Faculty Bibliography

The ability to monitor DNA replication fork directionality at the genome-wide scale is paramount for a greater understanding of how genetic and environmental perturbations can impact replication dynamics in human cells. Here we describe a detailed protocol for isolating and sequencing Okazaki fragments from asynchronously growing mammalian cells, termed Okazaki fragment sequencing (Ok-seq), for the purpose of quantitatively determining replication initiation and termination frequencies around specific genomic loci by meta-analyses. Briefly, cells are pulsed with 5-ethynyl-2'-deoxyuridine (EdU) to label newly synthesized DNA, and collected for DNA extraction. After size fractionation on a sucrose gradient, Okazaki fragments are concentrated and purified before click chemistry is used to tag the EdU label with a biotin conjugate that is cleavable under mild conditions. Biotinylated Okazaki fragments are then captured on streptavidin beads and ligated to Illumina adapters before library preparation for Illumina sequencing. The use of Ok-seq to interrogate genome-wide replication fork initiation and termination efficiencies can be applied to all unperturbed, asynchronously growing mammalian cells or under conditions of replication stress, and the assay can be performed in less than 2 weeks.

Cellular processes are largely carried out by macromolecular assemblies, most of which are dynamic, having components that are in constant flux. One such assembly is the nuclear pore complex (NPC), an ∼50 MDa assembly comprised of ∼30 different proteins called Nups that mediates selective macromolecular transport between the nucleus and cytoplasm. We developed a proteomics method to provide a comprehensive picture of the yeast NPC component dynamics. We discovered that, although all Nups display uniformly slow turnover, their exchange rates vary considerably. Surprisingly, this exchange rate was relatively unrelated to each Nup's position, accessibility, or role in transport but correlated with its structural role; scaffold-forming Nups exchange slowly, whereas flexible connector Nups threading throughout the NPC architecture exchange more rapidly. Targeted perturbations in the NPC structure revealed a dynamic resilience to damage. Our approach opens a new window into macromolecular assembly dynamics.

Staphylococcus aureus, a common cause of serious and often fatal infections, is well-armed with secreted factors that disarm host immune defenses. Highly expressed in vivo during infection, Staphylococcal protein A (SpA) is reported to also contribute to nasal colonization that can be a prelude to invasive infection. Co-evolution with the host immune system has provided SpA with an Fc-antibody binding site, and a Fab-binding site responsible for non-immune superantigen interactions via germline-encoded surfaces expressed on many human BCRs. We wondered whether the recurrent exposures to S. aureus commonly experienced by adults, result in the accumulation of memory B-cell responses to other determinants on SpA. We therefore isolated SpA-specific class-switched memory B cells, and characterized their encoding VH : VL antibody genes. In SpA-reactive memory B cells, we confirmed a striking bias in usage for VH genes, which retain the surface that mediates the SpA-superantigen interaction. We postulate these interactions reflect co-evolution of the host immune system and SpA, which during infection results in immune recruitment of an extraordinarily high prevalence of B cells in the repertoire that subverts the augmentation of protective defenses. Herein, we provide the first evidence that human memory responses are supplemented by B-cell clones, and circulating-antibodies, that bind to SpA determinants independent of the non-immune Fc- and Fab-binding sites. In parallel, we demonstrate that healthy individuals, and patients recovering from S. aureus infection, both have circulating antibodies with these conventional binding specificities. These findings rationalize the potential utility of incorporating specially engineered SpA proteins into a protective vaccine.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.

A 5',7-methylguanosine cap is a quintessential feature of RNA polymerase II-transcribed RNAs, and a textbook aspect of co-transcriptional RNA processing. The cap is bound by the cap-binding complex (CBC), canonically consisting of nuclear cap-binding proteins 1 and 2 (NCBP1/2). Interest in the CBC has recently renewed due to its participation in RNA-fate decisions via interactions with RNA productive factors as well as with adapters of the degradative RNA exosome. A novel cap-binding protein, NCBP3, was recently proposed to form an alternative CBC together with NCBP1, and to interact with the canonical CBC along with the protein SRRT. The theme of post-transcriptional RNA fate, and how it relates to co-transcriptional ribonucleoprotein assembly, is abundant with complicated, ambiguous, and likely incomplete models. In an effort to clarify the compositions of NCBP1-, 2- and 3-related macromolecular assemblies, we have applied an affinity capture-based interactome screen where the experimental design and data processing have been modified to quantitatively identify interactome differences between targets under a range of experimental conditions. This study generated a comprehensive view of NCBP-protein interactions in the ribonucleoprotein context and demonstrates the potential of our approach to benefit the interpretation of complex biological pathways.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

BACKGROUND:The COVID-19 pandemic has resulted in significant morbidity and mortality, with large numbers of patients requiring intensive care threatening to overwhelm healthcare systems globally. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. OBJECTIVE:The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. METHODS:Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, non-laboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts probability of mortality using biomarker measurements (CRP, PCT, D-dimer) and age. Both Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China comprising 160 and 375 patients, respectively. RESULTS:All biomarkers were measured at significantly higher levels in patients that died vs. those that were not hospitalized or discharged (P < .001). The Tier 1 and Tier 2 internal validation had AUC (95% confidence interval) of 0.79 (0.74-0.84) and 0.95 (0.92-0.98), respectively. The Tier 1 and Tier 2 external validation had AUCs of 0.79 (0.74-0.84) and 0.97 (0.95-0.99), respectively. CONCLUSIONS:Our results demonstrate validity of the clinical decision support system and mobile app, which are now ready to assist healthcare providers in making evidence-based decisions in managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics, sites whereby application of such tools could lead to improvements in patient outcomes and cost containment. CLINICALTRIAL/UNASSIGNED/:

Osteoarthritis (OA) affects nearly 10% of the population of the United States and other industrialized countries and, at present, short of surgical joint replacement, there is no therapy available that can reverse the progression of the disease. Adenosine, acting at its A2A receptor (A2AR), is a critical autocrine factor for maintenance of cartilage homeostasis and here we report that injection of liposomal suspensions of either adenosine or a selective A2AR agonist, CGS21680, significantly reduced OA cartilage damage in a murine model of obesity-induced OA. The same treatment also improved swelling and preserved cartilage in the affected knees in a rat model of established post-traumatic OA (PTOA). Differential expression analysis of mRNA from chondrocytes harvested from knees of rats with PTOA treated with liposomal A2AR agonist revealed downregulation of genes associated with matrix degradation and upregulation of genes associated with cell proliferation as compared to liposomes alone. Studies in vitro and in affected joints demonstrated that A2AR ligation increased the nuclear P-SMAD2/3/P-SMAD1/5/8 ratio, a change associated with repression of terminal chondrocyte differentiation. These results strongly suggest that targeting the A2AR is an effective approach to treat OA.

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.