Biology subjects

Seitzer, P.

Publications and source records attributed to Seitzer, P..

4 recordsLinked to original sources

bioRxiv2026

A multiomic lifespan signature in genetically diverse, diet-restricted mice

Dietary restriction extends lifespan across model organisms, but the plasma molecular changes mediating this effect remain incompletely characterized. We present a longitudinal multiomic analysis of 2,234 plasma samples from 960 Diversity Outbred mice subjected to intermittent fasting or caloric restriction and followed to natural death. Using mass spectrometry, we quantified 1,512 metabolites, lipids, and proteins and mapped their associations with diet, age and longevity. DR-induced molecular changes scale with caloric intake and modulate inflammatory, lipid catabolism, and oxidative stress pathways. Aging showed a biphasic signature with sharp acceleration beyond 85% of lifespan, demarcating terminal decline. Mediation and survival modeling both identified superoxide dismutase (SODE) and vascular cell adhesion molecule (VCAM1) as top lifespan predictors. Genetic analysis revealed 9,599 QTL, nine of which coincided with previously identified lifespan QTLs, and were largely related to immune regulation. These findings provide a rich multiomic and genetic resource for the aging research community.

systems biology↗

bioRxiv2024

Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice

Blood plasma is one of the most commonly analyzed and easily accessible biological samples. Here, we describe an automated liquid-liquid extraction (LLE) platform that generates accurate, precise, and reproducible samples for metabolomic, lipidomic, and proteomic analyses from a single aliquot of plasma while minimizing hands-on time and avoiding contamination from plasticware. We applied mass spectrometry to examine the metabolome, lipidome, and proteome of 90 plasma samples to determine the effects of age, time of day, and a high-fat diet in mice. From 25 L of mouse plasma, we identified 907 lipid species from 16 different lipid classes and subclasses, 233 polar metabolites, and 344 proteins. We found that the high-fat diet induced only mild changes in the polar metabolome, upregulated Apolipoproteins, and induced substantial shifts in the lipidome, including a significant increase in arachidonic acid (AA) and a decrease in eicosapentaenoic acid (EPA) content across all lipid classes.

physiology↗

bioRxiv2020

Precise Estimation of In Vivo Protein Turnover Rates

Isotopic labeling with deuterium oxide (D2O) is a common technique for estimating in vivo protein turnover, but its use has been limited by two long-standing problems: (1) identifying non-monoisotopic peptides; and (2) estimating protein turnover rates in the presence of dynamic amino acid enrichment. In this paper, we present a novel experimental and analytical framework for solving these two problems. Peptides with high probabilities of labeling in many amino acids present fragmentation spectra that frequently do not match the theoretical spectra used in standard identification algorithms. We resolve this difficulty using a modified search algorithm we call Conditional Ion Distribution Search (CIDS). Increased identifications from CIDS along with direct measurement of amino acid enrichment and statistical modeling that accounts for heterogeneous information across peptides, dramatically improves the accuracy and precision of half-life estimates. We benchmark the approach in cells, where near-complete labeling is possible, and conduct an in vivo experiment revealing, for the first time, differences in protein turnover between mice and naked mole-rats commensurate with their disparate longevity.

bioinformatics↗

bioRxiv2018

Incorporating in-source fragment information improves metabolite identification accuracy in untargeted LC-MS datasets

[ABSTRACT]In-source fragmentation occurs as a byproduct of electrospray ionization. We find that ions produced as a result of in-source fragmentation often match fragment ions produced during MS/MS fragmentation and we take advantage of this phenomenon in a novel algorithm to analyze LC-MS metabolomics datasets. Our approach organizes co-eluting MS1 features into a single peak group and then identifies in-source fragments among co-eluting features using MS/MS spectral libraries. We tested our approach using previously published data of verified metabolites, and compared the results to features detected by other mainstream metabolomics tools. Our results indicate that considering in-source fragment information as a part of the identification process increases annotation quality, allowing us to leverage MS/MS data in spectrum libraries even if MS/MS scans were not collected.

bioinformatics↗