Our goal is to identify components of human nuclear RNA pathways implicated in devastating genetic diseases and cancers. To achieve this goal, we developed novel genetic screening approaches that enabled identification of human pathways acting on nuclear long non-coding RNAs. We are currently employing these approaches to conduct forward genetic screening of pathways that regulate three disease-associated nuclear lncRNAs: MALAT1, MEN-beta and KSHV PAN. Identification of components of these pathways will provide a list of potential targets for the treatment of cancer, developmental, and viral disorders.

Mendelian disease affects approximately 1 in 17 people across the globe and has so far been associated with over 4800 genes. There is a compelling need to better understand human genetic variation given that over half of patients who undergo genetic testing do not receive a positive result, limiting their access to accurate prognostics, genetic counselling, family screening and targeted therapeutics. The primary goal of this study is to better understand the missing heritability in rare genetic disease, with a particular interest in inherited retinal diseases (IRD). The workflows include: discovery and characterization of novel genes implicated in genetic disease, studying the functional impact of non-coding variants in IRD genes, and developing methods and pipelines for long-read sequencing technologies, ultimately to investigate the clinical utility of these exciting technologies in rare disease diagnostics.

The COBRE project of the Konkel lab centers upon the structural variation within genomes, with emphasis on transposable elements in humans. Transposable elements, also commonly referred to as “jumping genes,” are abundant, comprising more than 50% of the human genome. Through ongoing mobilization, transposable elements create new insertions with a rate of about 1 new insertion for every 20 live births. Furthermore, the abundance of these repetitive sequences mediates genome rearrangements, such as deletions and duplications. We are interested in better understanding the role of transposable elements on the human genome and their impact on genetic variation and adaptation.

There are an estimated 7-10 thousand known rare diseases affecting 300-400 million individuals worldwide. Approximately 80% of these have a genetic etiology. However, despite increasing availability of whole genome sequencing and other ‘omics’ technologies, rare disease diagnostic rates are unacceptably low and time to diagnosis is typically measured in years. Our goal is to alleviate the burden experienced by patients with rare diseases by accelerating rare disease research, increasing diagnostic rates, and reducing time to diagnosis. To achieve these goals, our research focuses on the development of artificial intelligence (AI) capabilities that combine structured biological knowledge with multiomic and phenotypic patient data to discover associations between phenotypes and rare variants, predict in-silico variant pathogenicity, and enable clinical decision support tools to help clinicians diagnose rare genetic disorders.

Many individuals living today are admixed, with genetic variation coming from multiple ancestral sources. Past interactions between populations can shape the genetic variants present in modern people, but how exactly allele frequencies are impacted by admixture is understudied. In this project, we will leverage demographic simulations of admixture to identify selection occurring in ancient populations over time. This work will first survey ancient Europe, where the demographic history of populations are well-known and sampled ancient individuals are abundant, to demonstrate that this method can help detect selection in ancient populations. We will also examine ancient American populations and how Indigenous ancestry was impacted by the effects of European colonization, which included admixture with European and African populations.

The gene-regulatory mechanisms controlling mitochondrial biogenesis during early development stages are not well-understood, leading to difficulties in predicting pediatric cardiomyopathies due to mitochondrial dysfunction. Our long-term goal is to understand the relationship between transcriptional regulation and metabolism in the cardiovascular system. Research conducted in our laboratory revealed that GATA4 binds to regulatory DNA regions to regulate the expression of PPARGC1A (encoding PGC-1α), which serves as an important regulator of mitochondrial biogenesis and maturation. We also demonstrated that overexpression of GATA4 can rescue mitochondrial function and biogenesis in cardiomyocytes that were impaired from cancer drug exposure; and repression of GATA4 decreased mitochondrial respiration in cardiomyocytes. Our central hypothesis is that GATA4 is involved in mitochondrial biogenesis via regulation of PPARGC1A during early cardiac development. In the proposed study, we will combine a scaffold-free 3D-cardiac organoid method with CRISPR/dCas9-interference approaches as a novel system, so as to investigate the relationship between dynamic alterations in metabolic profiles and GATA4-mediated regulatory networks during early cardiac differentiation. This study will fill the gaps in our understanding of early-onset cardiac defects from the metabolic dysregulation due to dysfunction of GATA4, and it will also improve therapeutic intervention for pediatric cardiomyopathies from metabolic disorders.

Flame retardants (FRs) are a ubiquitous group of chemicals used in furniture, car seats, and children’s products and can leach into indoor dust, resulting in chronic exposures. Epidemiological and experimental evidence shows that developmental FR exposures result in short- and long-term adverse health outcomes, but the knowledge gap remains- what targets do they attack and how do they drive adverse outcomes? Using zebrafish, our preliminary data on a brominated FR, tetrabromobisphenol A (TBBPA) shows that TBBPA exposures during early developmental windows (cleavage, blastula, early gastrula) results in developmental delays and gene-level disruption. These developmental windows encompass maternal to zygotic transition (MZT) and zygotic genome activation (ZGA), when maternally loaded mRNA degrade, and zygotic genome is activated. The overarching goal of this project is to understand the diversity of TBBPA-induced alterations in histone modifications and chromatin remodeling- key regulatory factors driving genome activation. Leveraging embryonic zebrafish as a model and a combination of ChIP, ATAC and metabolic RNA sequencing, this project will fill knowledge gaps on how environmental toxicants target early developmental events such as MZT and ZGA that set stage for plutipotency.

Multi-drug resistance (MDR) is a serious issue for late-stage cancer patients treated with chemotherapy medications. One of the ways that MDR can occur is through the overexpression of the P-glycoprotein (Pgp).  Many observed cases of MDR show resistance after exposure to the chemotherapeutic agent through somatic alterations, such as amplification or mutations, in the tumor genome. Interestingly, MDR can also occur before the initiation of chemotherapy. Researchers have not identified the reasons for the overexpression of the Pgp in chemotherapy-naïve cancer cells, but it may be linked to other environmental pressures such as statins. The primary purpose of this study is to understand the possible association between statins, non-chemotherapy medications, and MDR tumors in chemotherapy-naïve cancer patients.   To examine this connection, we will perform next-generation sequencing and PCR on clinical specimens from chemotherapy-naïve patients with colorectal tumors and the metabolomics of statin-treated cancer cells.

Recent advancements in technology, methods, and data resources have enabled great strides in the area of predicting complex, polygenic phenotypes. However, existing predictive approaches face limitations to their broad applicability, in part because they are not based on causal genotype-phenotype relationships and because they cannot account for environmental variation that impacts how traits manifest. Our project seeks to improve complex trait prediction by integrating both DNA methylation and genome sequencing data to estimate both the genetic and environmental contributions to phenotypic variation across a diverse range of human populations. Our work focuses on blood pressure traits, which are known to be significantly shaped by both environmental and genetic factors. In particular, we hope to shed light on the underlying reasons that hypertension rates show such significant disparities among ethnic groups globally