Noa Rappaport

This study identifies that plasma proteins selectively reflect tissue-specific aging processes in muscle and ovary, revealing context-dependent age-related changes.

A Biolink-compliant graph (∼15 million nodes, ∼113 million edges) adds RefMet and LIPID MAPS metabolite and lipid classifications, plus polygenic score and clinical data element entities, to Translator content.

This paper introduces a benchmarking framework for predicting perturbation signatures across biological contexts, showing that projection and network-based methods perform well across diverse datasets and contexts.

The paper develops metaSPLiT, a single-cell RNA sequencing method, to reveal sub-species functional specialization in the human gut microbiome.

This study identifies shared metabolic changes across five longevity interventions in mice, highlighting ergothioneine and lipid remodeling as key features.

The paper shows that modulating mitochondrial complex I in advanced Alzheimer's disease improves metabolic resilience through coordinated mitochondrial and lipid metabolism.

The paper identifies metabolomic signatures associated with extreme old age using data from the New England Centenarian Study and links them to biological age and mortality risk.

The paper uses machine learning to identify plasma metabolomic and proteomic features that distinguish slow-aging mice from controls, finding triglycerides with varying fatty acid chain lengths as key predictors.

This study uses multiomic analysis to identify compartmentalized immune responses and tissue-vascular signatures in Lyme disease, linking local immune processes to systemic symptoms.

The paper validates and expands a serum protein signature associated with APOE genotypes, identifying new protein associations and potential targets for Alzheimer's disease prevention.

The paper identifies APOE genotype-related metabolite associations with biological age and bioenergetics in humans, showing counterintuitive patterns in {varepsilon}2 and {varepsilon}4 carriers.

The paper examines the link between bowel movement frequency and blood metabolites in healthy individuals, finding associations with kidney function and chronic disease risk.

This paper uses microbial community-scale metabolic modeling to predict individual-specific short chain fatty acid production profiles in the human gut and links these predictions to clinical health markers.

The paper analyzes the genetic and microbiome contributions to blood metabolite variation across 2,049 individuals, finding that over 60% of metabolites are associated with either genetics or the microbiome, with some showing interaction effects.

This study identifies blood-based biomarkers associated with Alzheimer's disease genetic risk variants in a large cohort of healthy adults, showing effects from early adulthood and sex-specific patterns.

The paper compares microglial transcriptomic responses to Aβ peptide aggregates ex vivo and in vivo, finding distinct responses and highlighting the limitations of using ex vivo models to predict in vivo behavior.

The paper shows that gut microbiome uniqueness increases with age and predicts survival in humans.

The paper identifies a blood metabolome signature that robustly predicts gut microbiome α-diversity across health and disease conditions.