Biology subjects

Matsubara, T.

Publications and source records attributed to Matsubara, T..

21 records · Page 2Linked to original sources

bioRxiv2019

GABA neurons in the ventral tegmental area regulate non-rapid eye movement sleep in mice

The daily sleep/wakefulness cycle is regulated by coordinated interactions between sleep- and wakefulness-regulating neural circuitry. However, the detailed neural circuitry mediating sleep is far from understood. Here, we found that glutamic acid decarboxylase 67 (Gad67)-positive GABAergic neurons in the ventral tegmental area (VTAGad67+) are a key regulator of non-rapid eye movement (NREM) sleep in mice. VTAGad67+ neurons project to multiple brain areas implicated in sleep/wakefulness regulation such as the lateral hypothalamus (LH) and dorsal raphe nucleus. Chemogenetic activation of VTAGad67+ neurons promoted NREM sleep with higher delta power whereas optogenetic inhibition of these neurons induced prompt arousal from NREM sleep under highly somnolescent conditions, but not during REM sleep. In vivo fiber photometry recordings revealed that VTAGad67+ neurons showed the highest population activity in NREM sleep and the lowest activity in REM sleep. Acute brain slice electrophysiology combined with optogenetics revealed that VTAGad67+ neurons directly innervate and inhibit wake-promoting orexin/hypocretin neurons in the LH by releasing GABA. Taken together, we reveal that VTAGad67+ neurons play a crucial role in the regulation of NREM sleep.

neuroscience↗

bioRxiv2018

Microbiome composition comparison in oral and atherosclerotic plaque from patients with and without periodontitis

There is no conclusive evidence regarding a causal relationship between periodontitis and atherosclerosis. In this study, we examined the microbiome in the oral cavity and atheromatous plaques from atherosclerosis patients with or without periodontitis to investigate the role of oral bacteria in the formation of atheromatous plaques. We chose four patients with and without periodontitis, who had undergone carotid endarterectomy. Bacterial samples were extracted from saliva on the tongue surface, from periodontal pocket (during the oral examination), and from the atheromatous plaques. We investigated the general and oral conditions from each patient and performed next-generation sequencing analysis for all bacterial samples. There were no significant differences between both groups concerning general conditions. However, the microbiome patterns of the gingival pocket showed differences depending on the absence or presence of periodontitis, while those of the saliva were relatively similar. The microbiome pattern of the atheromatous plaques was entirely different from that in saliva present on the tongue surface and gingival pocket, and oral bacteria were seldom detected. However, the microbiome pattern in atheromatous plaques was different in the presence or absence of periodontitis. These results indicated that oral bacteria did not affect the formation of atheromatous plaques directly. However, the metabolic products of microbiome or the host inflammatory response might indirectly influence the composition of atheromatous plaques.

microbiology↗

bioRxiv2017

Classification and characterisation of brain network changes in chronic back pain: a multicenter study.

Chronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

neuroscience↗