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Hannah Lee

Publications and source records attributed to Hannah Lee.

3 recordsLinked to original sources

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs.

Schizophrenia (SCZ) is a common psychiatric disorder characterized by psychosis, emotional withdrawal, and cognitive deficits. Most SCZ risk variants reside in non-coding regions of the genome and are thought to influence disease risk by modulating gene regulation. However, the target genes, biological pathways, and cell types through which these variants exert their effects remain poorly understood. To address this gap, we employed in vivo CRISPR droplet sequencing (CROP-seq) in the postnatal mouse neocortex. We perturbed 12 SCZ risk genes previously linked to functionally validated risk variants, followed by single-cell RNA sequencing. We identified 3,031 differentially expressed genes (DEGs) that recapitulate transcriptional alterations observed in postmortem SCZ brains. Integrative analysis using DEG clustering, factor analysis, and gene regulatory network inference uncovered convergent gene programs with distinct biological functions and cell type specificity. Notably, ciliary transcriptional programs consistently emerged across analytical frameworks. The primary cilium is a neurocircuit modulating signaling organelle in neurons and glia that remains understudied in SCZ. Perturbation of key contributors to the ciliary transcriptional programs led to significant alterations in ciliary structure, suggesting that SCZ genetic risk factors may influence how brain cells sense and transduce extracellular signals through synapse-independent mechanisms. Together, this study provides the first in vivo characterization of the functional consequence of common variant architecture in SCZ and implicates ciliary dysfunction as a convergent downstream mechanism.

Journal Article↗

Nrf2 is a critical regulator of the innate immune response and survival during experimental sepsis.

Host genetic factors that regulate innate immunity determine susceptibility to sepsis. Disruption of nuclear factor-erythroid 2-related factor 2 (Nrf2), a basic leucine zipper transcription factor that regulates redox balance and stress response, dramatically increased the mortality of mice in response to endotoxin- and cecal ligation and puncture-induced septic shock. LPS as well as TNF-alpha stimulus resulted in greater lung inflammation in Nrf2-deficient mice. Temporal analysis of pulmonary global gene expression after LPS challenge revealed augmented expression of large numbers of proinflammatory genes associated with the innate immune response at as early as 30 minutes in lungs of Nrf2-deficient mice, indicating severe immune dysregulation. The expression profile indicated that Nrf2 has a global influence on both MyD88-dependent and -independent signaling. Nrf2-deficient mouse embryonic fibroblasts showed greater activation of NF-kappaB and interferon regulatory factor 3 in response to LPS and polyinosinic-polycytidylic acid [poly(I:C)] stimulus, corroborating the effect of Nrf2 on MyD88-dependent and -independent signaling. Nrf2's regulation of cellular glutathione and other antioxidants is critical for optimal NF-kappaB activation in response to LPS and TNF-alpha. Our study reveals Nrf2 as a novel modifier gene of sepsis that determines survival by mounting an appropriate innate immune response.

Animals↗

Multiple-laboratory comparison of microarray platforms.

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

Baltimore↗