Biology subjects

Sakai, Y.

Publications and source records attributed to Sakai, Y..

63 records · Page 4Linked to original sources

bioRxiv2020

Effects of water content and mesh size on tea bag decomposition

The tea bag method was developed to provide uniform litter bags that enable comparison of organic matter decomposition rates on a large scale. However, it remains uncertain whether tea bag decomposition in response to wetness is representative of that of natural litters. We performed incubation experiments to examine whether the effect of soil water on tea bag decomposition becomes inhibitory at higher water contents, as was demonstrated in natural leaf litters. In addition, we performed field studies in a mixed forest and cedar plantation in Japan to compare two litter bag mesh sizes: 0.25-mm mesh, the size previously used by a major manufacturer of tea bags (Lipton), and nonwoven bags with mesh sizes finer than 0.25 mm, which are currently produced by Lipton. Both green tea and rooibos tea exhibited higher decomposition rates at higher water contents, but decomposition was inhibited at the highest water content, consistent with conceptual models of natural litters. The nonwoven tea bags did not show lower decomposition rates, despite the finer mesh size. Rather, the nonwoven rooibos tea bags exhibited slightly higher decomposition rates than the 0.25-mm mesh bags in the cedar plantation, possibly due to a greater abundance of microorganisms that decompose litters in the nonwoven bags, due to the decrease in predation by mesofauna. Our findings provide essential information for future studies of tea bag decomposition.

ecology↗

bioRxiv2020

Trace Imbalance in Reinforcement and Punishment Systems Can Mis-reinforce Implicit Choices Leading to Anxiety

We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we model obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the reinforcement learning framework show that agents implicitly learn to respond to intrusive thoughts when the memory trace signal for past actions decays differently for positive and negative prediction errors. Moreover, this model extends our understanding of therapeutic effects of behavioral therapy in OCD. Using empirical data, we confirm that patients with OCD show extremely imbalanced traces, which are normalized by serotonin enhancers. We find that healthy participants also vary in their obsessive-compulsive tendencies, consistent with the degree of imbalanced traces. These behavioral characteristics can be generalized to variations in the healthy population beyond the spectrum of clinical phenotypes.

neuroscience↗

bioRxiv2020

Generation of human bronchial organoids for SARS-CoV-2 research

Coronavirus disease 2019 (COVID-19) is a disease that causes fatal disorders including severe pneumonia. To develop a therapeutic drug for COVID-19, a model that can reproduce the viral life cycle and evaluate the drug efficacy of anti-viral drugs is essential. In this study, we established a method to generate human bronchial organoids (hBO) from commercially available cryopreserved human bronchial epithelial cells and examined whether they could be used as a model for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) research. Our hBO contain basal, club, ciliated, and goblet cells. Angiotensin-converting enzyme 2 (ACE2), which is a receptor for SARS-CoV-2, and transmembrane serine proteinase 2 (TMPRSS2), which is an essential serine protease for priming spike (S) protein of SARS-CoV-2, were highly expressed. After SARS-CoV-2 infection, not only the intracellular viral genome, but also progeny virus, cytotoxicity, pyknotic cells, and moderate increases of the type I interferon signal could be observed. Treatment with camostat, an inhibitor of TMPRSS2, reduced the viral copy number to 2% of the control group. Furthermore, the gene expression profile in SARS-CoV-2-infected hBO was obtained by performing RNA-seq analysis. In conclusion, we succeeded in generating hBO that can be used for SARS-CoV-2 research and COVID-19 drug discovery. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/115600v2_ufig1.gif" ALT="Figure 1"> View larger version (99K): org.highwire.dtl.DTLVardef@13a6908org.highwire.dtl.DTLVardef@1c59300org.highwire.dtl.DTLVardef@362167org.highwire.dtl.DTLVardef@1cb31ed_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2020

Inhibition of TRPV1 and TRPA1 by mosquito and mouse saliva

Arthropods are the largest group of living organisms and among them, mosquitoes spread parasites and viruses causing deathly diseases. Mosquitos painless piercing is due to the thinness of their fascicle, but mosquito saliva might also contribute to it. We found that mosquito and mouse saliva inhibit TRPV1 and TRPA1 channels, either heterologously expressed in HEK293T cells or endogenously expressed in native mouse sensory neurons. We have also identified sialorphin as a candidate antinociceptive peptide as it showed similar effects on TRPV1 and TRPA1. Finally, we confirmed the antinociceptive effects of saliva and sialorphin in vivo by observing decreased pain-related behaviors in mice coinjected with these substances. Similar inhibitory effects of mosquito and mouse saliva suggest that the antinociceptive effects of saliva are universal, which could explain why many animals including humans often lick their wounds. These findings would lead to the development of novel anti-nociceptive agents.

cell biology↗

bioRxiv2020

Common brain networks between major depressive disorder and symptoms of depression that are validated for independent cohorts

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable major depressive disorder (MDD) brain network markers which would distinguish patients from healthy controls (a classifier) or would predict symptom severity (a prediction model) based on resting state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD brain network markers. The classifier achieved 70% generalization accuracy, and the prediction model moderately well predicted symptom severity for an independent validation dataset with 449 participants from 4 different imaging sites. Finally, we found common 2 functional connections between those related to MDD diagnosis and those related to depression symptoms. The successful generalization to the perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

neuroscience↗

bioRxiv2019

White Matter Microstructure and its Relation to Clinical Features of Obsessive-Compulsive Disorder: Findings from the ENIGMA OCD Working Group

ImportanceMicrostructural alterations in cortico-subcortical connections are thought to be present in Obsessive-Compulsive Disorder (OCD). However, prior studies have yielded inconsistent findings, perhaps because small sample sizes provided insufficient power to detect subtle abnormalities. ObjectiveTo investigate microstructural white matter alterations and their relation to clinical features in the largest dataset of adult and pediatric OCD to date. Design, Setting, and ParticipantsIn this cross-sectional case-control magnetic resonance study, we investigated diffusion tensor imaging metrics from 700 adult patients and 645 adult controls, as well as 174 pediatric patients and 144 pediatric controls across 19 sites participating in the ENIGMA-OCD Working Group. Main Outcomes and MeasuresWe extracted measures of fractional anisotropy (FA) as main outcome, and mean diffusivity, radial diffusivity, and axial diffusivity as secondary outcomes for 25 white matter regions. We meta-analyzed patient-control group differences (Cohens d) across sites, after adjusting for age and sex, and investigated associations with clinical characteristics. ResultsAdult OCD patients showed significant FA reduction in the sagittal stratum (d=-0.21, z=-3.21, p=0.001) and posterior thalamic radiation (d=-0.26, z=-4.57, p<0.0001). In the sagittal stratum only, lower FA was associated with a younger age of onset (z=2.71, p=0.006), longer duration of illness (z=-2.086, p=0.036) and a higher percentage of medicated patients in the cohorts studied (z=-1.98, p=0.047). No significant association with symptom severity was found. Pediatric OCD patients did not show any detectable microstructural abnormalities compared to matched controls. Conclusions and RelevanceMicrostructural alterations in projection and association fibers to posterior brain regions were found in adult OCD, and related to disease course and medication status. Such results are relevant to models positing deficits in connectivity as a crucial mechanism in OCD. KEY POINTSO_ST_ABSQuestionC_ST_ABSDo patients with Obsessive-Compulsive Disorder (OCD) show white matter microstructural alterations, and are these alterations related to clinical features? FindingsData from 19 sites of the ENIGMA-OCD Consortium were included, involving 700 adult patients and 645 adult controls, 174 pediatric patients and 144 pediatric controls. Diffusion tensor imaging data were meta-analyzed using a harmonized data processing and analysis protocol. Adult, but not pediatric, patients showed alterations in the sagittal stratum and posterior thalamic radiation; sagittal stratum differences were associated with clinical features. MeaningMicrostructural abnormalities found in adult but not in the pediatric cohort, are related to illness duration and medication status.

neuroscience↗

bioRxiv2018

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

When collecting large neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent the greatest barrier when acquiring multi-site neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multi-site, multi-disorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. Effects on resting-state functional MRI connectivity because of both bias types were greater than or equal to those because of psychiatric disorders. Furthermore, our findings indicated that each site can sample only from among a subpopulation of participants. This result suggests that it is essential to collect large neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using traveling-subject dataset and achieved the reduction of the measurement bias by 29% and the improvement of the signal to noise ratios by 40%.

neuroscience↗

bioRxiv2017

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Individual differences in cognitive function have been shown to correlate with brain-wide functional connectivity, suggesting a common foundation relating connectivity to cognitive function across healthy populations. However, it remains unknown whether this relationship is preserved in cognitive deficits seen in a range of psychiatric disorders. Using machine learning methods, we built a prediction model of working memory function from whole-brain functional connectivity among a healthy population (N = 17, age 19-24 years). We applied this normative model to a series of independently collected resting state functional connectivity datasets (N = 968), involving multiple psychiatric diagnoses, sites, ages (18-65 years), and ethnicities. We found that predicted working memory ability was correlated with actually measured working memory performance in both schizophrenia patients (partial correlation,{rho} = 0.25, P = 0.033, N = 58) and a healthy population (partial correlation,{rho} = 0.11, P = 0.0072, N = 474). Moreover, the model predicted diagnosis-specific severity of working memory impairments in schizophrenia (N = 58, with 60 controls), major depressive disorder (N = 77, with 63 controls), obsessive-compulsive disorder (N = 46, with 50 controls), and autism spectrum disorder (N = 69, with 71 controls) with effect sizes g = -0.68, -0.29, -0.19, and 0.09, respectively. According to the model, each diagnosiss working memory impairment resulted from the accumulation of distinct functional connectivity differences that characterizes each diagnosis, including both diagnosis-specific and diagnosis-invariant functional connectivity differences. Severe working memory impairment in schizophrenia was related not only with fronto-parietal, but also widespread network changes. Autism spectrum disorder showed greater negative connectivity that related to improved working memory function, suggesting that some non-normative functional connections can be behaviorally advantageous. Our results suggest that the relationship between brain connectivity and working memory function in healthy populations can be generalized across multiple psychiatric diagnoses. This approach may shed new light on behavioral variances in psychiatric disease and suggests that whole-brain functional connectivity can provide an individual quantitative behavioral profile in a range of psychiatric disorders.

neuroscience↗

bioRxiv2017

A common neural network among state, trait, and pathological anxiety from whole-brain functional connectivity

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a neural network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related neural network derived from whole brain. Through the fMRI task for provoking anxiety, we found a common neural network of state anxiety across participants (1,638 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants trait anxiety scale (879 participants from the Human Connectome Project), we demonstrated that trait anxiety also shared the same neural network as state anxiety. Furthermore, the common neural network between state and trait anxiety could detect patients with obsessive-compulsive disorder, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety are not completely independent but have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.

neuroscience↗