinnings of its catalytic mechanism must be elucidated. Cas9 utilizes guide RNA to cleave complementary double-stranded DNA upon binding of a Protospacer Adjacent Motif (PAM), a key genomic recognition sequence. Two nuclease domains within Cas9, HNH and RuvC, cleave the DNA strands and early biophysical studies suggest conformational changes within the nucleases upon RNA and DNA binding are functionally relevant. However, X-ray crystal structures offer little information about the activated state of Cas9, as the catalytic HNH domain and its target DNA strand are shown 20 Å apart. Interestingly, this HNH conformational shift is closely correlated to the enhancement of RuvC nuclease activity, suggesting these spatially separated domains are functionally coupled by an allosteric mechanism. The molecular motions associated with interdomain signaling have not been clarified, and the PI hypothesizes that allosteric communication, and ultimately nuclease function, in Cas9 is driven by structural dynamics on multiple timescales. The hypothesis will be investigated with a synergistic solution nuclear magnetic resonance (NMR) and computational approach to assess the contribution of conformational dynamics to long-range allosteric signaling in Cas9. A detailed understanding of this mechanism will facilitate greater structural control of Cas9 and help to circumvent current limitations in its genome editing power, most notably errors due to off-target nucleotide mismatches and poor temporal control of Cas9 activity. Our initial studies of Cas9 conformational dynamics will use novel constructs that facilitate interrogation of specific domains within the 160 kDa molecular machine that is nearly inaccessible with biomolecular NMR alone. Our preliminary data is the first evidence of micro-millisecond protein motions in the HNH nuclease, consistent with dynamically-regulated endonuclease activity. Foundational work completed during the funding period will implement novel computational methodologies that enhance our experimental insight to provide an atomistic glimpse of the Cas9 allosteric pathway. These results can be leveraged in subsequent expansions of the work toward targeting genes implicated in disease.

CRISPR-Cas9 has potential to modify disease-causing genes, but is prone to off-target alterations due to poor temporal control its expression. It is desirable to develop an allosterically controlled Cas9 that elicits no function unless activated, circumventing this limitation. Cas9 is reliant on conformational dynamics for allosteric function, but typical solution methods for characterizing motional ensembles, namely NMR, are pushing their limits as standalone techniques for enzymes of this size. Pairing experiments with in silico methods can elevate the level of insight from NMR alone by more precisely treating NMR data and generating dynamic structural networks that illuminate regions of allosteric crosstalk that may become functional handles for enhanced control over genome editing.

Sexually transmitted infections (STIs) remain a hidden epidemic of significant health and economic consequence in the United States and the world. Available therapies against many STIs, including HIV and HSV, do not provide a long-term cure, have significant side effects and can be cost-prohibitive. In spite of the progress in our understanding of the viral pathogenesis and immunologic mechanisms of protection with these STIs, the method of generating protective immunity through vaccination continues to be elusive. Current research on HIV vaccination is largely focused on antibody-based approaches and while CD8 T lymphocytes have a proven protective antiviral role against HIV, this has not been translated to a successful outcome in vaccine trials. Lack of an in depth understanding of the T cell biology within frontline mucosal tissues where viral replication is occurring (e.g. female reproductive tract, FRT) is a significant barrier to unlocking the full antiviral potential of T cells. Our previous studies in rodent models suggest resident memory CD8 T cells (TRM) located in the FRT are capable of eliciting rapid antiviral responses when triggered. Hence, establishing an abundant number of highly functional CD8 TRM in the FRT to mediate rapid pathogen clearance is a key long-term goal of anti-HIV vaccination programs. However, achieving sufficient quantity and quality of mucosal TRM hinges on a detailed understanding of the different types of TRM that exist in the FRT, their differentiation paths and their functional immune contribution. Our preliminary data indicate that the CD8 TRM population in the FRT is phenotypically and functionally heterogeneous. The overall objective of this pilot proposal is to use next-generation sequencing technologies to identify distinct sub-populations of TRM that are genetically and functionally distinct. Understanding this unexplored transcriptional heterogeneity is a key first step in dissecting FRT TRM differentiation that will guide efforts to generate and maintain the optimal subpopulation of TRM in the FRT needed for effective immunosurveillance.

Memory CD8 T cells are critical for protection against intracellular pathogens and tumors. Generating a robust population of memory T cells in barrier mucosal organs where they can rapidly intercept invading pathogens is a key goal of most T-cell based vaccines. This proposal will answer fundamental gaps in our current understanding of the different memory CD8 T cells present in the genital mucosa. Findings from this study will greatly improve vaccine design against pathogens that target reproductive mucosae.

In this study, we propose to use a graph-based deep learning framework to integrate information about the 3D organization of the DNA and its environment to predict gene expression. Gene regulation is the process of controlling the expression of genes to go high or low. Any disruption in this process can have severe downstream consequences and result in diseases like cancer. Promoters and regulatory elements, like enhancers and repressors, spatiotemporally participate in gene regulation. They have been shown to frequently affect the gene expression from long distances beyond the neighborhood of the transcribing gene. This long-range effect is attributed to the 3D organization of the DNA that can control access to remote gene sites. Performing a genome-wide analysis of such interactions is challenging due to the sheer size of the search space. This bottleneck requires the development of data-driven approaches to capture relevant information. Existing machine learning methods can model the local interactions between regulatory elements to predict the gene expression. However, by focusing on fixed-length regions around the genes, they fail to incorporate the potential long-range interactions that play a crucial role in gene regulation. To overcome these challenges, we propose to apply a Graph Convolutional Network (GCN), a deep learning framework, to integrate the spatial structure of the DNA with signals from regulatory elements. The overall objective of this proposal is to computationally model global (and local) gene regulation from chromatin modification and three-dimensional interaction data. This model can then be used to identify key features that determine which long-range or short-range features drive gene expression at a particular locus. Therefore, the first aim of this project is to develop the graph-based deep learning model that will input the 3D organization of the DNA (as graph G) and values of the regulatory signals (as its node features) to predict gene expression. This task will allow the model to automatically capture the relevant interactions from the data that are correlated with high/low gene expression. The second aim will be to identify these relevant interactions that are predictive of gene expression using interpretation methods. These methods aim to explain which input features are important for a given prediction. Finally, for the third aim, we will validate these learned relevant features using literature survey as well as biological experiments.

Our proposed deep learning framework will capture and identify the important long-range regulatory interactions from the data. A differential analysis of these interactions between healthy and diseased cells will be essential for comprehensively understanding cell development and misregulation in human diseases.