Biology subjects

Oh, I.

Publications and source records attributed to Oh, I..

5 recordsLinked to original sources

bioRxiv2026

Batch Effect Correction to Enhance Utility of a Publicly Available ALS Proteomics Dataset

Large, publicly available databases of patient data have been successful in identifying associations between neurodegenerative diseases and biological features that might otherwise go unnoticed. The Answer ALS Consortium is one such database that has generated a rich repository of multi-omic data from people with amyotrophic lateral sclerosis (ALS). However, large studies can suffer from batch effects that obscure genuine biological signal and prevent experiment reproducibility. In this study, we identified a batch effect in the Answer ALS 1-2 proteomic dataset (508 samples x 3,643 proteins) and corrected the effect using both the internal controls provided by Answer ALS and the batch effect-correction algorithm (BECA) ComBat. We identified a batch effect in the Answer ALS Dataset 1-2 proteomic dataset that, uncorrected, may obscure biology. Proteins MOS and H4 were outliers of expression in the dataset that correlated with the batch effect. Removing MOS and H4 did not correct the effect fully but did reveal new principal component (PC) contributing proteins related to motor neurons and potentially ALS: AINX, NFL, and NFM. The batch differential controls (BDCs) provided by Answer ALS were used to correct samples in their respective batches. ComBat, a well-characterized BECA, was applied to the dataset both before (ComBat-BDC) and after (BDC-ComBat) correction with the BDCs. BDC-ComBat was the only method that successfully reduced batch effect across the entire dataset. This method also preserved four of the top six PC-contributing proteins from the original dataset, including three neuronal proteins, suggesting that the corrected dataset recapitulates biology as well as the variability of the original dataset. Altogether, we produced a version of the Answer ALS proteomic dataset (354 samples by 3,641 proteins) with globally reduced batch effect and protein-level reduced batch effect. This version of the dataset recapitulates the individual protein variability and important neuronal proteins of the original. We also created a workflow for identifying and correcting batch effects that can be applied to other large data repositories.

bioinformatics↗

bioRxiv2024

Revealing heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementias underlying heterogeneity, compared to analyzing the entire cohort as a single homogeneous group. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.

neuroscience↗

bioRxiv2023

Evaluation of purine-nucleoside degrading ability and in vivo uric acid lowering of Streptococcus thermophilus (ID-PDP3), a novel antiuricemia strain

This study evaluated 15 lactic acid bacteria in terms of their ability to degrade inosine and hypoxanthine--which are the intermediates in purine metabolism--for the management of hyperuricemia and gout. After a preliminary screening based on HPLC, CR1 (Lactiplantibacillus plantarum) and GZ1 (Lactiplantibacillus pentosus) showed the highest nucleoside degrading rates and were therefore selected for further characterization. IDCC 2201 (S. thermophilus), which possessed the hpt gene encoding hypoxanthine-guanine phosphoribosyltransferase (HGPRT) and exhibited purine degradation, was also selected. These three selected strains were examined in terms of the effect of probiotics on lowering serum uric acid in a rat model of potassium oxonate (PO)-induced hyperuricemia. Among them, the level of serum uric acid was most reduced by IDCC 2201 (p < 0.05). Further, analysis of the microbiome showed that administration of IDCC 2201 recovered the ratio of Bacteroidetes/Firmicutes in the intestinal microbial composition unbalanced by hyperuricemia and showed a difference in the intestinal microbial composition compared to the group administered with allopurinol. Moreover, intestinal short-chain fatty acids (SCFAs) were significantly increased. Ultimately, the findings show that IDCC 2201 lowers uric acid levels by degrading purine-nucleosides and restores intestinal flora and SCFAs, ultimately suggesting that IDCC 2201 is a promising candidate for use as an adjuvant treatment in patients with hyperuricemia.

microbiology↗

bioRxiv2022

Loss of estrogen unleashing neuro-inflammation increases the risk of Alzheimer's disease in women

The risk of Alzheimers disease (AD) in women is about 2 times greater than in men. The estrogen hypothesis is being accepted as the essential sex factor causing the sex difference in AD. Also, the recent meta-analysis using large-scale medical records data indicated estrogen replacement therapy. However, the underlying molecular targets and mechanisms explaining this sex difference in AD disease development remain unclear. In this study, we identified that estrogen treatment can strongly inhibition of neuro-inflammation signaling targets, using the systems pharmacology model; and identified ESR1/ESR2 (the receptors of estrogen) are topologically close to the neuroinflammation biomarker genes using signaling network analysis. Moreover, the estrogen level in women decreased to an extremely lower level than in men after age 55. Pooling together the multiple pieces of evidence, it is concluded that the loss of estrogen unleashing neuro-inflammation increases the womens risk of Alzheimers disease. These analysis results provide novel supporting evidence explaining the potential mechanism of the anti-neuroinflammation role of estrogen causing the sex difference of AD. Medications boosting the direct downstream signaling of ESR1/ESR2, or inhibiting upstream signaling targets of neuroinflammation, like JAK2 inhibitors, on the signaling network can be potentially effective or synergistic combined with estrogen for AD prevention and treatment.

neuroscience↗

bioRxiv2021

An Atlas of Cortical Arealization Identifies Dynamic Molecular Signatures

The human brain is subdivided into distinct anatomical structures. The neocortex, one of these structures, enables higher-order sensory, associative, and cognitive functions, and in turn encompasses dozens of distinct specialized cortical areas. Early morphogenetic gradients are known to establish an early blueprint for the specification of brain regions and cortical areas. Furthermore, recent studies have uncovered distinct transcriptomic signatures between opposing poles of the developing neocortex1. However, how early, broad developmental patterns result in finer and more discrete spatial differences across the adult human brain remains poorly understood2. Here, we use single-cell RNA-sequencing to profile ten major brain structures and six neocortical areas during peak neurogenesis and early gliogenesis. Our data reveal that distinct cell subtypes are predominantly brain-structure specific. Within the neocortex, we find that even early in the second trimester, a large number of genes are differentially expressed across distinct cortical areas in all cell types, including radial glia, the neural progenitors of the cortex. However, the abundance of areal transcriptomic signatures increases as radial glia differentiate into intermediate progenitor cells and ultimately give rise to excitatory neurons. Using an automated, multiplexed single-molecule fluorescent in situ hybridization (smFISH) approach, we validated the expression pattern of area-specific neuronal genes and also discover that laminar gene expression patterns are highly dynamic across cortical regions. Together, our data suggest that early cortical areal patterning is defined by strong, mutually exclusive frontal and occipital gene expression signatures, with resulting gradients giving rise to the specification of areas between these two poles throughout successive developmental timepoints.

developmental biology↗