Acute Effects of Growth Hormone on the Cellular Immunologic Landscape in Pediatric Patients.

INTRODUCTION: Growth hormone (GH) and the immune system have multiple bidirectional interactions. Data about the acute effects of GH on the immune system are lacking. The objective of our study was to evaluate the acute effects of GH on the immune system using time-of-flight mass cytometry. METHODS: This was a prospective study of pediatric patients who were being evaluated for short stature and underwent a GH stimulation test at a tertiary care center. Blood samples for immunologic markers, i.e., complete blood count (CBC) and time of flight mass cytometry (CyTOF), were collected at baseline (T0) and over the course of three hours (T3) of the test. Differences in immune profiling in patients by timepoint (T0, T3) and GH response (growth hormone sufficient (GHS) versus growth hormone deficient (GHD)) were calculated using a two-way ANOVA test.  Results: A total of 54 patients (39 boys and 15 girls) aged five to 18 years were recruited. Twenty-two participants tested GHD (peak GH <10 ng/ml). The CyTOF analysis showed a significant increase from T0 to T3 in granulocyte percentage, monocyte count, and dendritic cell (DC) count; in contrast, a significant decrease was seen in T lymphocytes (helper and cytotoxic) and IgD+ B lymphocytes. The CBC analysis supported these findings: an increase in total white blood cell count, absolute neutrophil count, and neutrophil percentage; a decrease in absolute lymphocyte count, lymphocyte percentage, absolute eosinophil count, and absolute monocyte count. No significant differences were found between CBC/CyTOF measurements and GH status at either time. CONCLUSIONS: This study provides the first high-resolution map of acute changes in the immune system with GH stimulation. This implies a key role for GH in immunomodulatory function.

Cancer-associated fibroblasts are the main contributors to epithelial-to-mesenchymal signatures in the tumor microenvironment.

Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature (COL1A1, COL1A2, COL3A1) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.

Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.

Federated learning is becoming increasingly more popular as the concern of privacy breaches rises across disciplines including the biological and biomedical fields. The main idea is to train models locally on each server using data that are only available to that server and aggregate the model (not data) information at the global level. While federated learning has made significant advancements for machine learning methods such as deep neural networks, to the best of our knowledge, its development in sparse Bayesian models is still lacking. Sparse Bayesian models are highly interpretable with natural uncertain quantification, a desirable property for many scientific problems. However, without a federated learning algorithm, their applicability to sensitive biological/biomedical data from multiple sources is limited. Therefore, to fill this gap in the literature, we propose a new Bayesian federated learning framework that is capable of pooling information from different data sources without breaching privacy. The proposed method is conceptually simple to understand and implement, accommodates sampling heterogeneity (i.e., non-iid observations) across data sources, and allows for principled uncertainty quantification. We illustrate the proposed framework with three concrete sparse Bayesian models, namely, sparse regression, Markov random field, and directed graphical models. The application of these three models is demonstrated through three real data examples including a multi-hospital COVID-19 study, breast cancer protein-protein interaction networks, and gene regulatory networks.

Osr1 Regulates Macrophage-mediated Liver Inflammation in Nonalcoholic Fatty Liver Disease Progression.

BACKGROUND & AIMS: Liver macrophage-mediated inflammation contributes to the pathogenesis of the nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH). Odd skipped-related 1 (Osr1) is a putative transcription factor previously reported to be involved in NASH progression; however, the underlying mechanisms remain unknown. The current study focused on the role of Osr1 in macrophage polarization and metabolism and its associated functions in the inflammation-induced pathogenesis of NASH. METHODS: OSR1/Osr1 expression patterns were compared in normal and NASH patients and mouse livers. NASH was established and compared between hepatocyte-specific Osr1 knockout (Osr1ΔHep), macrophage-specific Osr1 knockout (Osr1ΔMφ), and wild-type (Osr1F) mice fed with 3 different chronic obesogenic diets and methionine choline-deficient diet. Using genetic and therapeutic strategies in vitro and in vivo, the downstream targets of Osr1 and the associated mechanisms in inflammation-induced NASH were established. RESULTS: Osr1 was expressed in both hepatocytes and macrophages and exhibited different expression patterns in NASH. In NAFLD and NASH murine models, deleting Osr1 in myeloid cells (Osr1ΔMφ), but not hepatocytes, aggravated steatohepatitis with pronounced liver inflammation. Myeloid Osr1 deletion resulted in a polarization switch toward a pro-inflammatory phenotype associated with reduced oxidative phosphorylation activity. These inflamed Osr1ΔMφ macrophages promoted steatosis and inflammation in hepatocytes via cytokine secretion. We identified 2 downstream transcriptional targets of Osr1, c-Myc, and PPARγ and established the Osr1-PPARγ cascade in macrophage polarization and liver inflammation by genetic study and rosiglitazone treatment in vivo. We tested a promising intervention strategy targeting Osr1-PPARγ by AAV8L-delivered Osr1 expression or rosiglitazone that significantly repressed NAFLD/NASH progression in Osr1F and Osr1ΔMφ mice. CONCLUSIONS: Myeloid Osr1 mediates liver immune homeostasis and disrupting Osr1 aggravates the progression of NAFLD/NASH.

Medication adherence, utilization of healthcare services, and mortality of patients with epilepsy on opiate replacement therapy: A retrospective cohort study.

INTRODUCTION: Substance misuse is not uncommonly recognized in people with epilepsy (PWE). Mortality is significantly greater in those with comorbid substance misuse, but it remains unclear whether epilepsy care and management contribute to this. This cohort study aimed to compare the rates of mortality in PWE receiving opiate replacement therapy (ORT) and PWE alone, as well as evaluate their medication adherence, levels of engagement with epilepsy services as currently delivered, and utilization of unscheduled hospital care. MATERIAL AND METHODS: A 5-year historical cohort for PWE was identified and manually validated using electronic patient records registered with NHS Tayside. Overall incidence rates for mortality and contact with emergency health care services were calculated for PWE receiving ORT and PWE alone. Engagement with outpatient epilepsy services was also noted. Adherence to antiepileptic drugs (AEDs) was expressed in terms of medication possession ratio (MPR). RESULTS: Of the 1297 PWE attending a tertiary care epilepsy service, 68 (5.3%) PWE were receiving ORT. The mortality rate was significantly greater in PWE on ORT in comparison to PWE only (7.4% vs 1.7 %; P < 0.05; relative risk of death: 4.34, 95% CI 1.19-15.7), as well as the incidence of emergency healthcare services contact being higher (24.5% vs 17.7%; P < 0.05; incidence rate ratio: 1.39, 95% CI: 1.12-1.71). Poor adherence to AEDs was also more common in PWE on ORT (28.4% vs 23.5%; P = 0.02), as well as failure to engage with elective outpatient services (8.4% vs 3.0%; P < 0.05; rate ratio 2.77, 95% CI: 1.86-4.1). CONCLUSION: People with epilepsy on ORT are less likely to engage with elective epilepsy services as currently delivered or take AEDs as prescribed despite most of these patients having daily attendance at a community pharmacist. This may contribute to the significantly increased rates of mortality and unscheduled hospital care. Clinicians and policymakers should consider service redesign to meet the demands of this high-risk population in an attempt to reduce mortality and morbidity.

Systematic Analysis of Environmental Chemicals That Dysregulate Critical Period Plasticity-Related Gene Expression Reveals Common Pathways That Mimic Immune Response to Pathogen.

The tens of thousands of industrial and synthetic chemicals released into the environment have an unknown but potentially significant capacity to interfere with neurodevelopment. Consequently, there is an urgent need for systematic approaches that can identify disruptive chemicals. Little is known about the impact of environmental chemicals on critical periods of developmental neuroplasticity, in large part, due to the challenge of screening thousands of chemicals. Using an integrative bioinformatics approach, we systematically scanned 2001 environmental chemicals and identified 50 chemicals that consistently dysregulate two transcriptional signatures of critical period plasticity. These chemicals included pesticides (e.g., pyridaben), antimicrobials (e.g., bacitracin), metals (e.g., mercury), anesthetics (e.g., halothane), and other chemicals and mixtures (e.g., vehicle emissions). Application of a chemogenomic enrichment analysis and hierarchical clustering across these diverse chemicals identified two clusters of chemicals with one that mimicked an immune response to pathogen, implicating inflammatory pathways and microglia as a common chemically induced neuropathological process. Thus, we established an integrative bioinformatics approach to systematically scan thousands of environmental chemicals for their ability to dysregulate molecular signatures relevant to critical periods of development.

Lyme Disease Patient Trajectories Learned from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response.

Lyme disease (LD) is the most common tick-borne illness in the United States. Although appropriate antibiotic treatment is effective for most cases, up to 20% of patients develop post-treatment Lyme disease syndrome (PTLDS). There is an urgent need to improve clinical management of LD using precise understanding of disease and patient stratification. We applied machine-learning to electronic medical records to better characterize the heterogeneity of LD and developed predictive models for identifying medications that are associated with risks of subsequent comorbidities. For broad disease categories, we identified 3, 16, and 17 comorbidities within 2, 5, and 10 years of diagnosis, respectively. At a higher resolution of ICD-9 codes, we identified known associations with LD including chronic pain and cognitive disorders, as well as particular comorbidities on a timescale that matched PTLDS symptomology. We identified 7, 30, and 35 medications associated with risks of these comorbidities within 2, 5, and 10 years, respectively. For instance, the first-line antibiotic doxycycline exhibited a consistently protective association for typical symptoms of LD, including backache. Our approach and findings may suggest new hypotheses for more personalized treatments regimens for LD patients.

High-Throughput Identification of the Plasma Proteomic Signature of Inflammatory Bowel Disease.

BACKGROUND: The molecular aetiology of inflammatory bowel disease [IBD] and its two subtypes, ulcerative colitis [UC] and Crohn's disease [CD], have been carefully investigated at genome and transcriptome levels. Recent advances in high-throughput proteome quantification has enabled comprehensive large-scale plasma proteomics studies of IBD. METHODS: The study used two cohorts: [1] The CERTIFI-cohort: 42 samples from the CERTIFI trial of anti-TNFα-refractory CD patients; [2] the PROgECT-UNITI-HCs cohort: 46 UC samples of the PROgECT study, 84 CD samples of the UNITI I and UNITI II studies, and 72 healthy controls recruited in Mount Sinai Hospital, New York, USA. The plasma proteome for these two cohorts was quantified using high-throughput platforms. RESULTS: For the PROgECT-UNITI-HCs cohort, we measured a total of 1310 proteins. Of these, 493 proteins showed different plasma levels in IBD patients to the plasma levels in controls at 10% false discovery rate [FDR], among which 11 proteins had a fold change greater than 2. The proteins upregulated in IBD were associated with immunity functionality, whereas the proteins downregulated in IBD were associated with nutrition and metabolism. The proteomic profiles were very similar between UC and CD. In the CERTIFI cohort, 1014 proteins were measured, and it was found that the plasma protein level had little correlation with the blood or intestine transcriptomes. CONCLUSIONS: We report the largest proteomics study to date on IBD and controls. A large proportion of plasma proteins are altered in IBD, which provides insights into the disease aetiology and indicates a potential for biomarker discovery.

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data.

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).

Isolation and Identification of the Follicular Microbiome: Implications for Acne Research.

Our understanding of the microbiome and the role of Propionibacterium acnes in skin homeostasis and acne pathogenesis is evolving. Multiple methods for sampling and identifying the skin's microbiome exist, and understanding the differences between the abilities of various methods to characterize the microbial landscape is warranted. This study compared the microbial diversity of samples obtained from the cheeks of 20 volunteers, collected by surface swab, pore strips, and cyanoacrylate glue follicular biopsy, all sequenced with 16S rRNA sequencing (V1-V3) and whole-genome metagenomic sequencing. The sequencing method of choice influenced the detection of microbial profiles as whole-genome sequencing captured more species diversity, including viruses, compared with 16S sequencing. The relative abundance of bacterial or fungal species and overall diversity did not differ between sampling methods. However, the viral composition of the skin's surface is unique compared with the follicle, suggesting distinct viral niches within the skin. P. acnes bacteria, ribotypes, and bacteriophages were identified equally by all sampling methods indicating that the sampling method, whether for the skin's surface or follicle, does not impact P. acnes-related characterization and that all may be equally useful for acne-related research studies.

Reflecting health: smart mirrors for personalized medicine.

Inexpensive embedded computing and the related Internet of Things technologies enable the recent development of smart products that can respond to human needs and improve everyday tasks in an attempt to make traditional environments more "intelligent". Several projects have augmented mirrors for a range of smarter applications in automobiles and homes. The opportunity to apply smart mirror technology to healthcare to predict and to monitor aspects of health and disease is a natural but mostly underdeveloped idea. We envision that smart mirrors comprising a combination of intelligent hardware and software could identify subtle, yet clinically relevant changes in physique and appearance. Similarly, a smart mirror could record and evaluate body position and motion to identify posture and movement issues, as well as offer feedback for corrective actions. Successful development and implementation of smart mirrors for healthcare applications will require overcoming new challenges in engineering, machine learning, computer vision, and biomedical research. This paper examines the potential uses of smart mirrors in healthcare and explores how this technology might benefit users in various medical environments. We also provide a brief description of the state-of-the-art, including a functional prototype concept developed by our group, and highlight the directions to make this device more mainstream in health-related applications.

Cell-specific prediction and application of drug-induced gene expression profiles.

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning.

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach.

PURPOSE: Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed. METHODS: We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM. RESULTS: We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days. CONCLUSION: Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

A functional genomics predictive network model identifies regulators of inflammatory bowel disease.

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

Environments Tune and Select Cellular Diversity.

Technical advances in single-cell sequencing data and their application to greater samples is revealing substantial cell-to-cell variation in expression levels and propagation of this variation between molecules across cells. New quantitative approaches that apply mechanistic and statistical models in a systems-wide approach are illuminating the drivers of phenotypic diversity.

Automated cell type discovery and classification through knowledge transfer.

MOTIVATION: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. RESULTS: We present a new algorithm called utomated ell-type iscovery and lassification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. AVAILABILITY AND IMPLEMENTATION: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc . CONTACT: brian.kidd@mssm.edu or joel.dudley@mssm.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Novel Therapeutics Identification for Fibrosis in Renal Allograft Using Integrative Informatics Approach.

Chronic allograft damage, defined by interstitial fibrosis and tubular atrophy (IF/TA), is a leading cause of allograft failure. Few effective therapeutic options are available to prevent the progression of IF/TA. We applied a meta-analysis approach on IF/TA molecular datasets in Gene Expression Omnibus to identify a robust 85-gene signature, which was used for computational drug repurposing analysis. Among the top ranked compounds predicted to be therapeutic for IF/TA were azathioprine, a drug to prevent acute rejection in renal transplantation, and kaempferol and esculetin, two drugs not previously described to have efficacy for IF/TA. We experimentally validated the anti-fibrosis effects of kaempferol and esculetin using renal tubular cells in vitro and in vivo in a mouse Unilateral Ureteric Obstruction (UUO) model. Kaempferol significantly attenuated TGF-ß1-mediated profibrotic pathways in vitro and in vivo, while esculetin significantly inhibited Wnt/ß-catenin pathway in vitro and in vivo. Histology confirmed significantly abrogated fibrosis by kaempferol and esculetin in vivo. We developed an integrative computational framework to identify kaempferol and esculetin as putatively novel therapies for IF/TA and provided experimental evidence for their therapeutic activities in vitro and in vivo using preclinical models. The findings suggest that both drugs might serve as therapeutic options for IF/TA.

High-Throughput Characterization of Blood Serum Proteomics of IBD Patients with Respect to Aging and Genetic Factors.

To date, no large scale, systematic description of the blood serum proteome has been performed in inflammatory bowel disease (IBD) patients. By using microarray technology, a more complete description of the blood proteome of IBD patients is feasible. It may help to achieve a better understanding of the disease. We analyzed blood serum profiles of 1128 proteins in IBD patients of European descent (84 Crohn's Disease (CD) subjects and 88 Ulcerative Colitis (UC) subjects) as well as 15 healthy control subjects, and linked protein variability to patient age (all cohorts) and genetic components (genotype data generated from CD patients). We discovered new, previously unreported aging-associated proteomic traits (such as serum Albumin level), confirmed previously reported results from different tissues (i.e., upregulation of APOE with aging), and found loss of regulation of MMP7 in CD patients. In carrying out a genome wide genotype-protein association study (proteomic Quantitative Trait Loci, pQTL) within the CD patients, we identified 41 distinct proteomic traits influenced by cis pQTLs (underlying SNPs are referred to as pSNPs). Significant overlaps between pQTLs and cis eQTLs corresponding to the same gene were observed and in some cases the QTL were related to inflammatory disease susceptibility. Importantly, we discovered that serum protein levels of MST1 (Macrophage Stimulating 1) were regulated by SNP rs3197999 (p = 5.96E-10, FDR<5%), an accepted GWAS locus for IBD. Filling the knowledge gap of molecular mechanisms between GWAS hits and disease susceptibility requires systematically dissecting the impact of the locus at the cell, mRNA expression, and protein levels. The technology and analysis tools that are now available for large-scale molecular studies can elucidate how alterations in the proteome driven by genetic polymorphisms cause or provide protection against disease. Herein, we demonstrated this directly by integrating proteomic and pQTLs with existing GWAS, mRNA expression, and eQTL datasets to provide insights into the biological processes underlying IBD and pinpoint causal genetic variants along with their downstream molecular consequences.