Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. While well-established tools exist for transcriptomics and other omics layers, the development of robust enrichment resources for metabolomics remains comparatively limited. To address this gap, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case.

Poor infiltration of CD8+ T cells and dysregulation of MHC class I (MHC-I) confer resistance to anticancer immunotherapies. Inhibition of the epigenetic regulator lysine-specific demethylase 1 (LSD1) has been shown to increase CD8+ T-cell infiltration in head and neck squamous cell carcinoma (HNSCC). In this study, we aimed to elucidate the mechanisms of LSD1 inhibition in antitumor immunity in HNSCC to aid in the development of effective therapeutic strategies. LSD1 inhibition in syngeneic and chronic tobacco carcinogen-induced HNSCC mouse models increased the recruitment of activated dendritic cells (DC), as well as CD4+ and CD8+ T cells, and the expression of IFNγ in CD8+ T cells, CXCL9 in DCs, and CXCR3 in CD4+ T cells. Humanized HNSCC mice and patient data validated the inverse correlation of KDM1A with DC markers, CD8+ T cells, and their activating chemokines. Kdm1a knockout in mouse HNSCC and LSD1 inhibitor treatment of human HNSCC cells cocultured with human peripheral blood mononuclear cells resulted in MHC-I upregulation in cancer cells. LSD1 inhibition promoted CD8+ T-cell activation via a DC-dependent mechanism and induced efficient antigen presentation in CD8+ T cells. Finally, LSD1 inhibition increased H3K4me2 at the promoters of DC-related markers (BATF3 and CXCL9), T-cell markers (CXCR3), and MHC-I (HLA-A). Overall, LSD1 inhibition in tumor cells upregulates MHC-I expression and stimulates CXCL9 secretion by DCs to enhance antigen presentation and promote CD8+ T-cell activation via the CXCL9-CXCR3 signaling axis, resulting in increased IFNγ production. This may have implications for treating poorly immunogenic and immunotherapy-resistant cancers. SIGNIFICANCE:LSD1 inhibition enhances antigen presentation and reprograms the tumor microenvironment by inducing infiltration of T cells and dendritic cells, activating antitumor immunity and providing an epigenetic therapy for head and neck cancer.

Perturbational transcriptomics is a powerful tool for understanding gene function and drug effects, yet predicting how perturbations manifest across different biological contexts remains a central challenge, limiting translation from model systems to clinically relevant tissues. Despite growing interest in this problem, benchmarking efforts have been hindered by inconsistent evaluation tasks, heterogeneous metrics, and limited assessment across perturbation types and biological systems. Here, we introduce a benchmarking framework for cross-context perturbation-signature prediction (a task we define as signature recontextualization), grounded in explicit definitions of the prediction task, target-data availability, and evaluation metrics centered on signature recovery. The framework evaluates prediction performance across three target-context data regimes: (1) control only, where only control profiles from the target context are measured; (2) low coverage, where a limited subset of perturbations in the target context are measured; and (3) high coverage, where most perturbations in the target context are measured. This design enables systematic assessment of how prediction performance depends on target-context sample size while providing a standardized basis for comparing methods. We evaluate newly developed projection-based (projectCor) and network-based (netProp) methods alongside deep learning-based foundation models (scGPT, STACK) and statistical baselines. The benchmark spans four diverse perturbational datasets: CRISPR knockdowns and drug perturbations in cell lines, plus in vivo chemical perturbations in rat tissues from DrugMatrix, extending evaluation beyond isolated cell-line models to tissue-level responses. Across tasks, projection and network propagation approaches show strong flexibility across perturbation types and biological contexts, and in several cases match or exceed the performance of deep learning and foundation models, suggesting that model complexity does not inherently improve cross-context generalization. We further show that perturbation predictability varies substantially with pathway conservation, transcriptional response strength, and baseline similarity between source and target contexts. All datasets, methods, and evaluation utilities are released as an open-source R package (sigRecon), providing a foundation for reproducible benchmarking and future method development.

RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the limited-sample-size challenges common in high-dimensional (e.g., 'omics') data. It supports both the estimation of partial correlation networks modeled as Gaussian networks1 and conditional Gaussian Bayesian networks for mixed data types that combine continuous and discrete variables2. The framework incorporates multiple resampling strategies, including bootstrap, subsampling, and cluster-based approaches, to accommodate both independent and correlated (e.g., family-based) observations. To enhance interpretability, RSNet integrates graphlet-based topology analysis that captures higher-order connectivity and edge sign information, enabling single-node and subnetwork-level insights. Notably, RSNet is the first R package to efficiently construct signed graphlet degree vector matrices (GDVMs) in near-constant time for sparse networks, providing scalable analysis of higher-order network structure. Collectively, RSNet offers a versatile tool for statistically reliable and interpretable network inference in high-dimensional data.

In this study, we developed an integrated single-cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling >600 000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment, we were able to characterize heterogeneity of CD4, CD8 T cells, and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer-associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune and stromal cell types, which conferred a survival advantage in both The Cancer Genome Atlas (TCGA), METABRIC, and SCANB. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.

Establishing sub-phenotypes of pneumonia based on distinct host processes will be a step towards using host-directed therapies (to complement microbe-directed therapies) more rationally and precisely. Although pneumonia is a pulmonary pathophysiology, histological changes within the lungs have not been leveraged for sub-phenotyping. We addressed this by scoring 18 histopathology features (e.g., type 2 cell hyperplasia or necrosis) across rapid autopsy lung samples from 276 elderly subjects with pneumonia. Machine learning algorithms segregated subjects into seven different sub-phenotypes of pneumonia with distinct histopathology signatures. Quantitative immunofluorescence demonstrated associations of macrophages, neutrophils, T cells, and B cells with select histology features and pulmonary pathology sub-phenotypes. Mouse models revealed corollary sub-phenotypes, although some histology features observed in human lungs were never observed in mice. By illuminating this spectrum of histopathologies and discriminating discrete sub-phenotypes of pneumonia, a foundational framework emerges for developing and using host-directed therapies for subsets of pneumonia patients.

Although aging is a universal event, some individuals are able to achieve extreme longevity. The Long-Life Family Study (LLFS) enrolls participants from families enriched with long-lived individuals, serves as a valuable dataset for studying ageing phenotypes and identify potential intervention targets. We analyzed the association between age at blood draw and 16,284 RNAseq-based blood transcriptomic data from 2,167 LLFS participants with ages ranging from 18 to 107, replicated the results in the Integrative Longevity Omics Study (ILO) dataset of 20,884 RNAseq-based blood transcriptomic data from 419 participants, with ages ranging from 60 to 108, and further compared our findings to a published reference aging signature. We identified 4,227 transcripts increasing and 4,044 transcripts decreasing with age, and enrichment analysis revealed age-related upregulation of inflammatory and senescence-related pathways, and downregulation of MYC and Wnt/β-catenin targets, among others. Further, a subset of transcripts showed age associations unique to the longevity-enriched cohorts (LLFS and ILO). We also identified 314 transcripts significantly associated with mortality risk and found that pro-survival gene sets included NK cell-mediated cytotoxicity and GPCR signaling. Finally, increased transcriptomic age predicted using transcriptomic clock was strongly associated with increased mortality. In summary, this study identified robust transcriptomic signatures of aging and mortality in a longevity-enriched population, highlighting key biological pathways such as immune modulation, inflammation, and senescence.

The New England Centenarian Study (NECS) provides a unique resource for the study of extreme human longevity (EL). To gain insight into biological pathways related to EL, chronological age and survival, we used an untargeted serum metabolomic approach (> 1400 metabolites) in 213 NECS participants, followed by integration of our findings with metabolomic data from four additional studies. Compared to their offspring and matched controls, EL individuals exhibited a distinct metabolic profile characterized by higher levels of primary and secondary bile acids—most notably chenodeoxycholic acid (CDCA) and lithocholic acid (LCA)—lower levels of biliverdin and bilirubin, and stable levels of selected steroids. Notably, elevated levels of both bile acids and steroids were associated with lower mortality. Several metabolites associated with age and survival were inversely associated with metabolite ratios related to NAD+ production and/or levels (tryptophan/kynurenine, cortisone/cortisol), gut bacterial metabolism (ergothioneine/trimethylamine N-oxide, aspartate/quinolinate), and oxidative stress (methionine/methionine sulfoxide), implicating these pathways in aging and/or longevity. We further developed a metabolomic clock predictive of biological age, with age deviations significantly associated with mortality risk. Key metabolites predictive of biological aging, such as taurine and citrate, were not captured by traditional age analyses, pointing to their potential role as biomarkers for healthy aging. These results highlight metabolic pathways that may be targeted to promote metabolic resilience and healthy aging.

BACKGROUND:Head and Neck Squamous Cell Carcinomas (HNSCC) are the seventh most prevalent form of cancer and are associated with human papilloma virus infection (HPV-positive) or with tobacco and alcohol use (HPV-negative). HPV-negative HNSCCs have a high recurrence rate, and individual patients' responses to treatment vary greatly due to the high level of cellular heterogeneity of the tumor and its microenvironment. METHODS:Here, we describe a HPV-negative HNSCC single cell atlas, which we created by integrating six publicly available datasets encompassing over 230,000 cells across 54 patients. We classify cell types, subpopulations, and their expression programs in the immune, mesenchymal, endothelial and epithelial compartments. We interrogate the relationship between cell types through hierarchical clustering, cell-cell communication analysis and correlating populations changing together across patients. RESULTS:We resolve the myeloid and fibroblast compartments, revealing an IL1B+ myeloid population previously unexplored in HNSCC and clarifying two immune cancer associated fibroblast populations that are frequently conflated, identify sex-associated changes in cell type proportions, and a unique interaction between CXCL8-positive fibroblasts and vascular endothelial cells. CONCLUSIONS:We utilize the atlas to contextualize the relationships between existing signatures and cell populations, harmonize nomenclature across studies, and show the power of this large-scale resource to robustly identify associations between transcriptional signatures and clinical phenotypes that would not be possible to discover using fewer patients. Beyond our findings, the atlas serves as a public resource for the high-resolution characterization of tumor heterogeneity of HPV-negative HNSCC.

Abstract Introduction Pulmonary infections induce heterogeneous lung immune responses, resulting in multiple pneumonia histopathology sub-phenotypes. Necrosuppurative pneumonia is characterized by alveolar necrosis, neutrophils, edema, and airspace fibrin accumulation, observed in human autopsies and S. pneumoniae (Sp)-infected mice. We endeavor to elucidate innate immunity mechanisms leading to the necrosuppurative sub-phenotype of pneumonia. Methods We compared outcomes of Sp pneumonia in wild-type (WT) mice and those with tissue factor (TF) inducibly deleted from lung epithelial cells. Results In WT mice, fibrinogen mRNA was induced in the liver and elevated in the blood during pneumonia, while TF mRNA was strongest in lung epithelial cells. Bacteria grew inexorably in the lungs and caused bacteremia, which was not affected by deletion of TF from the lung epithelium. However, deletion of lung epithelial cell TF reduced alveolar fibrin deposition and neutrophil recruitment. While Sp infection caused alveolar necrosis in WT mice, we instead observed type II epithelial cell hyperplasia when TF was deleted from the lung epithelium. Conclusion The airspace fibrin, neutrophil accumulation, and epithelial necrosis that are defining features of necrosuppurative pneumonia caused by severe pneumococcal infection all depend on TF produced by lung epithelial cells. Funding Source NIH-NHLBI Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)

Women with obesity-driven type 2 diabetes (T2D) face worse breast cancer outcomes, yet metabolic status does not fully inform current standards of care. We previously identified plasma exosomes as key drivers of tumor progression; however, their effect on immune cells within the tumor microenvironment (TME) remains unclear. Using a novel patient-derived organoid (PDO) system that preserves native tumor-infiltrating lymphocytes (TILs), we show that T2D plasma exosomes induce a 13.6-fold expansion of immunosuppressive TILs relative to nondiabetic controls. This immune dysfunction may promote micrometastatic survival and resistance to checkpoint blockade, a known issue in T2D cancer patients. Tumor-intrinsic analysis revealed a 1.5-fold increase in intratumoral heterogeneity and 2.3-fold upregulation of aggressive signaling networks. These findings reveal how T2D-associated metabolic dysregulation alters tumor-immune crosstalk through previously underappreciated exosomal signaling, impairing antitumor immunity and accelerating progression. Understanding these dynamics could inform tailored therapies for this high-risk, underserved patient population.

Aging is a heterogeneous process that unfolds differently across individuals and biological systems. While single biological clocks provide valuable insights, they often fail to capture the complex and multidimensional nature of aging. In this study, we developed system-specific aging clocks using metabolomics data from the Integrative Longevity Omics study to better understand the heterogeneity of aging trajectories. Each clock was designed to estimate biological age within a distinct metabolic system, under the assumption that variability in system-specific function reflects unique aspects of the aging process. Our analyses revealed striking inter-individual variability: some participants consistently exhibited age acceleration across systems, others showed age deceleration, and many demonstrated mixed patterns depending on the system measured. To further explore this heterogeneity, we clustered participants into subgroups based on their system-specific aging profiles. We then examined associations between these subgroups and (1) the Nutrient Variety Index (NVI), a comprehensive metric summarizing dietary diversity across 19 nutrient groups, (2) cognitive performance, and (3) mortality risk. We found that several subgroups displayed significant associations with multiple NVIs, particularly those reflecting balanced intake of carbohydrates. These same subgroups also showed more favorable cognitive outcomes and reduced mortality risk, suggesting that consistent patterns of healthy aging may be linked to dietary diversity and nutritional balance. Conversely, other subgroups displayed discordant patterns of aging acceleration and were associated with poorer outcomes. These findings highlight that aging is not uniform but system-specific, and that metabolomic aging clocks offer a promising framework for uncovering distinct pathways shaping healthy aging.

Among prostate cancer patients, co-morbid Type 2 Diabetes (T2D) is associated with faster progression to biochemical recurrence and increased risk of mortality. Previous work from our lab provides evidence that exosomes purified from media of insulin resistant adipocytes or T2D patient plasma likely drives these outcomes by delivering miRNAs that exacerbate tumor aggressiveness in several breast and prostate cancer models. Here, we build on our previous findings to investigate whether treatment with metabolic medications attenuates the tumor promoting effects of exosomes. We found that human DU145 cells, a model for prostate cancer, treated with plasma exosomes from T2D patients, shows patterns in global gene transcription that resolve by patient treatment with metformin. To test the effects of metformin experimentally, we used a murine model of insulin resistance (IR). Treating DU145 cells with miRNAs purified from the plasma exosomes of IR mice, we found that cells transfected with miRNAs from the metformin-treated IR group displayed significantly less migration than cells transfected with miRNAs from the unmedicated IR group. We suggest that metformin may partially reverse effects of T2D to exacerbate tumor aggressiveness by modifying the miRNA payload of plasma exosomes.

Abstract Basal-like breast cancers exhibit distinct cellular heterogeneity that contributes to disease pathology. In this study we used a genetic mouse model of basal-like breast cancer driven by epithelial-specific inactivation of the Hippo pathway-regulating LATS1 and LATS2 kinases to elucidate epithelial-stromal interactions. We demonstrate that basal-like carcinoma initiation in this model is accompanied by the accumulation of distinct cancer-associated fibroblasts and macrophages and dramatic extracellular matrix remodeling, phenocopying the stromal diversity observed in human triple-negative breast tumors. Dysregulated epithelial-stromal signals were observed, including those mediated by TGF-β, PDGF, and CSF. Autonomous activation of the transcriptional effector TAZ was observed in LATS1/2-deleted cells along with non-autonomous activation within the evolving tumor niche. We further show that inhibition of the YAP/TAZ-associated TEAD family of transcription factors blocks the development of the carcinomas and associated microenvironment. These observations demonstrate that carcinomas resulting from Hippo pathway dysregulation in the mammary epithelium are sufficient to drive cellular events that promote a basal-like tumor-associated niche and suggest that targeting dysregulated YAP/TAZ-TEAD activity may offer a therapeutic opportunity for basal-like mammary tumors.

Understanding epithelial stem cell differentiation and morphogenesis during breast tissue development is essential, as disruption in these processes underlie breast cancer formation. We used a next-generation single-cell-derived organoid model to investigate how individual stem cells give rise to complex tissue. We show that discoidin domain receptor 1 (DDR1) inhibition traps cells in a bipotent state, blocking alveolar morphogenesis and luminal cell expansion, which is necessary for complex epithelium formation. Disrupting RUNX1 function produced nearly identical phenotypes, underscoring its critical role downstream of DDR1. Mechanistically, DDR1 affects the interaction and expression of RUNX1 and its cofactor core binding factor beta (CBFβ), thereby regulating its activity. Mutational analyses in breast cancer patients reveal frequent alterations in the DDR1-RUNX1 signaling axis, particularly co-occurring mutations. Together, these findings uncover DDR1-RUNX1 as a central signaling pathway driving breast epithelial differentiation, whose dysregulation may contribute fundamentally to breast cancer pathogenesis.

Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after "adjusting" for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a cluster-based bootstrap algorithm to infer GGMs from correlated data. We use extensive simulations of correlated data from family-based studies to show that the proposed bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions when there are sufficient number of clusters. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well without power loss.

Pulmonary infections induce heterogeneous immune responses in the lung, resulting in multiple pneumonia phenotypes. An etiological agent cannot be identified in the majority of pneumonia cases; thus, elucidating these heterogeneous lung pathobiologies for the development of host-directed therapies is a major research priority. To characterize the heterogeneity in human pneumonia pathobiology, we scored 20 pneumonia features across hundreds of autopsy tissue samples from elderly subjects who died with pneumonia. These pneumonia features varied significantly across our human lung samples, particularly the presence and severity of polymerized fibrin in the alveolar spaces. In our human pneumonia samples, alveolar fibrin deposition was most severe in the samples diagnosed with bronchopneumonia and positively-correlated with neutrophilia and necrosis, while it was less frequent in our samples diagnosed with interstitial pneumonia and negatively-correlated with lymphoplasmacytosis and fibrosis. To understand the mechanism and significance of alveolar fibrin deposition during pneumonia, we further characterized these features in C57BL/6 mice with severe pneumonias caused by Streptococcus pneumoniae (Sp), Escherichia coli (Ec), Klebsiella pneumoniae (Kp), influenza A virus (IAV), or SARS-CoV-2 (SCV2). Sp- and Ec-infected lungs were dominated by neutrophilic influx and high levels of polymerized fibrin in the alveolar spaces, reflecting the fibrin-neutrophil association observed in human autopsy samples, while Kp-, IAV-, and SCV2-infected lungs had little-to-no alveolar fibrin staining despite an abundance of fibrin in the vasculature. During Sp infection, fibrinogen was upregulated in the liver and significantly increased in the blood. RNA in situ hybridization of Sp-infected lungs revealed an upregulation of extrinsic coagulation factors that promote fibrin polymerization, including tissue factor (F3) in airway epithelial cells, F10 in recruited neutrophils, and F13a1 in myeloid cells, revealing potential mechanistic drivers of fibrin polymerization in the airspace. Together, these data demonstrate that (1) alveolar fibrin is observed in only a subset of human lungs with pneumonia, and (2) some pneumonia-causing pathogens, but not others, upregulate fibrinogen and promote cleaved fibrin polymerization in the alveolar spaces of mice. These mouse models can be used to elucidate the immunological and pathophysiological significance of this fibrin accumulation in a subset of pneumonias, which may provide additional targets for the development of host-directed therapies aiming to enhance or reduce alveolar fibrin deposition during pneumonia.

Breast cancer, the most common cancer among women worldwide, continues to pose significant public health challenges. Among the subtypes of breast cancer, triple-negative breast cancer (TNBC) is particularly aggressive and difficult to treat due to the absence of receptors for estrogen, progesterone, or human epidermal growth factor receptor 2, rendering TNBC refractory to conventional targeted therapies. Emerging research underscores the exacerbating role of metabolic disorders, such as type 2 diabetes and obesity, on TNBC aggressiveness. Here, we investigate the critical cellular and molecular factors underlying this link. We explore the pivotal role of circulating plasma exosomes in modulating the tumor microenvironment and enhancing TNBC aggressiveness. We find that plasma exosomes from diet-induced obesity mice induce epithelial-mesenchymal transition features in TNBC cells, leading to increased migration in vitro and enhanced metastasis in vivo . We build on our previous reports demonstrating that plasma exosomes from obese, diabetic patients, and exosomes from insulin-resistant 3T3-L1 adipocytes, upregulate key transcriptional signatures of epithelial-mesenchymal transition in breast cancer. Bioinformatic analysis reveals that TNBC cells exhibit higher expression and activation of proteins related to the Rho-GTPase cascade, particularly the small Ras-related protein Rac1. Our approach suggests novel therapeutic targets and exosomal biomarkers, ultimately to improve prognosis for TNBC patients with co-morbid metabolic disorders.

A signature of 16 serum proteins that were previously profiled using the aptamer-based Somascan technology highlighted the roles of the e2 allele of APOE in lipid regulation via apolipoprotein B (APOB) and apolipoprotein E (APOE) and in inflammation. Here, the serum protein signature of APOE is validated and expanded using a combination of mass-spectrometry, ELISA, Luminex, blood transcriptomics, and antibody-based Olink serum proteomics. Some of the findings were replicated in the UK Biobank using antibody-based Olink serum proteomics. This analysis replicated the association between APOB and the e2 allele of APOE, detected a new, robust pattern of association between APOE genotypes and the serum level of APOE, and discovered new associations between APOE genotypes and the complex of apolipoproteins APOC1, APOC2, APOC3, APOC4, APOE, APOF, and APOL1. In addition, 13 new proteins correlated with APOE genotypes. This extended signature includes granule proteins CAMP, CTSG, DEFA3, and MPO secreted from neutrophils and points to olfactomedin 4 (OLFM4) as a new target for the prevention of Alzheimer's disease.

We previously identified a signature of 16 serum proteins that highlighted a role of the e2 allele of APOE in lipid regulation via apolipoprotein B (APOB) and apolipoprotein E (APOE), and in inflammation. The serum proteins were profiled using the aptamer-based Somalogic technology. Here, we validate and expand the serum protein signature of APOE using a combination of mass-spectrometry, ELISA, Luminex, antibody-based Olink proteomics, and blood transcriptomics. We replicate the association between APOB and the e2 allele of APOE, we correct the pattern of association between APOE genotypes and serum level of APOE, and we detect new associations between APOE genotypes and the complex of apolipoproteins APOC1, APOC4, APOC2, APOC3, APOE, APOF and APOL1. In addition, we discover 13 new proteins that correlate with APOE genotypes. This extended signature includes granule proteins CAMP, CTSG, DEFA3, and MPO secreted from neutrophils and points to olfactomedin 4 (OLFM4) as a new target for the prevention of Alzheimer's disease.