Faculty Bibliography

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.

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.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Objective: To better understand uterine cancer using an integrated, high-throughput strategy that evaluates patterns of protein expression, post-translational modification (PTM), somatic mutations, copy number variation (CNV), and patterns of protein-coding and noncoding RNA expression in parallel.
Method(s): State-of-the-art platforms for mass spectrometry, next-generation sequencing, and bioinformatics were used to comprehensively profile carefully annotated specimens of endometrial cancer and matched adjacent and nonmatched normal tissue. Normals were histologically assessed for their relative composition of endometrium and myometrium. Microsatellite instability (MSI) was categorized as high or low based on whole exome sequencing and patterns of genomic methylation. Clinical demographics, including tumor histology and patient comorbid conditions, were verified across multiple source sites.
Result(s): Use of mass spectrometry consistently identified ~11,000 distinct proteins for each cancer (n = 101) and normal specimen (n = 40) in the discovery dataset (matched endometrium low, n = 25; nonmatched endometrium high normal cores, n = 9; and matched pure myometrium, n = 6). These data reveal unique patterns of proteomic biomarkers and neoantigen expression that differ significantly between cancer histotypes and normals. Cancer-associated protein expression is driven, at least partly, by distinct patterns of mutations and genomic CNV. Our data also reveal novel patterns of circular RNA associated with disruptions in protein expression that vary by uterine cancer histotype. Last, proteogenomic integration reveals previously unrecognized mechanisms potentially responsible for modulating immune responses in MSI-H uterine cancers. Ongoing work includes integrating patterns of microRNA expression, protein phosphorylation, and acetylation into our analyses.
Conclusion(s): Comprehensive proteogenomic analyses have revealed novel patterns of protein, PTM, and circular RNA expression associated with uterine cancer. These observations will be discussed, emphasizing the novel metabolic and immunologic susceptibilities we have identified.
Copyright

Aberrant phospho-signaling is a hallmark of cancer. We investigated kinase-substrate regulation of 33,239 phosphorylation sites (phosphosites) in 77 breast tumors and 24 breast cancer xenografts. Our search discovered 2,134 quantitatively-correlated kinase-phosphosite pairs, enriching for and extending experimental or binding-motif predictions. Among the 91 kinases with auto-phosphorylation, elevated EGFR, ERBB2, PRKG1, and WNK1 phosphosignaling were enriched in basal, HER2-E, Luminal A, and Luminal B breast cancers, respectively, revealing subtype-specific regulation. CDKs, MAPKs, and ataxia-telangiectasia proteins were dominant, master regulators of substrate-phosphorylation, whose activities are not captured by genomic evidence. We unveiled phospho-signaling and druggable targets from 113 kinase-substrate pairs and cascades downstream of kinases, including AKT1, BRAF and EGFR. We further identified kinase-substrate-pairs associated with clinical or immune signatures and experimentally validated activated phosphosites of ERBB2, EIF4EBP1, and EGFR. Overall, kinase-substrate regulation revealed by the largest unbiased global phosphorylation data to date connects driver events to their signaling effects.

Alterations in gut microbiota are known to affect intestinal inflammation and obesity. Antibiotic treatment can affect weight gain by elimination of histone deacetylase (HDAC) inhibitor-producing microbes, which are anti-inflammatory by augmenting regulatory T (T_reg) cells. We asked whether mice that lack HDAC6 and have potent suppressive T_reg cells are protected from microbiota-induced accelerated weight gain. We crossed wild-type and HDAC6-deficient mice and subjected the offspring to perinatal penicillin, inducing weight gain via microbiota disturbance. We observed that male HDAC6-deficient mice were not protected and developed profoundly accelerated weight gain. The antibiotic-exposed HDAC6-deficient mice showed a mixed immune phenotype with increased CD4⁺ and CD8⁺ T-cell activation yet maintained the enhanced T_reg cell-suppressive function phenotype characteristic of HDAC6-deficient mice. 16S rRNA sequencing of mouse fecal samples reveals that their microbiota diverged with time, with HDAC6 deletion altering microbiome composition. On a high-fat diet, HDAC6-deficient mice were depleted in representatives of the S24-7 family and Lactobacillus but enriched with Bacteroides and Parabacteroides; these changes are associated with obesity. Our findings further our understanding of the influence of HDACs on microbiome composition and are important for the development of HDAC6 inhibitors in the treatment of human diseases.-Lieber, A. D., Beier, U. H., Xiao, H., Wilkins, B. J., Jiao, J., Li, X. S., Schugar, R. C., Strauch, C. M., Wang, Z., Brown, J. M., Hazen, S. L., Bokulich, N. A., Ruggles, K. V., Akimova, T., Hancock, W. W., Blaser, M. J. Loss of HDAC6 alters gut microbiota and worsens obesity.

BACKGROUND:Translatomics data, particularly genome-wide ribosome profiling and polysome profiling, provide multiple levels of gene regulatory information that can be used to assess general transcription and translation, as well translational efficiency. The increasing popularity of these techniques has resulted in multiple algorithms to detect translational regulation, typically distributed in the form of command line tools that require a basic level of programming ability. Additionally, due to the static nature of current software, dynamic transcriptional and translational comparative analysis cannot be adequately achieved. In order to streamline hypothesis generation, investigators must have the ability to manipulate and interact with their data in real-time. RESULTS:To address the lack of integration in current software, we introduce RIVET, Ribosomal Investigation and Visualization to Evaluate Translation, an R shiny based graphical user interface for translatomics data exploration and differential analysis. RIVET can analyze either microarray or RNA sequencing data from polysome profiling and ribosome profiling experiments. RIVET provides multiple choices for statistical analysis as well as integration of transcription, translation, and translational efficiency data analytics and the ability to visualize all results dynamically. CONCLUSIONS:RIVET is a user-friendly tool designed for bench scientists with little to no programming background. RIVET facilitates the data analysis of translatomics data allowing for dynamic generation of results based on user-defined inputs and publication ready visualization. We expect RIVET will allow for scientists to efficiently make more comprehensive data observations that will lead to more robust hypothesis regarding translational regulation.