Biology subjects

Hong, E. L.

Publications and source records attributed to Hong, E. L..

6 recordsLinked to original sources

bioRxiv2026

The single nucleotide polymorphism rs1053230 modulates kynurenine 3-monooxygenase stability and is associated with cognitive and mood phenotypes

BackgroundThe single nucleotide polymorphism (SNP) rs1053230 within the kynurenine 3-monooxygenase (KMO) gene encodes either an arginine (CGC) or cysteine (TGC) at amino acid residue 452. The rs1053230 genotype is associated with alterations in KMO expression and activity, and impaired cognition. Additionally, KMO intronic SNP rs2275163 is associated with schizophrenia endophenotypes. However, the direct functional consequences of these SNPs on KMO function have never been investigated. MethodsHere we performed the first in vitro cell-based examination of the rs1053230 genotype on KMO expression, activity, cellular localisation and KMO-protein interactions, as well as examination of the effects of rs1053230 on schizophrenia-relevant clinical measures. We also examined the effects of rs2275163 genotype on KMO pre-mRNA stability and alternative splicing. ResultsHEK293T cells expressing KMO-Arg452 or KMO-Cys452 with a red fluorescent protein (RFP) tag produced equivalent levels of KMO mRNA, protein and enzymatic activity, and localised to mitochondria to the same extent. However, cycloheximide-mediated inhibition of protein translation revealed a striking reduction in protein stability of KMO-Arg452-RFP. KMO-RFP-trap pull-down followed by tandem liquid-chromatography-mass spectrometry (LC-MS/MS) identified dramatic differences in protein partners between KMO variants. Indeed, gene ontology-term enrichment analysis revealed that terms associated with synaptic function were more highly enriched amongst KMO-Cys452 interacting proteins. rs1053230 genotype was found to associate with chronic, trait-like depressive mood symptoms in patients. rs2275163 genotype had no effect on KMO pre-mRNA. ConclusionsDifferences in protein stability and protein-protein interactions may underlie the mechanisms by which the KMO rs1053230 genotype influences neuronal function, leading to cognitive differences in psychiatric conditions.

molecular biology↗

bioRxiv2026

Identification of a miRNA signature for schizophrenia in plasma-derived extracellular vesicles

Background and Hypothesis: Extracellular vesicles (EVs) are phospholipid bilayer vesicles released from cells containing proteins, lipids and nucleic acids derived from the parent cell. Alterations in miRNA expression within blood-derived EVs have been proposed as potential biomarkers of disease. Specifically, identification of differentially expressed miRNAs in patients with schizophrenia (SZ) compared to healthy individuals could be used as a "miRNA signature" to aid in diagnosis and treatment. We therefore aimed to identify differentially expressed miRNAs in plasma-derived EVs between people living with SZ and healthy controls and to correlate miRNA levels with SZ-relevant clinical measures. Study Design: Plasma-derived EVs were isolated from a cohort of 33 individuals with SZ and 34 controls. Expression of 84 miRNAs was examined using a RT-qPCR panel. Study Results: Three miRNAs (hsa-miR-30e-5p, hsa-miR-103a-3p, hsa-miR-200b-3p) were differentially expressed between controls and patients. Gene ontology analysis of putative target genes shared between these miRNAs revealed enrichment of biological process terms related to neurogenesis. Analysis of miRNA expression compared to clinical measures showed that hsa-miR-103a-3p expression was associated with working memory and negatively correlated with white matter integrity in the combined patient-control group. Conclusions: We have identified a miRNA signature for SZ in plasma-derived EVs and shown for the first time that hsa-miR-30e-5p expression is significantly increased in plasma-derived EVs in SZ. The genetic links between differentially expressed miRNAs and neurogenesis, along with the correlations of hsa-miR-103a-3p with working memory and white matter integrity may underlie the functional importance of altered expression of the identified miRNAs in SZ.

genetics↗

bioRxiv2025

Mitochondrial Response to Psychological Stress and Its Medial Prefrontal Biomarker Correlates

BackgroundStress response obligates increased mitochondrial activities to meet stress-induced high energy requirement. This stress-mitochondrial response process involves glucocorticoid but also multiple alternative pathways that are top-down regulated by the medial prefrontal cortex (mPFC). These pathways are important for many neuropsychiatric conditions that are sensitive to stress. However, the field lacks a reliable, clinically accessible stress-mitochondrial response paradigm to study the process in humans. MethodWe used an established psychological stress challenge combined with assaying salivary cell-free mitochondrial DNA (cf-mtDNA), thought to reflect heightened mitochondrial changes or disruptions, in 35 healthy individuals (21 males). We also explored if these stress-induced cf-mtDNA marker elevations were associated brain metabolites as measured by magnetic resonance spectroscopy (MRS), as well as high-resolution brain imaging based cortical thickness focusing on the mPFC. ResultsWe found that salivary cf-mtDNA was significant elevated immediately after the stress challenge (p=2.0x10-7) and gradually declined after. Exploratory causal analysis showed that this cf-mtDNA response was not primarily driven by cortisol response. Instead, individuals with higher baseline dACC lactate+ levels, thought to in part reflect mitochondrial dysfunctions, was significantly associated with the cf-mtDNA response (r=0.80, p<0.001). Higher mtDNA response was also significantly associated with thinner dorsomedial prefrontal cortex (r=-0.52, p=0.01). Age had a U-shape effect such that cf-mtDNA response trended lower in earlier adulthood but higher in older people, explaining 33.8% of the ct-mtDNA response variance (p=0.003). ConclusionThis stress challenge-salivary cf-mtDNA assay paradigm may offer a new, non-invasive approach to evaluate the stress-mitochondrial pathway functioning in aging, psychopharmacology, and neuropsychiatric conditions where psychological stress plays a role.

neuroscience↗

bioRxiv2025

Blood and Neuronal Extracellular Vesicle Mitochondrial Disruptions in Schizophrenia

The high energy demand of the human brain obligates robust mitochondrial energy metabolism, while mitochondrial dysfunctions have been linked to neuropsychiatric disorders including schizophrenia spectrum disorders (SSD). However, in vivo assessments that can directly inform brain mitochondrial functioning and its etiopathophysiological path to SSD remain difficult to obtain. We hypothesized that system and brain mitochondrial dysfunctions in SSD may be indexed by elevated cell-free mitochondrial DNA (cf-mtDNA) levels in the blood and in neuronal extracellular vesicles (nEVs). We also explored if these mtDNA marker elevations were associated brain metabolites as measured by magnetic resonance spectroscopy (MRS). We examined blood cf-mtDNA in 58 SSD patients and 33 healthy controls, followed by assessing nEV mtDNA and metabolite levels using MRS in a subgroup of patients and controls. We found that people with SSD had significantly elevated cf-mtDNA levels in both the blood (p=0.0002) and neuronal EVs (p=0.003) compared to controls. These mtDNA abnormalities can be linked back to brain lactate+ levels such that higher blood and nEV mtDNA levels were significantly associated with higher lactate+ levels measured at the anterior cingulate cortex (r=0.53, 0.53; p=0.008, 0.03, respectively) in SSD patients. Furthermore, higher developmental stress and trauma were significantly associated with higher cf-mtDNA levels in both the blood and neuronal EVs in SSD patients (r=0.29, 0.49; p=0.01, 0.03, respectively). In conclusion, if replicated and fully developed, blood and neuronal EV-based cell free mtDNA may provide a clinically accessible biomarker to more directly evaluate the mitochondrial hypothesis and the abnormal bioenergetics pathways in schizophrenia.

neuroscience↗

bioRxiv2023

The ENCODE Uniform Analysis Pipelines

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses. Database URL: https://www.encodeproject.org/

bioinformatics↗

bioRxiv2020

Ancestry Inference Using Reference Labeled Clusters of Haplotypes

We present ARCHes, a fast and accurate haplotype-based approach for inferring an individuals ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations to 1,001 sections of a genotype using 10 CPU). We test ARCHes on public data from the 1,000 Genomes Project and HGDP as well as simulated examples of known admixture. Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture. Author SummaryHuman DNA is inherited from ancestors that come from different populations across the globe and across time. Being able to identify which of those populations make up an individuals DNA, how much they contribute, and on which chromosomes, is currently an important open research problem with many applications in the study of human diversity and history. As DNA sequencing and genotyping technology has developed, we have greater and greater amounts of data, which allows for the development of new sophisticated machine learning methods to approach this problem, and presents a need to process large amounts of data efficiently. These methods learn from examples of DNA data from known populations, and must be robust to differences in size and diversity among those reference populations. We present a new approach to this problem called ARCHes (Ancestry inference using Reference labeled Clusters of Haplotypes), that models the global diversity of small segments of human DNA sequence ("haplotypes"), and the extent to which these haplotypes are associated with each of a set of population reference panels. It then computes the most likely population assignments and the points along the genome where the populations change. Our experiments show that ARCHes has superior accuracy compared to a state-of-the-art method in identifying source populations and their locations on the genome, regardless of the number of different populations present in the genome, how closely related those populations are. ARCHes is also able to model populations despite having a small amount of population reference DNA data.

genomics↗