Faculty Bibliography

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Myeloid cells are a vital component of innate immunity and comprise monocytes, macrophages, dendritic cells, and granulocytes. How myeloid cell lineage affects activation states in response to cytokines remains poorly understood. The cytokine environment and cellular infiltrate during an inflammatory response may contain prognostic features that predict disease outcome. In this study, we analyzed the transcriptional responses of human monocytes, macrophages, dendritic cells, and neutrophils in response to stimulation by IFN-γ, IFN-β, IFN-λ, IL-4, IL-13, and IL-10 cytokines to better understand the heterogeneity of activation states in inflammatory conditions. This generated a myeloid cell-cytokine-specific response matrix that can infer representation of myeloid cells and the cytokine environment they encounter during infection, in tumors and in whole blood. Neutrophils were highly responsive to type 1 and type 2 cytokine stimulation but did not respond to IL-10. We identified transcripts specific to IFN-β stimulation, whereas other IFN signature genes were upregulated by both IFN-γ and IFN-β. When we used our matrix to deconvolute blood profiles from tuberculosis patients, the IFN-β-specific neutrophil signature was reduced in tuberculosis patients with active disease, whereas the shared response to IFN-γ and IFN-β in neutrophils was increased. When applied to glioma patients, transcripts of neutrophils exposed to IL-4/IL-13 and monocyte responses to IFN-γ or IFN-β emerged as opposing predictors of patient survival. Hence, by dissecting how different myeloid cells respond to cytokine activation, we can delineate biological roles for myeloid cells in different cytokine environments during disease processes, especially during infection and tumor progression.

MOTIVATION/BACKGROUND:Retrotransposition is an important force in shaping the human genome and is involved in prenatal development, disease, and aging. Current genome browsers are not optimized for visualizing the experimental evidence for retrotransposon insertions. RESULTS:We have developed a specialized browser to visualize the evidence for retrotransposon insertions for both targeted and whole genome sequencing data. AVAILABILITY/BACKGROUND:TranspoScope's source code, as well as installation instructions, are available at https://github.com/FenyoLab/transposcope.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Long interspersed element-1 (LINE-1, L1) sequences, which comprise about 17% of human genome, are the product of one of the most active types of mobile DNAs in modern humans. LINE-1 insertion alleles can cause inherited and de novo genetic diseases, and LINE-1-encoded proteins are highly expressed in some cancers. Genome-wide LINE-1 mapping in single cells could be useful for defining somatic and germline retrotransposition rates, and for enabling studies to characterize tumour heterogeneity, relate insertions to transcriptional and epigenetic effects at the cellular level, or describe cellular phylogenies in development. Our laboratories have reported a genome-wide LINE-1 insertion site mapping method for bulk DNA, named transposon insertion profiling by sequencing (TIPseq). There have been significant barriers applying LINE-1 mapping to single cells, owing to the chimeric artefacts and features of repetitive sequences. Here, we optimize a modified TIPseq protocol and show its utility for LINE-1 mapping in single lymphoblastoid cells. Results from single-cell TIPseq experiments compare well to known LINE-1 insertions found by whole-genome sequencing and TIPseq on bulk DNA. Among the several approaches we tested, whole-genome amplification by multiple displacement amplification followed by restriction enzyme digestion, vectorette ligation and LINE-1-targeted PCR had the best assay performance. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.

MOTIVATION/BACKGROUND:LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify LINE-1 RNA at specific loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA. RESULTS:We provide a tool, L1EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each genomic locus, separating transcripts that are capable of generating retrotransposition from those that are not. We show the accuracy of L1EM on simulated data and against long read sequencing from HEK cells. AVAILABILITY/BACKGROUND:L1EM is written in python. The source code along with the necessary annotations are available at https://github.com/FenyoLab/L1EM and distributed under GPLv3. SUPPLEMENTARY INFORMATION/BACKGROUND:Supplementary data are available at Bioinformatics online.

Long interspersed element-1 (LINE-1, or L1) is the only autonomous retrotransposon that is active in human cells. Different host factors have been shown to influence L1 mobility; however, systematic analyses of these factors are limited. Here, we developed a high-throughput microscopy-based retrotransposition assay that identified the double-stranded break (DSB) repair and Fanconi anemia (FA) factors active in the S/G2 phase as potent inhibitors and regulators of L1 activity. In particular, BRCA1, an E3 ubiquitin ligase with a key role in several DNA repair pathways, directly affects L1 retrotransposition frequency and structure and plays a distinct role in controlling L1 ORF2 protein translation through L1 mRNA binding. These results suggest the existence of a 'battleground' at the DNA replication fork between homologous recombination (HR) factors and L1 retrotransposons and reveal a potential role for L1 in the genotypic evolution of tumors characterized by BRCA1 and HR repair deficiencies.

LINE-1 retrotransposon overexpression is a hallmark of human cancers. We identified a colorectal cancer wherein a fast-growing tumor subclone downregulated LINE-1, prompting us to examine how LINE-1 expression affects cell growth. We find that nontransformed cells undergo a TP53-dependent growth arrest and activate interferon signaling in response to LINE-1. TP53 inhibition allows LINE-1⁺ cells to grow, and genome-wide-knockout screens show that these cells require replication-coupled DNA-repair pathways, replication-stress signaling and replication-fork restart factors. Our findings demonstrate that LINE-1 expression creates specific molecular vulnerabilities and reveal a retrotransposition-replication conflict that may be an important determinant of cancer growth.