Biology subjects

Hwang, H.-J.

Publications and source records attributed to Hwang, H.-J..

6 recordsLinked to original sources

bioRxiv2026

Methanogenic Ethanol Production from Acetate

Biological ethanol production is important for the circular carbon economy and makes up 73% of the U.S. biological fuels market. Previously, we produced ethanol by reversing methanogenesis and capturing methane by cloning methyl-coenzyme M reductase (Mcr) from an unculturable population of anaerobic methanotrophic archaea; this process was predicated on the generation of the intermediate acetate and its conversion by the methanogenic host to ethanol. Moreover, methanogens are generally thought to be detrimental for converting acetate to ethanol and are usually intentionally inhibited. Here, we demonstrate that direct growth on acetate as the sole carbon and energy source by the methanogen Methanosarcina acetivorans C2A results in 40% of the metabolized acetate becoming ethanol and that there is 430% more ethanol produced, compared to growth on methane via Mcr. In addition, we found growth on methanol results primarily in methane generation and low levels of ethanol. Therefore, acetate may be readily converted by the methanogen M. acetivorans to ethanol at high yields.

microbiology↗

bioRxiv2026

Enhancing Reverse Methanogenesis Using RNA-Guided Silencing

Utilizing methane and carbon dioxide before it can enter the upper atmosphere is beneficial for mitigating climate change as well as for producing valuable chemicals. Because anaerobic methanotrophic archaea (ANME) have not yet been cultured in isolation, we previously reversed methanogenesis by cloning the genes encoding methyl-coenzyme M reductase (Mcr) derived from Black Sea ANME-1 into the methanogen Methanosarcina acetivorans. The resulting engineered archaeal strain captures, rather than produces, methane and may be used to convert methane and carbon dioxide into electricity, acetate, L-lactate, and ethanol. However, the engineered M. acetivorans strain also contains a chromosomal locus encoding its native Mcr (McrM.a.), which produces methane from substrates such as methanol, whereas the heterologously expressed ANME-1 Mcr (McrANME-1) promotes methane oxidation. Therefore, we reasoned that McrM.a. may compete with McrANME-1-mediated reversal of methanogenesis. To enhance the reversal of methanogenesis, here we implemented an antisense RNA (asRNA) silencing approach to suppress McrM.a. during growth on methane while still allowing its expression during routine growth on methanol. We found that silencing McrM.a. during McrANME-1-mediated growth on methane increased ethanol and acetate production by more than an order of magnitude. These results were corroborated by both a more than 10-fold increase in methane utilization by McrANME-1 and a greater than 1,000-fold reduction in the McrM.a. mcrBGA transcript levels under methane-grown conditions. Therefore, asRNA-mediated silencing may be used to enhance methane capture by suppressing production of the host McrM.a. for biotechnological applications.

synthetic biology↗

bioRxiv2025

Persister Cells Form Based on Low Ribosome Content

Persister cells survive any severe stress including antibiotics, starvation, heat, oxidative conditions, and phage attack, by entering a dormant physiological state. They arise without genetic change and can resume growth once the stress is removed and nutrients are available. Critically, upon resuscitation, persister cells can reconstitute infections. Although it is known that persister cells resuscitate in proportion to their ribosome content, it has remained unclear whether ribosome levels also influence the formation of persister cells. Here, we used fluorescence-activated cell sorting (FACS) to fractionate exponentially growing cells into four populations spanning low to high ribosome levels and demonstrated that cells with low ribosome content form persister cells approximately 80-fold more frequently than cells with population-average ribosome levels. These findings show that persister cell formation is inversely proportional to ribosome abundance. Cells with low ribosome levels are less metabolically-active and therefore less capable of initiating a stress response like most cells; instead, they become dormant.

microbiology↗

bioRxiv2024

Motor Hotspot Localization Based on Electroencephalography Using Convolutional Neural Network in Patients with Stroke

BackgroundAlthough transcranial magnetic stimulation (TMS) is the optimal tool for identifying individual motor hotspots for transcranial electrical stimulation (tES), it requires a cumbersome procedure in which patients must visit the hospital each time and rely on expert judgment to determine the motor hotspot. Therefore, in previous study, we proposed electroencephalography (EEG)-based machine learning approach to automatically identify individual motor hotspots. In this study, we proposed an advanced EEG-based motor hotspot identification algorithm using a deep learning model and assessed its clinical feasibility and benefits by applying it to stroke patient EEGs. MethodsEEG data were measured from thirty subjects as they performed a simple hand movement task. We utilized the five types of input data depending on the processing levels to assess the signal processing capability of our proposed deep learning model. The motor hotspot locations were estimated using a two-dimensional convolutional neural network (CNN) model. The error distance between the 3D coordinate information of the individual motor hotspots identified by the TMS (ground truth) and EEGs was calculated using the Euclidean distance. Additionally, we confirmed the clinical benefits of our proposed deep-learning algorithm by applying the EEG of stroke patients. ResultsA mean error distance between the motor hotspot locations identified by TMS and our approach was 2.34 {+/-} 0.19 mm when using raw data from only 9 channels around the motor area. When it was tested on stroke patients, the mean error distance was 1.77 {+/-} 0.15 mm using only 5 channels around the motor area. ConclusionWe have demonstrated that an EEG-based deep learning approach can effectively identify the individual motor hotspots. Moreover, we validated the clinical benefits of our algorithm by successfully implementing it in stroke patients. Our algorithm can be used as an alternative to TMS for identifying motor hotspots and maximizing rehabilitation effectiveness.

bioengineering↗

bioRxiv2022

Investigation of neuromodulatory effect of anodal cerebellar transcranial direct current stimulation on the primary motor cortex using functional near-infrared spectroscopy

BackgroundCerebellar brain inhibition (CBI), a neural connection between the cerebellum and primary motor cortex (M1), has been researched as a target pathway for neuromodulation to improve clinical outcomes in various neurological diseases. However, conflicting results of anodal cerebellar transcranial direct current stimulation (acb-tDCS) on M1 excitability indicate that additional investigation is required to examine its precise effect. Objective/HypothesisThis study aimed to gather evidence of the neuromodulatory effect of acb-tDCS on the M1 using functional near-infrared spectroscopy (fNIRS). MethodsSixteen healthy participants were included in this cross-over study. Participants received real and sham acb-tDCS in a random order, with a minimum one-week washout period between them. The anode and cathode were placed on the right cerebellum and the right buccinator muscle, respectively. Stimulation lasted 20 min at an intensity of 2 mA, and fNIRS data were recorded for 42 min (including a 4 min baseline before stimulation and an 18 min post-stimulation duration) using eight channels attached bilaterally on the M1. Resultsacb-tDCS induced a significant decrease in oxyhemoglobin (HbO) concentration (inhibitory effect) in the left (contralateral) M1, whereas it induced a significant increase in HbO concentration (excitatory effect) in the right (ipsilateral) M1 compared to sham tDCS during (p < 0.05) and after stimulation (p < 0.01) in a group level analysis. At the individual level, variations in the response to acb-tDCS were observed. ConclusionOur findings demonstrate the neuromodulatory effects of acb-tDCS on the bilateral M1 in terms of neuronal hemodynamics.

neuroscience↗

bioRxiv2021

An artificial Neural-Network Approach for Motor Hotspot Identification Based on Electroencephalography: A Proof-of-Concept Study

BackgroundTo apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. MethodsEEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility. ResultsThe minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 {+/-} 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 {+/-} 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels. ConclusionWe demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.

bioengineering↗