Faculty Bibliography

Introduction: Although a substantial body of research documents a relationship between traumatic stress in childhood and the initiation of substance use later in the life course, only limited research has examined potential linkages between adverse childhood experiences (ACEs) and the initiation of non-medical prescription opioid use and other opioid use behaviors. The present study contributes to this growing body of work by investigating the association of childhood trauma with early initiation of a series of opioid use behaviors. Methods: New York City young adults (n = 539) ages 18-29 who reported non-medical use of prescription opioids or heroin use in the past 30 days were recruited using Respondent-Driven Sampling in 2014-16. Ten ACEs were assessed via self-report with the ACE Questionnaire. Associations between number of ACEs and self-reported ages of initiating seven opioid use behaviors (e.g., non-medical prescription opioid use, heroin use, heroin injection) were estimated with multivariable logistic regression. Results: Eighty nine percent of participants reported at least one ACE, and 46% reported four or more ACEs, a well-supported threshold indicating elevated risk for negative health consequences. Every increase of one trauma was associated with a 12-23% increase in odds of early initiation across the seven opioid use behaviors. Findings also document that the mean age at initiation increased with increasing risk severity across the behaviors, contributing to evidence of a trajectory from opioid pill misuse to opioid injection. Discussion: Increasing number of childhood traumas was associated with increased odds of earlier initiation of multiple opioid misuse behaviors. In light of prior research linking earlier initiation of substance use with increased substance use severity, present findings suggest the importance of ACEs as individual-level determinants of increased opioid use severity. Efforts to prevent onset and escalation of opioid use among at-risk youth may benefit from trauma prevention programs and trauma-focused screening and treatment, as well as increased attention to ameliorating upstream socio-structural drivers of childhood trauma.

Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, many results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 42 studies focusing on the gut microbiome in 12 autoimmune diseases to identify a microbial signature predictive of multiple sclerosis (MS), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and general autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. To do this, we used four machine learning algorithms, random forest, eXtreme Gradient Boosting (XGBoost), ridge regression, and support vector machine with radial kernel and recursive feature elimination to rank disease predictive taxa comparing disease vs. healthy participants and pairwise comparisons of each disease. Comparing the performance of these models, we found the two tree-based methods, XGBoost and random forest, most capable of handling sparse multidimensional data, to consistently produce the best results. Through this modeling, we identified a number of taxa consistently identified as dysregulated in a general autoimmune disease model including Odoribacter, Lachnospiraceae Clostridium, and Mogibacteriaceae implicating all as potential factors connecting the gut microbiome to autoimmune response. Further, we computed pairwise comparison models to identify disease specific taxa signatures highlighting a role for Peptostreptococcaceae and Ruminococcaceae Gemmiger in IBD and Akkermansia, Butyricicoccus, and Mogibacteriaceae in MS. We then connected a subset of these taxa with potential metabolic alterations based on metagenomic/metabolomic correlation analysis, identifying 215 metabolites associated with autoimmunity-predictive taxa.

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.

Knowledge of fundamental differences between breast cancer subtypes has driven therapeutic advances; however, basal-like breast cancer (BLBC) remains clinically intractable. Because BLBC exhibits alterations in DNA repair enzymes and cell-cycle checkpoints, elucidation of factors enabling the genomic instability present in this subtype has the potential to reveal novel anti-cancer strategies. Here, we demonstrate that BLBC is especially sensitive to suppression of iron-sulfur cluster (ISC) biosynthesis and identify DNA polymerase epsilon (POLE) as an ISC-containing protein that underlies this phenotype. In BLBC cells, POLE suppression leads to replication fork stalling, DNA damage, and a senescence-like state or cell death. In contrast, luminal breast cancer and non-transformed mammary cells maintain viability upon POLE suppression but become dependent upon an ATR/CHK1/CDC25A/CDK2 DNA damage response axis. We find that CDK1/2 targets exhibit hyperphosphorylation selectively in BLBC tumors, indicating that CDK2 hyperactivity is a genome integrity vulnerability exploitable by targeting POLE.

Background/Purpose: Patients with systemic lupus erythematosus (SLE) are at increased risk of premature atherosclerosis and thrombosis. Hydroxychloroquine (HCQ) is widely used in the treatment of SLE and has been considered of benefit for overall vascular health albeit studies to address this benefit at the cellular level have been limited. Accordingly, this study was initiated to investigate the relationship between HCQ use and dose with platelet activity, the platelet transcriptome, and vascular functional readouts.
Method(s): Patients fulfilling ACR or SLICC criteria for SLE were consecutively recruited for platelet evaluation with the only exclusion being on nonsteroidal anti-inflammatory medications, aspirin or anticoagulants. At enrollment, blood was collected for hematology analysis using the Sysmex XN-1000 analyzer, platelet aggregation via the Helena AggRAMTM system, and platelet RNA isolation and storage. Microvascular function was assessed via sublingual sidestream darkfield imaging. Brachial artery reactivity testing was used to evaluate large vessel function. Stored platelet RNA was isolated and analyzed by RNA sequencing (Illumina HiSeq4000 Sequencing).
Result(s): Among 132 SLE subjects, 108 were on HCQ. Mean age was 39.9 +/- 13.0 and 97% were female. Lupus disease activity at the time of blood draw assessed by the SELENA-SLEDAI activity index was 3.44 (range 0-20). Demographics and SLE disease activity did not differ between those on versus off HCQ (Table 1). Platelet count and size were not different between groups (Figure 1A). Platelet aggregation in response to submaximal ADP at multiple concentrations was lower in participants on HCQ (Figure 1B). Consistently, there was an inverse relationship between HCQ dosing and platelet aggregation in response to ADP (2uM: R=-0.213, P=0.037; 1uM: R=-0.310, P=0.0025; 0.4uM: R=-0.376, P=0.00018; Figure 1C). Since no subjects were on aspirin (or any other antiplatelet therapy at enrollment), aggregation in response to arachidonic acid (AA) was robust and similar between groups. However, after incubating platelets with aspirin (3mM) in vitro, platelet aggregation in response to AA was lower in the HCQ group compared to non-HCQ group (P=0.035, Figure 1B). To investigate the potential mechanisms of HCQ induced lower platelet aggregation, we evaluated platelet RNA sequencing in 49 subjects (8 no HCQ, 41 on HCQ). Positive regulation of pathways related to platelet activation (and in particular, P-selectin expression) was inversely related to HCQ, especially with higher doses (Figure 1E). In terms of vascular function, subjects on HCQ had improved microvascular function as noted by an increased proportion of sublingual capillaries filled with RBCs (P=0.011) and smaller perfused boundary region (PBR, P=0.010). HCQ dosing correlated with PBR (R=-0.599, P=0.002, Figure 1H) and RBC Filling (R=-0.592, P=0.002, Figure 1I). BART also trended positively with HCQ dose (R=0.385, P=0.094; Figure 1J).
Conclusion(s): These findings suggest that HCQ may provide benefit for vascular health in SLE as supported by ex vivo experiments demonstrating decreased platelet aggregation and downregulation of platelet functional pathways as well as improved vascular readouts

BACKGROUND:Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow. RESULTS:We present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Users can efficiently evaluate a huge range of clustering results from multiple models and hyperparameters to identify an optimal model. CONCLUSIONS:Hypercluster improves ease of use, robustness and reproducibility for unsupervised clustering application for high throughput biology. Hypercluster is available on pip and bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster .

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.

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.

The relative contributions of genetic and environmental factors to variation in immune responses are poorly understood. Here, we performed a phenotypic analysis of immunological parameters in laboratory mice carrying susceptibility genes implicated in inflammatory bowel disease (IBD) (Nod2 and Atg16l1) upon exposure to environmental microbes. Mice were released into an outdoor enclosure (rewilded) and then profiled for immune responses in the blood and lymph nodes. Variations of immune cell populations were largely driven by the environment, whereas cytokine production elicited by microbial antigens was more affected by the genetic mutations. We identified transcriptional signatures in the lymph nodes associated with differences in T cell populations. Subnetworks associated with responses against Clostridium perfringens, Candida albicans, and Bacteroides vulgatus were also coupled with rewilding. Therefore, exposing laboratory mice with genetic mutations to a natural environment uncovers different contributions to variations in microbial responses and immune cell composition.

Free-living mammals, such as humans and wild mice, display heightened immune activation compared with artificially maintained laboratory mice. These differences are partially attributed to microbial exposure as laboratory mice infected with pathogens exhibit immune profiles more closely resembling that of free-living animals. Here, we examine how colonization by microorganisms within the natural environment contributes to immune system maturation by releasing inbred laboratory mice into an outdoor enclosure. In addition to enhancing differentiation of T cell populations previously associated with pathogen exposure, outdoor release increased circulating granulocytes. However, these "rewilded" mice were not infected by pathogens previously implicated in immune activation. Rather, immune system changes were associated with altered microbiota composition with notable increases in intestinal fungi. Fungi isolated from rewilded mice were sufficient in increasing circulating granulocytes. These findings establish a model to investigate how the natural environment impacts immune development and show that sustained fungal exposure impacts granulocyte numbers.