Biology subjects

Min, H.-K.

Publications and source records attributed to Min, H.-K..

4 recordsLinked to original sources

bioRxiv2023

Pan-cancer analysis of the ion permeome reveals functional regulators of glioblastoma aggression

Ion channels, transporters, and other ion-permeating proteins, collectively comprising the ion permeome (IP), are common drug targets. However, their roles in cancer are understudied. Our integrative pan-cancer analysis shows that IP genes display highly-elevated expression patterns in subsets of cancer samples significantly more often than expected transcriptome-wide. To enable target identification, we identified 410 survival-associated IP genes in 29 cancer types using a machine learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and associate with poor prognosis in glioblastoma (GBM), the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived GBM cells induces transcriptome-wide changes involving neural projection and proliferation pathways, impairs cell viability and tumor sphere formation, mitigates tunneling nanotube formation, and extends the survival of GBM-bearing mice. Thus, aberrant activation of IP genes appears as a pan-cancer feature of tumor heterogeneity that can be exploited for mechanistic insights and therapy development.

cancer biology↗

bioRxiv2022

Synthesizing Images of Tau Pathology from Cross-modal Neuroimaging using Deep Learning

Given the prevalence of dementia and the development of pathology-specific disease modifying therapies, high-value biomarker strategies to inform medical decision making are critical. In-vivo tau positron emission tomography (PET) is an ideal target as a biomarker for Alzheimers disease diagnosis and treatment outcome measure. However, tau PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that impute tau PET images from more widely-available cross-modality imaging inputs. Participants (n=1,192) with brain MRI, fluorodeoxyglucose (FDG) PET, amyloid PET, and tau PET were included. We found that a CNN model can impute tau PET images with high accuracy, the highest being for the FDG-based model followed by amyloid PET and MRI. In testing implications of AI-imputed tau PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and MRI-based models utilized the non-local input from physically remote ROIs to estimate the tau PET, but this was not the case for the PiB-based model. This implies that the model can learn the distinct biological relationship between FDG PET, MRI, and tau PET from the relationship between amyloid PET and tau PET. Our study suggests that extending neuroimagings use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

bioengineering↗

bioRxiv2019

Multivariate pattern classification on BOLD activation pattern induced by deep brain stimulation in motor, associative, and limbic brain networks

Functional magnetic resonance imaging (fMRI) concurrently conducted with the deep brain stimulation (DBS) has shown that diffuse BOLD activation occurred not only near stimulation locus, but in multiple brain networks, supporting that network-wide modulation would underlie its therapeutic effect. While the extent and pattern of activation varies depending on specific anatomical locus stimulated by DBS, some stimulation targets could induce similar activation pattern in cerebral cortex, albeit different therapeutic and adverse effects were yielded.\n\nIn order to characterize the unique network-level activation effects of three DBS targets (subthalamic nucleus, the globus pallidus internus, and the nucleus accumbens), we trained the pattern classifier with DBS-fMRI data from three stimulation groups (21 healthy swine), wherein five six seconds of electrical stimulation was conducted while gradient-echo echo planar imaging was on going. Then whole brain regions were systematically grouped into different size of network-of-interest and the classification accuracy for individual target region was quantitatively assessed. We demonstrated that the pattern classifier could successfully differentiate BOLD activation pattern of cortical and subcortical brain regions originated from each individual stimulation target. Moreover, the success rate of classification indicated that some brain regions evoked indistinguishable BOLD pattern, suggesting the presence of commonly activated regions, which was influenced by stimulating different DBS targets.\n\nOur results provide an understanding of the biomarker of BOLD pattern that is associated with clinical effectiveness as well as an adverse effect associated to the stimulation. Further, we provide the proof-of-concept for multivariate pattern analysis that is capable of disentangling the complicated BOLD activation pattern, which cannot be readily achieved by a conventional univariate analysis.

bioinformatics↗

bioRxiv2019

Resting-state functional connectivity modulates the BOLD activation induced by nucleus accumbens stimulation in the swine brain

While it is known that the clinical efficacy of deep brain stimulation (DBS) alleviates motor-related symptoms, cognitive and behavioral effects of DBS and its action mechanism on brain circuits are not clearly understood. By combining functional magnetic resonance imaging (fMRI) and DBS, we investigated the pattern of blood-oxygenation-level-dependent (BOLD) signal changes induced by stimulating the nucleus accumbens and how inter-regional resting-state functional connectivity is related with the stimulation DBS effect in a healthy swine model. We found that the pattern of stimulation-induced BOLD activation was diffused across multiple functional networks including the prefrontal, limbic, and thalamic regions, altering inter-regional functional connectivity after stimulation. Furthermore, our results showed that the strength of the DBS effect is closely related to the strength of inter-regional resting-state functional connectivity including stimulation locus and remote brain regions. Our results reveal the impact of nucleus accumbens stimulation on major functional networks, highlighting functional connectivity may mediate the modulation effect of DBS via large-scale brain networks.

neuroscience↗