Biomedical subjects

Stephen J Glatt

Publications and source records attributed to Stephen J Glatt.

2 recordsLinked to original sources

PubMed2026

Predicted brain-regional gene expression patterns in individuals living with Alzheimer's disease.

Studying brain gene expression in Alzheimer's Disease (AD) remains difficult as postmortem brain is difficult to access, cannot be used to guide donor treatment, may be confounded by environmental factors before and after death, and is difficult to link to early AD states or disease progression. To circumvent these limitations, several studies have tested blood transcriptome biomarkers for AD. However, gene-expression levels in the blood have limited correlation with those in the brain. To evaluate the potential of monitoring Alzheimer's progression with peripheral data, we used transcriptome-imputation to identify brain-region-specific AD-associated gene-expression differences in cohorts with blood-based transcriptome data. This approach provides a high-resolution image of AD-associated molecular differences in the brains of individuals actively living with disease. We analyzed eight AD studies (777 AD cases, 779 cognitively unimpaired controls), imputing transcriptomes in 10 brain regions via the Brain Gene Expression and Network Imputation Engine (BrainGENIE). Hundreds of differentially expressed genes (DEGs) associated with AD were identified in nine brain regions, with anterior cingulate cortex and amygdala showing the most differential expression. AD-associated genes were enriched in pathways such as proteostasis, mitochondrial dysfunction, and immune activation. We observed significant yet moderate concordance between imputed AD-associated changes and those directly measured in the dorsolateral prefrontal cortex and cerebellum. These transcriptomic changes can guide future in vitro studies focused on pathogenesis or be targets of novel therapeutic development. In conclusion, we demonstrated the scope and utility of brain expression imputation from the peripheral transcriptome, laying the groundwork for biomarker discovery and prospective AD studies.

Alzheimer Disease↗

PubMed2025

LungGENIE: the lung gene-expression and network imputation engine.

BACKGROUND: Few cohorts have study populations large enough to conduct molecular analysis of ex vivo lung tissue for genomic analyses. Transcriptome imputation is a non-invasive alternative with many potential applications. We present a novel transcriptome-imputation method called the Lung Gene Expression and Network Imputation Engine (LungGENIE) that uses principal components from blood gene-expression levels in a linear regression model to predict lung tissue-specific gene-expression. METHODS: We use paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project to train LungGENIE models. We replicate model performance in a unique dataset, where we generated RNA sequencing data from paired lung and blood samples available through the SUNY Upstate Biorepository (SUBR). We further demonstrate proof-of-concept application of LungGENIE models in an independent blood RNA sequencing data from the Genetic Epidemiology of COPD (COPDGene) study. RESULTS: We show that LungGENIE prediction accuracies have higher correlation to measured lung tissue expression compared to existing cis-expression quantitative trait loci-based methods (median Pearson's r = 0.25, IQR 0.19-0.32), with close to half of the reliably predicted transcripts being replicated in the testing dataset. Finally, we demonstrate significant correlation of differential expression results in chronic obstructive pulmonary disease (COPD) from imputed lung tissue gene-expression and differential expression results experimentally determined from lung tissue. CONCLUSION: Our results demonstrate that LungGENIE provides complementary results to existing expression quantitative trait loci-based methods and outperforms direct blood to lung results across internal cross-validation, external replication, and proof-of-concept in an independent dataset. Taken together, we establish LungGENIE as a tool with many potential applications in the study of lung diseases.

Humans↗