Biology subjects

Maecker, H. T.

Publications and source records attributed to Maecker, H. T..

8 recordsLinked to original sources

bioRxiv2026

Immune Aging is an Independent Risk Factor for Cardiovascular Disease

Cardiovascular disease remains the leading cause of mortality, yet current clinical predictors miss substantial disease-risk. While the immune system contributes to this residual risk, its complexity has hindered broadly applicable, clinically scalable metrics of immune-state. Here, we establish the prognostic relevance of IMM-AGE, a system-level metric of immune-aging, to cardiovascular disease. We learn reference-free, low-dimensional representations of IMM-AGE across cell, protein, and mRNA measurements, enabling high-fidelity quantification across modalities, blood fractions, and platforms, including standard hospital flow cytometers. Among UK-Biobank participants, 56.9% of IMM-AGE variation remained unexplained by routine clinical measures, and across diverse cohorts totaling ~48,000 individuals, elevated IMM-AGE was independently associated with future cardiovascular risk, intervention outcomes, and mortality. Moreover, incorporation of IMM-AGE into the PREVENT 10-year risk equation significantly improved risk stratification. These findings establish immune-aging as an independent biological dimension of cardiovascular disease-risk and support IMM-AGE as a practical tool for precision risk assessment.

immunology↗

bioRxiv2025

Identification of a multi-omics factor predictive of long COVID in the IMPACC study

Following SARS-CoV-2 infection, [~]10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids in LC patients. The recovery factor was also associated with altered circulating immune cell frequencies. Notably, recovery factor scores were predictive of LC occurrence in patients as early as hospital admission, irrespective of acute disease severity. Thus, the recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

systems biology↗

bioRxiv2024

Single-Cell Peripheral Immunoprofiling of Lewy Body Disease in a Multi-site Cohort

Studies implicated peripheral organs involvement in the development of Lewy body disease (LBD), a spectrum of neurodegenerative diagnoses that include Parkinsons Disease (PD) without or with dementia (PDD) and dementia with Lewy bodies (DLB). This study characterized peripheral immune responses unique to LBD at single-cell resolution. Peripheral mononuclear cell (PBMC) samples were collected from sites across the U.S. The diagnosis groups comprise healthy controls (HC, n=164), LBD (n=132), Alzheimers disease dementia (ADD, n=98), other neurodegenerative disease controls (NDC, n=21), and immune disease controls (IDC, n=14). PBMCs were activated with three stimulants, stained by surface and intracellular signal markers, and analyzed by flow cytometry, generating 1,184 immune features. Our model classified LBD from HC with an AUROC of 0.90{+/-}0.06. The same model distinguished LBD from ADD, NDC, IDC, or other common conditions associated with LBD. Model predictions were driven by pPLC{gamma}2, p38, and pSTAT5 signals from specific cell populations and activations.

neuroscience↗

bioRxiv2023

Cross-platform comparison of highly-sensitive immunoassays for inflammatory markers in a COVID-19 cohort

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing antibody pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on serum samples from the NIH IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. Firstly, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Secondly, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Thirdly, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

immunology↗

bioRxiv2019

The FluPRINT dataset: A multidimensional analysis of the influenza vaccine imprint on the immune system

Recent advances in machine learning have allowed identification of molecular and cellular factors that underly successful antibody responses to influenza vaccines. Results of these studies have revealed the high level of complexity necessary to establish influenza immunity, and many different cellular and molecular components involved. However, identified correlates of protection fail to account for the majority of vaccinated cases across ages, cohorts, and influenza seasons. Major challenges arise from small sample sizes and from analysis of only one aspect of the biology such by using transcriptome data. The objective of the current study is to create a unified database, entitled FluPRINT, to enable a large-scale study exploring novel cellular and molecular underpinnings of successful immunity to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell subset phenotypes, and cytokine stimulations. FluPRINT, thus facilitates application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that drive successful influenza vaccination.

systems biology↗

bioRxiv2019

SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. Here, we developed Sequential Iterative Modelling "OverNight" or SIMON, an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust antibody response to influenza antigens. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.

immunology↗

bioRxiv2018

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

The success of immunotherapy has led to a myriad of new clinical trials. Connected to these trials are efforts to discover biomarkers providing mechanistic insight and predictive signatures for personalization. Still, the plethora of immune monitoring technologies can face investigator bias, missing unanticipated cellular responses in limited clinical material. We here present a mass cytometry workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate human immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. The resulting assay enumerated [≥] 98% of peripheral immune cells with [≥] 4 positively identifying antigens. Robustness and reproducibility were demonstrated on multiple samples types, across research centers and by orthogonal measurements. Using automated analysis, we monitored complex immune dynamics, identifying signatures in bone-marrow transplantation associated graft-versus-host disease. This validated and available workflow ensures comprehensive immunophenotypic analysis, data comparability and will accelerate biomarker discovery in immunomodulatory therapeutics.

immunology↗

bioRxiv2018

Differential T cell reactivity to hypocretin and flu antigen epitopes provides clues to the pathophysiology of Type 1 narcolepsy

Type 1 narcolepsy (T1N) is caused by hypocretin (HCRT) neuronal loss. Association with the Human Leukocyte Antigen (HLA)-DQB1*06:02/DQA1*01:02 (98% vs 25%) heterodimer (DQ0602), T cell receptor (TCR) and other immune loci suggest autoimmunity but autoantigen(s) are unknown. Onset is seasonal and associated with influenza A, notably pandemic 2009 H1N1 (pH1N1). An extensive unbiased DQ0602 binding peptide screen was performed encompassing peptides derived from Pandemrix(R) X-179-A pH1N1 influenza-A vaccine, a known T1N trigger, other H1N1 strains, and potential human autoantigens HCRT and RFX4, identifying 109 binders. The presence of cognate tetramer-peptide specific CD4+ T cells was studied in 35 narcolepsy cases and 22 DQ0602 controls after expansion of antigen-specific cells in Peripheral Blood Monocytes Cell (PBMC) cultures. Higher reactivity to influenza epitopes pHA273-287 (pH1N1 specific) and PR8 (H1N1 pre 2009)-specific NP17-31 were observed in T1N. Extensive reactivity to C-amidated but not native version of HCRT54-66 and HCRT86-97, which are two highly homologous peptides (HCRTNH2) was observed with higher frequencies of specific T cells in T1N. TCR/{beta} CDR3 sequences found in pHA273-287, NP17-31 and HCRTNH2 tetramer positive CD4+ cells were also retrieved in single INF{gamma}-secreting CD4+ sorted cells stimulated with Pandemrix(R), confirming immunodominance and functional significance in DQ0602-mediated responses and molecular mimicry. TCR/{beta} CDR3 motifs of HCRT54-66 and HCRT86-97 tetramers were extensively shared. Particularly notable was sharing across subjects of an CDR3, CAVETDSWGKLQF (in association with various CDR3{beta} that used TRAJ24, a chain modulated by Single Nucleotide Polymorphism (SNPs) rs1154155 and rs1483979 associated with T1N. Sharing of CDR3{beta} CASSQETQGRNYGYTF (in association with various CDR3 was also observed with HCRTNH2 and pHA273-287-tetramers across subjects. This segment uses TRBV4-2, a segment modulated by narcolepsy-associated SNP rs1008599. Higher HCRTNH2 positive CD4+ T cell numbers in T1N together with sharing of J24 CAVETDSWGKLQF in HCRTNH2 autoimmune responses, indicates causal DQ0602-mediated CD4+ autoreactivity to HCRT in T1N. Our results provide evidence for autoimmunity and molecular mimicry with flu antigens modulated by genetic components in the pathophysiology of T1N.

immunology↗