Structural insights into the octamerization of glycerol dehydrogenase

Glycerol dehydrogenase (GDH) catalyzes glycerol oxidation to dihydroxyacetone in a NAD+-dependent manner. As an initiator of the oxidative pathway of glycerol metabolism, a variety of functional and structural studies of GDH have been conducted previously. Structural studies revealed intriguing features of GDH, like the flexible β-hairpin and its significance. Another commonly reported structural feature is the enzyme's octameric oligomerization, though its structural details and functional significance remained unclear. Here, with a newly reported GDH structure, complexed with both NAD+ and glycerol, we analyzed the octamerization of GDH. Structural analyses revealed that octamerization reduces the structural dynamics of the N-domain, which contributes to more consistently maintaining a distance required for catalysis between the cofactor and substrate. This suggests that octamerization may play a key role in increasing the likelihood of the enzyme reaction by maintaining the ligands in an appropriate configuration for catalysis. These findings expand our understanding of the structure of GDH and its relation to the enzyme's activity.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Vowel speech recognition from rat electroencephalography using long short-term memory neural network

Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen’s κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

Lipid raft-disrupting miltefosine preferentially induces the death of colorectal cancer stem-like cells

BACKGROUND

Lipid rafts (LRs), cholesterol-enriched microdomains on cell membranes, are increasingly viewed as signalling platforms governing critical facets of cancer progression. The phenotype of cancer stem-like cells (CSCs) presents significant hurdles for successful cancer treatment, and the expression of several CSC markers is associated with LR integrity. However, LR implications in CSCs remain unclear.

METHODS

This study evaluated the biological and molecular functions of LRs in colorectal cancer (CRC) by using an LR-disrupting alkylphospholipid (APL) drug, miltefosine. The mechanistic role of miltefosine in CSC inhibition was examined through normal or tumour intestinal mouse organoid, human CRC cell, CRC xenograft and miltefosine treatment gene expression profile analyses.

RESULTS

Miltefosine suppresses CSC populations and their self-renewal activities in CRC cells, a CSC-targeting effect leading to irreversible disruption of tumour-initiating potential in vivo. Mechanistically, miltefosine reduced the expression of a set of genes, leading to stem cell death. Among them, miltefosine transcriptionally inhibited checkpoint kinase 1 (CHEK1), indicating that LR integrity is essential for CHEK1 expression regulation. In isolated CD44high CSCs, we found that CSCs exhibited stronger therapy resistance than non-CSC counterparts by preventing cell death through CHEK1-mediated cell cycle checkpoints. However, inhibition of the LR/CHEK1 axis by miltefosine released cell cycle checkpoints, forcing CSCs to enter inappropriate mitosis with accumulated DNA damage and resulting in catastrophic cell death.

CONCLUSION

Our findings underscore the therapeutic potential of LR-targeting APLs for CRC treatment that overcomes the therapy-resistant phenotype of CSCs, highlighting the importance of the LR/CHEK1 axis as a novel mechanism of APLs.