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Kato, N.

Publications and source records attributed to Kato, N..

33 records · Page 2Linked to original sources

Molecular basis for two stereoselective Diels-Alderases that produce decalin skeletons

Molecular chirality, discovered by Louis Pasteur in the middle of the 19th century1, is found in most primary and secondary metabolites. Particularly, the so-called natural products are rich in chiral centres2. The stereochemistry of natural products is strictly recognized in living organisms, and is thus closely related to their biological functions. The Diels-Alder (DA) reaction, which forms a six-membered ring with up to four chiral centres, is a fundamental practical reaction for C-C bond formation in synthetic chemistry3. Nature has also adopted this reaction to elaborate the complex structures of natural products using enzymes derived from various progenitor proteins4-7. Although enzymes catalysing the DA reaction, Diels-Alderases (DAases), have attracted increasing attention, little is known about the molecular mechanism by which they control the stereochemistry and perform catalysis. Here, we solved the X-ray crystal structures of a pair of decalin synthases, Fsa2 and Phm7, that catalyse intramolecular DA reactions to form enantiomeric decalin scaffolds during biosynthesis of the HIV-1 integrase inhibitor equisetin and its stereochemical opposite, phomasetin8,9. Based on the crystal structures, docking simulations followed by all-atom molecular dynamics simulations provided dynamic binding models demonstrating the folding of linear polyenoyl tetramic acid substrates in the binding pocket of these enzymes, explaining the stereoselectivity in the construction of decalin scaffolds. Site-directed mutagenesis studies verified the binding models and, in combination with density functional theory calculations, clarified how hydrophilic amino acid residues in the Phm7 pocket regulate and catalyse the stereoselective DA reaction. This study highlights the distinct molecular mechanisms of the enzymatic DA reaction and its stereoselectivity experimentally and computationally. We anticipate that clarified molecular mechanism herein provides not only the basic understanding how these important enzymes work but also the guiding principle to create artificial enzymes that produce designer bioactive molecules.

biochemistry↗

Whole-genome sequence analysis of mutations in rice plants regenerated from zygotes, mature embryos, and immature embryos

Somaclonal variation was studied by whole-genome sequencing in rice plants (Oryza sativa L., Nipponbare) regenerated from the zygotes, mature embryos, and immature embryos of a single mother plant. The mother plant and its seed-propagated progeny were also sequenced. A total of 338 variants of the mother plant sequence were detected in the progeny, and mean values ranged from 9.0 of the seed-propagated plants to 37.4 of regenerants from mature embryos. The ratio of single nucleotide variants among the variants was 74.3%, and the natural mutation rate calculated using the variants in the seed-propagated plants was 1.2 x 10-8. The percentage and the mutation rate were consistent with the values reported previously. Plants regenerated from mature embryos had significantly more variants than different progeny types. Therefore, using zygotes and immature embryos can reduce somaclonal variation during the genetic manipulation of rice.

plant biology↗

Orthosteric-allosteric dual inhibitors of PfHT1 as selective anti-malarial agents

Artemisinin-resistant malaria parasites have emerged and been spreading, posing a significant public health challenge. Anti-malarial drugs with novel mechanisms of action are therefore urgently needed. In this report, we exploit a "selective starvation" strategy by selectively inhibiting Plasmodium falciparum hexose transporter 1 (PfHT1), the sole hexose transporter in Plasmodium falciparum, over human glucose transporter 1 (hGLUT1), providing an alternative approach to fight against multidrug-resistant malaria parasites. Comparison of the crystal structures of human GLUT3 and PfHT1 bound to C3361, a PfHT1-specific moderate inhibitor, revealed an inhibitor binding-induced pocket that presented a promising druggable site. We thereby designed small-molecules to simultaneously block the orthosteric and allosteric pockets of PfHT1. Through extensive structure-activity relationship (SAR) studies, the TH-PF series was identified to selectively inhibit PfHT1 over GLUT1 and potent against multiple strains of the blood-stage P. falciparum. Our findings shed light on the next-generation chemotherapeutics with a paradigm-shifting structure-based design strategy to simultaneously targeting the orthosteric and allosteric sites of a transporter. Significance statementBlocking sugar uptake in P. falciparum by selectively inhibiting the hexose transporter PfHT1 kills the blood-stage parasites without affecting the host cells, indicating PfHT1 as a promising therapeutic target. Here, we report the development of novel small-molecule inhibitors that are selectively potent to the malaria parasites over human cell lines by simultaneously targeting the orthosteric and the allosteric binding sites of PfHT1. Our findings established the basis for the rational design of next-generation anti-malarial drugs.

pharmacology and toxicology↗

Nonlinear ridge regression improves robustness of cell-type-specific differential expression studies

BackgroundEpigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity. ResultsFirst, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data. ConclusionNonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas

bioinformatics↗

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↗

The CDK inhibitor SIAMESE targets both CDKA;1 and CDKB1 complexes to establish endoreplication in trichomes

Endoreplication, also known as endoreduplication, is a modified cell cycle in which DNA is replicated without subsequent cell division. Endoreplication plays important roles in both normal plant development and in stress responses. The SIAMESE (SIM) gene of Arabidopsis (Arabidopsis thaliana) encodes a cyclin-dependent kinase inhibitor that plays an central role in establishing endoreplication, and is the founding member of the SIAMESE-RELATED (SMR) family of plant-specific cyclin-dependent kinase inhibitors genes. However, there has been conflicting evidence regarding which specific cyclin/CDK complexes are inhibited by SIM in vivo. In this work, we use genetic evidence to show that SIM likely inhibits both CDKA;1- and CDKB1-containing CDK complexes in vivo to promote endoreplication in developing Arabidopsis trichomes. We also show that SIM interacts with CYCA2;3, a binding partner of CDKB1;1, via SIM Motif A, which we previously identified as a CDK-binding motif. In contrast, SIM Motif C, which has been indicated as a cyclin binding motif in other contexts, appears to be relatively unimportant for interaction between SIM and CYCA2;3. Together with earlier results, our work suggests that SIM and other SMRs likely have a multivalent interaction with CYC/CDK complexes. One sentence summaryThe cyclin-dependent kinase inhibitor SIAMESE (SIM) targets both CDKA;1 and CDKB1 complexes to establish endoreplication, and that SIM interacts with the cyclin CYCA2;3 via SIM Motif A.

plant biology↗

White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile

BackgroundAutism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) have high rates of co-occurrence and share atypical behavioral characteristics, including sensory problems. The present diffusion tensor imaging (DTI) study was conducted to examine whether and how white matter abnormalities are observed in adult populations with developmental disabilities (DD) and to determine how brain-sensory relationships are either shared between or distinct to ASD and ADHD.\n\nMethodsWe collected DTI data from adult developmental disorder (DD) populations (a primary diagnosis of ASD: n=105, ADHD: n=55) as well as age and sex matched typically developed (TD) participants (n=58). Voxel-wise fractional anisotropy (FA), mean diffusivity, axial diffusivity, and radial diffusivity (RD) were analyzed using tract-based spatial statistics. The severities of sensory problems were assessed using the Adolescent/Adult Sensory Profile (AASP).\n\nResultsCategorical analyses identified voxel clusters showing significant effects of DD on FA and RD in the posterior portion of the corpus callosum and its extension in the right hemisphere. Furthermore, regression analyses using the AASP scores revealed that slopes in relationships of FA or RD with the degree of sensory problems were parallel between the two DDs in large parts of the affected corpus callosum regions, although a small but significant cluster did exist showing interaction between the diagnosis of DD and an AASP subscale score on RD.\n\nConclusionsThese results indicate that white matter abnormalities and their relationships to sensory problems are largely shared between ASD and ADHD, with localized abnormalities showing significant between-diagnosis differences within DD. (247 words)

neuroscience↗

Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

AimPrior structural MRI studies demonstrated atypical gray matter characteristics in siblings of individuals with autism spectrum disorder (ASD). However, they did not clarify which aspect of gray matter presents the endophenotype. Further, because they did not enroll siblings of TD people, they underestimated the difference between individuals with ASD and their unaffected siblings. The current study aimed to solve these questions.\n\nMethodsWe recruited 30 pairs of adult male siblings (15 of them have an ASD endophenotype, other 15 pairs not) and focused on four gray matter parameters: cortical volume and three surface-based parameters (cortical thickness, fractal dimension, and sulcal depth [SD]). First, we sought to identify a pattern of an ASD endophenotype, comparing the four parameters. Then, we compared individuals with ASD and their unaffected siblings in the cortical parameters to identify neural correlates for the clinical diagnosis accounting for the difference between TD siblings.\n\nResultsA sparse logistic regression with a leave-one-pair-out cross-validation showed the highest accuracy for the identification of an ASD endophenotype (73.3%) with the SD compared with the other three parameters. A bootstrapping analysis accounting for the difference in the SD between TD siblings showed a significantly large difference between individuals with ASD and their unaffected siblings in six out of 68 regions-of-interest accounting for multiple comparisons.\n\nConclusionsThis proof-of-concept study suggests that an ASD endophenotype emerges in SD and that neural correlates for the clinical diagnosis can be dissociated from the endophenotype when we accounted for the difference between TD siblings. (248/250 words)

neuroscience↗

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↗

Overlapping but asymmetrical relationships between schizophrenia and autism revealed by brain connectivity

(Abstract included 248 words)Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. To address this issue, we took advantage of dual (ASD and SSD) classifiers that discriminate patients from their controls based on resting state brain functional connectivity. An SSD classifier using sophisticated machine-learning algorithms that automatically selected SSD- specific functional connections was applied to Japanese datasets including adult patients with SSD in a chronic stage. We demonstrated good performance of the SSD classification for independent validation cohorts. The generalizability was tested by USA and European cohorts in a chronic stage, and one USA cohort including first episode schizophrenia. The specificity was tested by two adult Japanese cohorts of ASD and major depressive disorder, and one European cohort of attention-deficit hyperactivity disorder. The weighted linear summation of the classifiers functional connections constituted the biological dimensions representing neural liability to the disorders. Our previously developed robust ASD classifier constituted the ASD dimension. Distributions of individuals with SSD, ASD and healthy controls were examined on the SSD and ASD biological dimensions. The SSD and ASD populations exhibited overlapping but asymmetrical patterns on the two biological dimensions. That is, the SSD population showed increased liability on the ASD dimension, but not vice versa. Furthermore, the two dimensions were correlated within the ASD population but not the SSD population. Using the two biological dimensions based on resting-state functional connectivity enabled us to quantify and visualize the relationships between SSD and ASD.

neuroscience↗

High-level expression of STING restricts susceptibility to HBV by mediating type III IFN induction

Hepatitis B virus (HBV) is a hepatotropic DNA virus causing hepatic diseases such as chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. To study HBV, human hepatoma HepG2 cells are currently used as an HBV infectious cell culture model worldwide. HepG2 cells exhibit susceptibility to HBV by exogenously expressing sodium taurocholate cotransporting polypeptide (NTCP). We herein demonstrated that human immortalized hepatocyte NKNT-3 cells exhibited susceptibility to HBV by exogenously expressing NTCP (NKNT-3/NTCP cells). By comparing the cGAS-STING signaling pathway in several NKNT-3/NTCP cell-derived cell clones, we found that STING was highly expressed in cell clones exhibiting resistance but not susceptibility to HBV. High-level expression of STING was implicated in HBV-triggered induction of type III IFN and a pro-inflammatory cytokine, IL-6. In contrast, RNAi-mediated knockdown of STING inhibited type III IFN induction and restored the levels of HBV total transcript in an HBV-infected cell clone exhibiting resistance to HBV. These results suggest that STING regulates susceptibility to HBV by its expression levels.\n\nSTING may thus be a novel target for anti-HBV strategies.

microbiology↗

Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

Endophenotype refers to a measurable and heritable component between genetics and diagnosis and exists in both individuals with a diagnosis and their unaffected siblings. We aimed to identify a pattern of endophenotype consisted of multiple connections. We enrolled adult male individuals with autism spectrum disorder (ASD) endophenotype (i.e., individuals with ASD and their unaffected siblings) and individuals without ASD endophenotype (i.e., pairs of typical development (TD) siblings) and utilized a machine learning approach to classify people with and without endophenotypes, based on resting-state functional connections (FCs). A sparse logistic regression successfully classified people as to the endophenotype (area under the curve=0.78, classification accuracy=75%), suggesting the existence of endophenotype pattern. A binomial test identified that nine FCs were consistently selected as inputs for the classifier. The least absolute shrinkage and selection operator with these nine FCs predicted severity of communication impairment among individuals with ASD (r=0.68, p=0.021). In addition, two of the nine FCs were statistically significantly correlated with the severity of communication impairment (r=0.81, p=0.0026 and r=-0.60, p=0.049). The current findings suggest that an ASD endophenotype pattern exists in FCs with a multivariate manner and is associated with clinical ASD phenotype.

neuroscience↗

Four assessment methods to measure student gains in a graduate course on mathematical modeling in cell biology

The push for mathematics and quantitative skill development in biology is greater than ever before. The perceptions of these techniques are a barrier to their implementation on a grand scale. Methods to assess the efficacy of approaches to teaching quantitative skills are needed to determine if they effectively overcome social stereotypes and fears of math. In our project we study the effect of a mathematical modeling course in cell biology. We developed four assessments to understand if hands-on modeling increases student understanding of the concepts while simultaneously reducing fear of math. Two concept inventories track students learning gains both in general and specific knowledge to the course content. Two surveys quantify students quantitative confidence and opinions of the usefulness of these techniques in biology. We discuss the effectiveness of our methods and the implications for assessment development in mathematical biology education.

scientific communication and education↗

Aberrant cerebellar-default-mode functional connectivity underlying auditory verbal hallucinations in schizophrenia revealed by multi-voxel pattern analysis of resting-state functional connectivity MRI data

Past neuroimaging studies have reported that aberrant functional connectivity (FC) underlying auditory verbal hallucinations (AVHs) in schizophrenia is highly distributed over multiple functional networks. There is thus a need for exploratory approaches without limiting analysis to particular seed regions or functional networks, to identify FC alterations underlying AVH. We applied a multi-voxel pattern analysis (MVPA) of FC together with a series of post-hoc FC analyses to resting-state fMRI data acquired from 25 patients with schizophrenia and 25 matched healthy controls. First, the MVPA revealed multiple clusters exhibiting altered FC patterns in schizophrenia. Subsequent multiple linear regression analysis using scores of these clusters identified that FC alteration in the right cerebellum crus I was significantly associated with the severity of AVH. Furthermore, post-hoc FC analysis with the right crus I as a seed revealed significant FC alterations with regions distributed across multiple functional networks, including speech, default-mode, thalamus, and cerebellum. Subsequent linear regression analyses further demonstrated that, among these regions, only reduced FC in the left precuneus was significantly associated with the severity of AVH. Our unbiased exploratory analysis of FC data revealed a novel evidence for the crucial role of FC between cerebellar and default-mode networks in AVH. (198 words)

neuroscience↗

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↗