Biology subjects

Schweickart, A.

Publications and source records attributed to Schweickart, A..

3 recordsLinked to original sources

bioRxiv2026

Matched Plasma and Tissue Proteomics Reveal the Selective Nature of Circulating Biomarkers of Human Aging

Aging is systemic, yet how circulating proteins reflect molecular aging within solid tissues remains unclear. We profiled 5,415 proteins using Olink Explore HT in 178 adults aged 20-87 years, including matched plasma-muscle (n = 56) and plasma-ovary (n = 24) pairs. Plasma-tissue concordance was limited, with 50 muscle and 17 ovarian proteins significantly correlated, indicating that plasma selectively reflects tissue protein abundance. Age-correlated proteins and pathways differed markedly across sex and tissue strata, Including immune/inflammatory signatures in female plasma, vascular and extracellular-matrix pathways in male plasma, and collagen remodeling in the ovary. Expression Variation Analysis further revealed predominantly context-specific age-related changes in protein coordination, including FAS/FADD/TRAIL death-receptor networks in plasma and a TP53-CDKN1A senescence-associated axis in muscle. Exploratory multivariate models identified additional plasma associations with subsets of tissue proteins, including tissue-restricted muscle proteins. Together, these findings define plasma as a selective, context-dependent window into human tissue aging.

systems biology↗

bioRxiv2023

AutoFocus: A hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimers disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimers Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.

bioinformatics↗

bioRxiv2021

Variational autoencoders learn universal latent representations of metabolomics data

Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4,500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which showed that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, schizophrenia, and acute myeloid leukemia and found significant correlations with clinical patient groups. Taken together, we demonstrate for the first time that the VAE is a powerful method that learns biologically meaningful, nonlinear, and universal latent representations of metabolomics data.

bioinformatics↗