Faculty Bibliography

Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes. By comparing differential protein levels between metastatic, primary tumor, and nonmalignant samples in two of the cohorts, we identified 274 proteins showing significant differences among the sample types. Further analysis of these 274 proteins in lymph node metastatic samples from a third cohort revealed that 45 proteins exhibited a significant effect on patient survival. The three most significant proteins were HP (HR = 4.67, p = 2.8e-06), LGALS7 (HR = 3.83, p = 2.9e-05), and UBQLN1 (HR = 3.2, p = 4.8e-05). The user-friendly interactive web platform, accessible at https://www.tnmplot.com/melanoma, provides an interactive interface for the analysis of proteomic and clinical data. The MEL-PLOT platform, through its interactive capabilities, streamlines the creation of a comprehensive knowledge base, empowering hypothesis formulation and diligent monitoring of the most recent advancements in the domains of biomedical research and drug development.

Human endogenous retroviruses (HERVs) occupy a large portion of the human genome. Most HERVs are transcriptionally silent, but they can be reactivated during pathological states such as viral infection and certain cancers. The HERV-K HML-2 clade includes elements that recently integrated have in the human germ line and often contain intact open reading frames that possibly support peptide and protein expression. Understanding HERV-K-host interactions and their potential as biomarkers is problematic due to the high similarity among different elements. Previously, we described a long-read single molecule real-time sequencing (PacBio) strategy to analyze HERV-K RNA expression profiles in different cell types. However, identifying HERV-K HML-2 proteins accurately is difficult without robust and reliable methods and reagents. Here we present a new approach to characterize the HML-2 elements that (a) are being translated and (b) produce enough protein to be detected and identified by mass spectrometry. Our data reveal that RNA expression profiling alone cannot accurately predict which HML-2 elements are responsible for protein production, as we observe several differences between the highest expressed RNAs and the elements that are the predominant source of HERV-K HML-2 protein synthesis. These studies represent an important advance toward untangling the complexity of HERV-K-host interactions.

GPR133 (ADGRD1) is an adhesion G-protein-coupled receptor that signals through Gαs/cyclic AMP (cAMP) and is required for the growth of glioblastoma (GBM), an aggressive brain malignancy. The regulation of GPR133 signaling is incompletely understood. Here, we use proximity biotinylation proteomics to identify ESYT1, a Ca²⁺-dependent mediator of endoplasmic reticulum-plasma membrane bridge formation, as an intracellular interactor of GPR133. ESYT1 knockdown or knockout increases GPR133 signaling, while its overexpression has the opposite effect, without altering GPR133 levels in the plasma membrane. The GPR133-ESYT1 interaction requires the Ca²⁺-sensing C2C domain of ESYT1. Thapsigargin-mediated increases in cytosolic Ca²⁺ relieve signaling-suppressive effects of ESYT1 by promoting ESYT1-GPR133 dissociation. ESYT1 knockdown or knockout in GBM slows tumor growth, suggesting tumorigenic functions of ESYT1. Our findings demonstrate a mechanism for the modulation of GPR133 signaling by increased cytosolic Ca²⁺, which reduces the signaling-suppressive interaction between GPR133 and ESYT1 to raise cAMP levels.

INTRODUCTION/UNASSIGNED:Biomarker discovery is increasingly moving from single omics to multiomics, as well as from multi-cell omics to single-cell omics. These transitions have increasingly adopted digital transformation technologies to accelerate the progression from data to insight. Here, we will discuss the concept of 'digitalomics' and how digital transformation directly impacts biomarker discovery. This will ultimately assist clinicians in personalized therapy and precision-medicine treatment decisions. AREAS COVERED/UNASSIGNED:Genotype-to-phenotype-based insight generation involves integrating large amounts of complex multiomic data. This data integration and analysis is aided through digital transformation, leading to better clinical outcomes. We also highlight the challenges and opportunities of Digitalomics, and provide examples of the application of Artificial Intelligence, cloud- and high-performance computing, and use of tensors for multiomic analysis workflows. EXPERT OPINION/UNASSIGNED:Biomarker discovery, aided by digital transformation, is having a significant impact on cancer, cardiovascular, infectious, immunological, and neurological diseases, among others. Data insights garnered from multiomic analyses, combined with patient meta data, aids patient stratification and targeted treatment across a broad spectrum of diseases. Digital transformation offers time and cost savings while leading to improved patent healthcare. Here, we highlight the impact of digital transformation on multiomics- based biomarker discovery with specific applications related to oncology.

OBJECTIVE:Whereas genetic susceptibility for systemic lupus erythematosus (SLE) has been well explored, the triggers for clinical disease flares remain elusive. To investigate relationships between microbiota community resilience and disease activity, we performed the first longitudinal analyses of lupus gut-microbiota communities. METHODS:In an observational study, taxononomic analyses, including multivariate analysis of ß-diversity, assessed time-dependent alterations in faecal communities from patients and healthy controls. From gut blooms, strains were isolated, with genomes and associated glycans analysed. RESULTS:(RG) occurred at times of high-disease activity, and were detected in almost half of patients during lupus nephritis (LN) disease flares. Whole genome sequence analysis of RG strains isolated during these flares documented 34 genes postulated to aid adaptation and expansion within a host with an inflammatory condition. Yet, the most specific feature of strains found during lupus flares was the common expression of a novel type of cell membrane-associated lipoglycan. These lipoglycans share conserved structural features documented by mass spectroscopy, and highly immunogenic repetitive antigenic-determinants, recognised by high-level serum IgG2 antibodies, that spontaneously arose, concurrent with RG blooms and lupus flares. CONCLUSIONS:Our findings rationalise how blooms of the RG pathobiont may be common drivers of clinical flares of often remitting-relapsing lupus disease, and highlight the potential pathogenic properties of specific strains isolated from active LN patients.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

PURPOSE/OBJECTIVE:Long interspersed nuclear element-1 (LINE-1 or L1) comprises 17% of the human genome. Retrotransposons may perturb gene integrity or alter gene expression by altering regulatory regions in the genome. The germline employs a number of mechanisms, including cytosine methylation, to repress retrotransposon transcription throughout most of life. Demethylation during germ cell and early embryo development de-represses retrotransposons. Intriguingly, de novo genetic variation appearing in sperm has been implicated in a number of disorders in offspring, including autism spectrum disorder, schizophrenia, and bipolar disorder. We hypothesize that human sperm exhibit de novo retrotransposition and employ a new sequencing method, single cell transposon insertion profiling by sequencing (scTIPseq) to map them in small amounts of human sperm. METHODS:Cross-sectional case-control study of sperm samples (n=10 men; ages 32-55 years old) from consenting men undergoing IVF at NYU Langone Fertility Center. scTIPseq identified novel LINE-1 insertions in individual sperm and TIPseqHunter, a custom bioinformatics pipeline, compared the architecture of sperm LINE-1 to known LINE-1 insertions from the European database of Human specific LINE-1 (L1Hs) retrotransposon insertions (euL1db). RESULTS:scTIPseq identified 17 novel insertions in sperm. New insertions were mainly intergenic or intronic. Only one sample did not exhibit new insertions. The location or number of novel insertions did not differ by paternal age. CONCLUSION/CONCLUSIONS:This study for the first time reports novel LINE-1 insertions in human sperm, demonstrating the feasibility of scTIPseq, and identifies new contributors to genetic diversity in the human germ line.

The adhesion G-protein-coupled receptor GPR133 (ADGRD1) supports growth of the brain malignancy glioblastoma. How the extracellular interactome of GPR133 in glioblastoma modulates signaling remains unknown. Here, we use affinity proteomics to identify the transmembrane protein PTK7 as an extracellular binding partner of GPR133 in glioblastoma. PTK7 binds the autoproteolytically generated N-terminal fragment of GPR133 and its expression in trans increases GPR133 signaling. This effect requires the intramolecular cleavage of GPR133 and PTK7's anchoring in the plasma membrane. PTK7's allosteric action on GPR133 signaling is additive with but topographically distinct from orthosteric activation by soluble peptide mimicking the endogenous tethered Stachel agonist. GPR133 and PTK7 are expressed in adjacent cells in glioblastoma, where their knockdown phenocopies each other. We propose that this ligand-receptor interaction is relevant to the pathogenesis of glioblastoma and possibly other physiological processes in healthy tissues.

UNLABELLED:. Surprisingly, compared with other genetic subsets, murine and human LKB1-mutant NSCLC show marked overexpression of the NAD+-catabolizing ectoenzyme, CD38 on the surface of tumor cells. Loss of LKB1 or inactivation of Salt-Inducible Kinases (SIKs)-key downstream effectors of LKB1- induces CD38 transcription induction via a CREB binding site in the CD38 promoter. Treatment with the FDA-approved anti-CD38 antibody, daratumumab, inhibited growth of LKB1-mutant NSCLC xenografts. Together, these results reveal CD38 as a promising therapeutic target in patients with LKB1 mutant lung cancer. SIGNIFICANCE/CONCLUSIONS:tumor suppressor of lung adenocarcinoma patients and are associated with resistance to current treatments. Our study identified CD38 as a potential therapeutic target that is highly overexpressed in this specific subtype of cancer, associated with a shift in NAD homeostasis.

GPR133 (ADGRD1) is an adhesion G protein-coupled receptor that signals through Gαs and is required for growth of glioblastoma (GBM), an aggressive brain malignancy. The regulation of GPR133 signaling is incompletely understood. Here, we use proximity biotinylation proteomics to identify ESYT1, a Ca²⁺-dependent mediator of endoplasmic reticulum-plasma membrane bridge formation, as an intracellular interactor of GPR133. ESYT1 knockdown or knockout increases GPR133 signaling, while its overexpression has the opposite effect, without altering GPR133 levels in the plasma membrane. The GPR133-ESYT1 interaction requires the Ca²⁺-sensing C2C domain of ESYT1. Thapsigargin-mediated increases in cytosolic Ca²⁺ relieve signaling-suppressive effects of ESYT1 by promoting ESYT1-GPR133 dissociation. ESYT1 knockdown or knockout in GBM impairs tumor growth in vitro, suggesting functions of ESYT1 beyond the interaction with GPR133. Our findings suggest a novel mechanism for modulation of GPR133 signaling by increased cytosolic Ca²⁺, which reduces the signaling-suppressive interaction between GPR133 and ESYT1 to raise cAMP levels.