Faculty Bibliography

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.

The relative contributions of genetic and environmental factors to variation in immune responses are still poorly understood. Here, we performed a deep phenotypic analysis of immunological parameters of laboratory mice released into an outdoor enclosure, carrying susceptibility genes (Nod2 and Atg16l1) implicated in the development of inflammatory bowel diseases. Variations of immune cell populations were largely driven by environment, whereas cytokine production in response to stimulation was affected more by genetic mutations. Multi-omic models identified transcriptional signatures associated with differences in T cell populations. Subnetworks associated with responses against Clostridium perfringens, Candida albicans and Bacteroides vulgatus were also coupled with rewilding. Hence, exposing laboratory mice carrying different genetic mutations to a natural environment uncovered important contributors to immune variation

Multiple sclerosis (MS) is a neuroinflammatory disorder resulting from infiltration of T cells into the central nervous system (CNS) and demyelination of neurons. Since T follicular helper (TFH) cells are associated with MS relapse, their selective inhibition could be an ideal therapeutic. TFH cells require transcription factor BCL6 and active mTORC1/2 for development. When mTORC is active, cap-binding protein eukaryotic translation initiation factor 4E (eIF4E) initiates translation of selective mRNAs. However, the requirement of eIF4E for translation of mRNAs necessary for TFH cell development has not been investigated. Disruption of eIF4E binding to the 5' mRNA cap with drug 4EGI-1 inhibits TFH and germinal center (GC) B cell development while having no effect on differentiation and effector function of TH1, TH2, TH17, or Tregs. Silencing of eIF4E in only CD4 T cells is sufficient to inhibit their formation of TFH cells. We used polysome profiling to determine which mRNAs are selectively translated by eIF4E and identified salient programs regulated by transcription (BCL6, NFAT) and costimulation (CD28, SLAM). eIF4E is required for translation of BCL6 in human lymph node TFH and GC B cells. Administration of 4EGI-1 during experimental autoimmune encephalitis (EAE) results in significantly decreased infiltration of CD4 T cells in the CNS, demyelination, and clinical score. Further, 4EGI-1 treatment following initiation of symptoms results in rapid improvement of symptoms and partial remission earlier than vehicle-treated animals. Thus, eIF4E is required for differentiation of TFH cells and pathogenesis of autoimmune encephalitis, and 4EGI-1 represents a potential therapeutic.

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.