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Okamoto, Y.

Publications and source records attributed to Okamoto, Y..

At least 19 recordsLinked to original sources

Metastable Dimeric Intermediate Drives Native-like Spidroin Fibrillogenesis, while Dehydration Promotes Cross-β Transition

The exceptional mechanical properties of spider dragline silk originate from hierarchical spidroin self-assembly, yet mimicking this process to generate native-like structures remains challenging. Here, we report a metastable dimeric intermediate as an essential molecular factor for fibrillogenesis. Small-angle X-ray scattering (SAXS) and atomic force microscopy (AFM) revealed a compact core stabilized by solvent-excluded "dry interfaces" surrounded by extended random coil regions. Within the core, a subset of the polyalanine blocks is held by side-chain packing rather than by main-chain {beta}-sheets; the majority remain exposed in the surrounding coil regions. Upon lowering the denaturant concentration, these exposed polyalanine segments pair with their counterparts at a fibril end, forming intermolecular {beta}-sheets that act as linkers and drive the stepwise end-to-end polymerization of the dimeric particles. In solution, the resulting fibrils exhibit an ordered {beta}-structure in which the polypeptide chains run parallel to the fibril axis, in contrast to amyloid-like cross-{beta}, in which the strands lie perpendicular to it; this matches key features of native silk nanofibril architecture. However, X-ray diffraction (XRD) analysis demonstrates that these native-like nanofibrils undergo an effectively irreversible transition toward amyloid-like cross-{beta} structures upon drying. Native-like fibrils are thus achievable by self-assembly, but structural control is lost during the final dehydration step. Accordingly, we propose a general principle for decoupling self-assembly from dehydration: native-like nanofibrils are formed first, then dried under conditions that restrict molecular-chain mobility (for example, mechanical constraint or immobilization on a surface), which we expect to suppress thermodynamic relaxation into cross-{beta} structures.

biophysics↗

Artificial initiation codons and engineered initiator tRNAs enable N-terminal noncanonical amino acid incorporation in intact cell-free translation systems

Noncanonical amino acid (ncAA) incorporation at the protein N-terminus provides a powerful strategy for installing defined chemical handles while minimizing perturbation of internal protein sequences. However, highly efficient initiation-based ncAA incorporation systems suppress the native methionine pathway by removing methionine or methionyl-tRNA synthetase, limiting their use for proteins containing internal methionine residues. Here, we developed an orthogonal initiation system for selective N-terminal ncAA incorporation into proteins in intact cell-free translation systems. We systematically profiled background initiation from all 64 codons in reconstituted translation systems and identified low-background artificial initiation codons. Engineered initiator tRNAs, termed tRNAIniTx, were then designed to decode selected codons and support ncAA-dependent initiation. The optimized CAC/tRNAIniTx04GUG pair enabled efficient N-terminal incorporation of N-biotinyl-L-phenylalanine without removing methionine or methionyl-tRNA synthetase, reaching over 90% incorporation. The system was further extended to p-azido-L-phenylalanine and to an Escherichia coli extract-based cell-free translation system. Finally, N-terminally biotinylated proteins were directly immobilized on streptavidin biosensors for purification-free biolayer interferometry analysis of computationally designed Brd4BD2 binders. This work establishes a codon-guided orthogonal initiation strategy for N-terminal protein functionalization while preserving the native methionine translation pathway.

synthetic biology↗

Lid speculum as effective active and reference electrodes for electroretinography recording in normal rabbits

PurposeAlthough electroretinography (ERG) is vital for evaluating retinal function, conventional corneal electrodes slide or detach in animals. This study aimed to investigate the effectiveness of a novel approach to ERG recording using a metal eyelid speculum for both active and reference electrodes in conjunction with a skin electrode-based ERG device. MethodsWe tested a stainless-steel eyelid speculum as both active and reference electrodes with a skin-electrode ERG system (HE-2000vet) in six healthy Japanese White rabbits. Dark-adapted rod and maximal responses and light-adapted cone and 30 Hz flicker ERGs were recorded in three weekly sessions. ResultsReproducible waveforms with identifiable a- and b-waves were obtained in every eye; rod b-waves reached 50-90 {micro}V and cone b-waves 40-55 {micro}V. Intraclass correlation coefficients revealed substantial interocular agreement and moderate-to-substantial inter-session reproducibility for b-wave amplitude and implicit time, whereas a-wave metrics were less reliable owing to lower amplitudes. The advantages of speculum electrode over corneal electrodes are that it requires no fur shaving, maintains stable contact regardless of globe orientation, and allows real-time observation. ConclusionsThis study demonstrated that an eyelid-speculum electrode is a practical, non-invasive alternative for veterinary and experimental ERG recordings, producing signal quality sufficient for longitudinal and interocular analyses while avoiding cosmetic and technical drawbacks of conventional methods.

neuroscience↗

Identification of Bone Morphogenetic Protein 7 as a Master Regulator of Gastric Cancer-Associated Cachexia

Cachexia is a devastating and multifactorial syndrome characterized by progressive loss of body weight, skeletal muscle wasting, and systemic inflammation, frequently observed in patients with advanced gastric cancer (GC) with peritoneal dissemination. Despite its clinical significance, the molecular mechanisms underlying cancer-associated cachexia remain poorly understood. In this study, comparative transcriptomic analysis using the GEMINI database identified bone morphogenetic protein 7 (BMP7) as a novel candidate cachexia-inducing factor, along with the known cachexia mediators, growth differentiation factor 11 (GDF11) and growth differentiation factor 15 (GDF15). Functional studies demonstrated that BMP7 acts as an upstream regulator that drives cachectic phenotypes by inducing the expression of GDF11 and GDF15. Knockdown of BMP7, GDF11, or GDF15 in the cachexia-inducing GC cell line, MKN45 significantly attenuated weight loss and muscle wasting in vivo. Conversely, overexpression of BMP7 in the non-cachectic GC cell line, NUGC3 induced cachexia and upregulated GDF11 and GDF15 in tumor tissues. Furthermore, clinical analysis revealed that high BMP7 expression in tumor specimens from patients with advanced GC was associated with significantly poorer overall survival. These findings identify BMP7 as a master regulator of cancer-associated cachexia through the induction of GDF11 and GDF15 and suggest its potential as a promising therapeutic target for mitigating cachexia in GC.

cancer biology↗

Senescent Activated Naive B Cells Promote Anti-Citrullinated Antigen T Cell Responses and the Transition to Clinical Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint and systemic inflammation. Anti-citrullinated protein antibodies (ACPAs) define an at-risk stage that precedes clinically apparent inflammatory arthritis (clinical RA) onset, yet the molecular mechanisms driving progression remain poorly understood. Here, we applied single-cell multi-omics to profile B cells longitudinally collected from ACPA individuals who either convert to clinical RA (Converters) or do not (Nonconverters). We identified a striking expansion of CXCR5CD69 activated naive B cells (aNAVs) uniquely in Converters prior to clinical RA. These aNAVs exhibited a pro-inflammatory, senescent transcriptional program and persist through to clinical RA. In Converters, aNAVs expressed polyreactive, autoreactive IgM with distinctive V-J gene rearrangements that dominate the BCR repertoire. Furthermore, in Converters most IgM aNAVs were developmentally arrested in the peripheral blood, while a subset undergoes class switching and follows divergent somatic hypermutation trajectories. Mechanistically, aNAVs infiltrated RA synovium and served as potent antigen presenting cells to activate both anti-citrullinated antigen CD4 and CD8 T cells in an HLA-dependent manner. Chronic exposure to citrullinated antigens and CpG synergistically drove aNAV activation and senescence. These findings establish a mechanistic link between naive B cell senescence and clinical RA development in ACPA+ individuals, providing a rationale for therapeutically targeting aNAV B cells for the prevention of RA. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/682430v1_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@153b185org.highwire.dtl.DTLVardef@1aba6eeorg.highwire.dtl.DTLVardef@5c886eorg.highwire.dtl.DTLVardef@10109a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

Unsupervised feature computation-based feature selection robustly extracted resting-state functional connectivity patterns related to mental disorders

Research on biomarkers for predicting psychiatric disorders from resting-state functional connectivity (FC) is advancing. While the focus has primarily been on the discriminative performance of biomarkers by machine learning, identification of abnormal FCs in psychiatric disorders has often been treated as a secondary goal. However, it is crucial to investigate the effect size and robustness of the selected FCs because they can be used as potential targets of neurofeedback training or transcranial magnetic stimulation therapy. Here, we incorporated approximately 5,000 runs of resting-state functional magnetic resonance imaging from six datasets, including individuals with three different psychiatric disorders (major depressive disorder [MDD], schizophrenia [SCZ], and autism spectrum disorder [ASD]). We demonstrated that an unsupervised feature-computation-based feature selection method can robustly extract FCs related to psychiatric disorders compared to other conventional supervised feature selection methods. We found that our proposed method robustly extracted FCs with larger effect sizes from the validation dataset compared to different types of feature selection methods based on supervised learning for MDD (Cohens d = 0.40 vs. 0.25), SCZ (0.37 vs. 0.28), and ASD (0.17 vs. 0.16). We found 78, 69, and 81 essential FCs for MDD, SCZ, and ASD, respectively, and these FCs were mainly thalamic and motor network FCs. The current study showed that the unsupervised feature-computation-based feature selection method robustly identified abnormal FCs in psychiatric disorders consistently across datasets. The discovery of such robust FCs will contribute to understanding neural mechanisms as abnormal brain signatures in psychiatric disorders. Furthermore, this finding can aid in developing precise therapeutic interventions, such as neurofeedback training or transcranial magnetic stimulation therapy.

neuroscience↗

Prediction of antidepressant treatment response with thalamo-somatomotor functional connectivity revealed by generalizable stratification of depressed patients

Major depressive disorder (MDD) is diagnosed based on symptoms and signs without relying on physical, biological, or cognitive tests. MDD patients exhibit a wide range of complex symptoms, and it is assumed that there are diverse underlying neurobiological backgrounds, possibly composed of several subtypes with relatively homogeneous biological features. Initiatives, including the Research Domain Criteria, emphasize the importance of biologically stratifying MDD patients into homogeneous subtypes using a data-driven approach while utilizing genetic, neuroscience, and cognitive information. If biomarkers can stratify MDD patients into biologically homogeneous subtypes at the first episode of depression, personalized precision medicine may be within our scope. Some pioneering studies have used resting-state functional brain connectivity (rs-FC) for stratification and predicted differential responses to various treatments for different subtypes. However, to our knowledge, little research has demonstrated reproducibility (i.e., generalizability) of stratification markers in independent validation cohorts. This issue may be due to inherent measurement and sampling biases in multi-site fMRI data, or overfitting of machine learning algorithms to discovery cohorts with small sample sizes, i.e., a lack of appropriate machine learning algorithms for generalizable stratification. To address this problem, we have constructed a multi-site, multi-disorder fMRI database with prospectively and retrospectively harmonized data from thousands of samples and proposed a hierarchical supervised/unsupervised learning strategy. In line with this strategy, our previous research first developed generalizable MDD diagnostic biomarkers using this fMRI database of MDD patients via supervised learning. The MDD diagnostic biomarker determines the importance of thousands to tens of thousands of rs-FCs across the whole brain for MDD diagnosis. In this study, we constructed stratification markers for MDD patients using unsupervised learning (Multiple co-clustering) with a subset of top-ranked rs-FCs in the MDD diagnostic biomarker. We developed a method to evaluate the clustering stability between two independent datasets as a generalization metric of stratification biomarkers. To discover stratification biomarkers with high stability across datasets, we utilized two multi-site datasets with substantial differences in data acquisition facilities and fMRI measurement protocols (Dataset-1: a dataset of 138 depressed patients obtained with a unified measurement protocol across three facilities; Dataset-2: a dataset of 181 depressed patients obtained with non-unified measurement protocols across four facilities, distinct from Dataset-1). Starting from several diagnostic biomarkers, we constructed some stratification markers and identified the stratification biomarker with the highest clustering stability between the two datasets. This stratification biomarker was based on several rs-FCs between the thalamus and the postcentral gyrus, and the MDD subgroups stratified by this biomarker showed significantly different treatment responsiveness to a selective serotonin reuptake inhibitor (SSRI). By narrowing down whole-brain rs-FCs using MDD diagnostic biomarkers and further dividing the rs-FCs using multiple co-clustering, the feature dimension was significantly reduced, thereby avoiding overfitting to the training data and successfully constructing stratification biomarkers that are highly stable between independent datasets, i.e., have generalizability. Furthermore, the correlation between MDD subgroups and antidepressant treatment response was demonstrated, suggesting the potential for achieving personalized precision medicine for MDD.

neuroscience↗

Computational Mechanisms of Neuroimaging Biomarkers Uncovered by Multicenter Resting-State fMRI Connectivity Variation Profile

Resting-state functional connectivity (rsFC) is increasingly used to develop biomarkers for psychiatric disorders. Despite progress, development of the reliable and practical FC biomarker remains an unmet goal, particularly one that is clinically predictive at the individual level with generalizability, robustness, and accuracy. In this study, we propose a new approach to profile each connectivity from diverse perspective, encompassing not only disorder-related differences but also disorder-unrelated variations attributed to individual difference, within-subject across-runs, imaging protocol, and scanner factors. By leveraging over 1500 runs of 10-minute resting-state data from 84 traveling-subjects across 29 sites and 900 participants of the case-control study with three psychiatric disorders, the disorder-related and disorder-unrelated FC variations were estimated for each individual FC. Using the FC profile information, we evaluated the effects of the disorder-related and disorder-unrelated variations on the output of the multi-connectivity biomarker trained with ensemble sparse classifiers and generalizable to the multicenter data. Our analysis revealed hierarchical variations in individual functional connectivity, ranging from within-subject across-run variations, individual differences, disease effects, inter-scanner discrepancies, and protocol differences, which were drastically inverted by the sparse machine-learning algorithm. We found this inversion mainly attributed to suppression of both individual difference and within-subject across-runs variations relative to the disorder-related difference by weighted-averaging of the selected FCs and ensemble computing. This comprehensive approach will provide an analytical tool to delineate future directions for developing reliable individual-level biomarkers.

neuroscience↗

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

The objective diagnostic and stratification biomarkers developed with resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for mental disorders. Unfortunately, there are currently no widely accepted biomarkers, partially due to the large variety of analysis pipelines for developing them. In this study we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD) (1162 participants from eight imaging sites). We explored the combinations of options in four subprocesses of analysis pipelines: six types of brain parcellation, four types of estimations of functional connectivity (FC), three types of site difference harmonization, and five types of machine learning methods. 360 different MDD diagnostic biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four imaging sites) as a discovery dataset and evaluated with datasets from other projects acquired with heterogeneous protocols (449 participants from four imaging sites) for independent validation. To identify the optimal options regardless of the discovery dataset, we repeated the same procedure after swapping the roles of the two datasets. We found pipelines that included Glassers parcellation, tangent-covariance, no harmonization, and non-sparse machine learning methods tended to result in high classification performance. The diagnosis results of the top 10 biomarkers showed high similarity, and weight similarity was also observed between eight of the biomarkers, except two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other mental disorders (autism spectral disorder: ASD and schizophrenia: SCZ) and eight of the ten biomarkers showed sufficient classification performances for both disorders, except two pipelines that included Pearson correlation, ComBat harmonization and random forest classifier combination. HighlightsO_LIWe evaluated the analysis pipelines of rsFC biomarker development. C_LIO_LIFour subprocesses in them were investigated with two multi-site datasets. C_LIO_LIGlassers parcellation, tangent covariance, and non-sparse methods were preferred. C_LIO_LIThe weight patterns of eight of the top 10 biomarkers showed high commonality. C_LIO_LIEight of the top 10 pipelines were successful for developing SCZ/ASD biomarkers. C_LI

neuroscience↗

Presynaptic cAMP-PKA-mediated potentiation induces reconfiguration of synaptic vesicle pools and channel-vesicle coupling at hippocampal mossy fiber boutons

It is widely believed that information storage in neuronal circuits involves nanoscopic structural changes at synapses, resulting in the formation of synaptic engrams. However, direct evidence for this hypothesis is lacking. To test this conjecture, we combined chemical potentiation, functional analysis by paired pre-postsynaptic recordings, and structural analysis by electron microscopy (EM) and freeze-fracture replica labeling (FRL) at the hippocampal mossy fiber synapse, a key synapse in the trisynaptic circuit of the hippocampus. Biophysical analysis of synaptic transmission revealed that forskolin-induced chemical potentiation increased the readily releasable vesicle pool size (RRP) and vesicular release probability (Pr) by 146% and 49%, respectively. Structural analysis of mossy fiber synapses by EM and FRL demonstrated an increase in the number of vesicles close to the plasma membrane and the number of clusters of the priming protein Munc13-1, indicating an increase in the number of both docked and primed vesicles. Furthermore, FRL analysis revealed a significant reduction of the nearest neighbor distance (NND) between Munc13-1 and CaV2.1 Ca2+ channels, suggesting reconfiguration of the channel-vesicle coupling nanotopography. Our results indicate that presynaptic plasticity is associated with structural reorganization of active zones (AZs). We propose that changes in potential nanoscopic organization at synaptic vesicle release sites may be correlates of learning and memory at a plastic central synapse.

neuroscience↗

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P<0.05, family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

neuroscience↗

A Large-Scale ENIGMA Multisite Replication Study of Brain Age in Depression

BackgroundSeveral studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived "brain age gap" has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N=2,126 controls & N=2,675 cases; +1.08 years [SE 0.22], Cohens d=0.14, 95% CI: 0.08-0.20), in independent cohorts that were not part of the original study. MethodsA previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scanners. ResultsOur ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2=0.47, MAE=7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohens d{square}={square}{square}.15, 95% CI: 0.05-0.25) compared with controls, highly similar to our previous finding. ConclusionsThis study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3,400 patients and >2,800 controls worldwide show reliable but subtle effects of brain aging in adult depression.

neuroscience↗

Discrimination of SARS-CoV-2 Omicron sub-lineages BA.1 and BA.2 using a high-resolution melting-based assay: A pilot study

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. As of March 2022, Omicron variant BA.2 is rapidly replacing variant BA.1. As variant BA.2 may cause more severe disease than variant BA.1, variant BA.2 requires continuous monitoring. The current study aimed to develop a novel high-resolution melting (HRM) assay for variants BA.1 and BA.2 and to determine the sensitivity and specificity of our method using clinical samples. Here, we focused on the mutational spectra at three regions in the spike receptor-binding domain (RBD; R408, G446/L452, and S477/T478) for the variant-selective HRM analysis. Each variant was identified based on the mutational spectra as follows: no mutations (Alpha variant); L452R and T478K (Delta variant); G446S and S477N/T478K (Omicron variant BA.1); and R408S and S477N/T478K (Omicron variant BA.2). Upon analysis of mutation-coding RNA fragments, the melting peaks of the wild-type fragments were distinct from those of the mutant fragments. The sensitivity and specificity of this method were determined as 100% and more than 97.5%, respectively, based on 128 clinical samples (40 Alpha, 40 Delta, 40 Omicron variants BA.1/BA.1.1, and 8 Omicron BA.2). These results suggest that this HRM-based assay is a promising screening method for monitoring the transmission of Omicron variants BA.1 and BA.2.

microbiology↗

Early Dynamics of Chronic Myeloid Leukemia on Nilotinib Predicts Deep Molecular Response

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase1,2. ABL1-selective tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML3-7. The ultimate goal of CML treatment is likely to be TKI-free maintenance of deep molecular response (DMR), which is defined by quantitative measurement of BCR-ABL1 transcripts on the international scale (IS)8, and durable DMR is a prerequisite to reach this goal9. Thus, an algorithm to enable the early prediction of DMR achievement on TKI therapy is quite valuable. Here, we show that our mathematical framework based on a clinical trial dataset10 can accurately predict the response to frontline nilotinib. We found that our simple dynamical model can predict nilotinib response by using two common laboratory findings (observation values): IS11,12 and white blood cell (WBC) count. Furthermore, our proposed method identified patients who failed to achieve DMR with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs in clinical practice. Significance StatementChronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. The goal of this treatment is the sequential achievement of deep molecular response (DMR). Tyrosine kinase inhibitors (TKIs) are effective in the reduction because they inhibit CML cell proliferation. However, because of individual differences in the TKI efficacy, some patients are unable to achieve DMR, so that early prediction of DMR reachability is necessary for personalized medicine. By combining time series analysis and mathematical modeling, we developed a mathematical method that accurately predicts patients who do not achieve DMR in the early stages of TKI administration. Our prediction method gives a basis of effective personalized treatments for CML patients.

systems biology↗

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

Neurofeedback (NF) aptitude, which refers to an individuals ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation systems characteristics in task-free resting-state brain activity.

neuroscience↗

Transsynaptic modulation of presynaptic short-term plasticity induction in hippocampal mossy fiber synapses

The hippocampal mossy fiber synapse is a key synapse of the trisynaptic circuit of the hippocampus. Post-tetanic potentiation (PTP) is the most powerful form of plasticity at this synaptic connection. It is widely believed that mossy fiber PTP is an entirely presynaptic phenomenon, implying that PTP induction is input-specific, and requires neither activity of multiple inputs nor stimulation of postsynaptic neurons for induction. Thus, mossy fiber PTP appears to lack cooperativity and associativity that characterize other forms of plasticity. To directly test these predictions, we made paired recordings between single mossy fiber terminals and postsynaptic CA3 pyramidal neurons in rat brain slices. By stimulating parallel but non-overlapping mossy fiber bouton (MFB) inputs converging onto single CA3 neurons, we confirmed that PTP was inputspecific and non-cooperative. Unexpectedly, mossy fiber PTP showed anti-associative induction properties. Mossy fiber excitatory postsynaptic currents (EPSCs) showed only minimal PTP after combined pre- and postsynaptic high-frequency stimulation (HFS) with intact postsynaptic Ca2+ signaling (0.1 mM EGTA), but marked PTP in the absence of postsynaptic spiking and after suppression of postsynaptic Ca2+ signaling (10 mM EGTA). PTP was rescued by blocking Ca2+ entry via voltage-gated R-type and to a smaller extent L-type Ca2+channels. PTP was also recovered by extracellular application of group II metabotropic glutamate receptor (mGluR) antagonists and vacuolar-type (v-type) H+-ATPase inhibitors, suggesting the involvement of retrograde vesicular glutamate signaling. Transsynaptic regulation of PTP induction may increase the computational power of mossy fiber synapses, and implement a break on hippocampal mossy fiber detonation.

neuroscience↗

Diploid male gametes circumvent hybrid sterility between Asian and African rice species

In F1 hybrids of Oryza sativa (Asian rice) and O. glaberrima (African rice), heterozygosity leads to a complete gamete abortion because of allelic conflict at each of the 13 hybrid sterility (HS) loci. We systematically produced 19 plants from the F1 hybrids of both the rice species by the anther culture (AC) method. Five of the 19 interspecific hybrid plants were fertile and able to produce seeds. Unlike ordinal doubled haploid plants resulting from AC, these regenerated plants showed various ploidy levels (diploid to pentaploid) and different zygosities (completely homozygous, completely heterozygous, and a combination). These properties were attributable to meiotic anomalies in the interspecific hybrid F1 plants. Examination of the genetic structures of the regenerated plants suggested meiotic non-reduction took place in the interspecific hybrid F1 plants. The centromeric regions in the regenerated plants revealed that the abnormal first and/or second divisions of meiosis, namely the first division restitution (FDR) and/or second division restitution (SDR), had occurred in the interspecific hybrid. Immunohistochemical observations also verified these phenomena. FDR and SDR occurrences at meiosis might strongly lead to the formation of diploid microspores. The results demonstrated that meiotic anomalies functioned as a reproductive barrier occurred before the HS genes acted in gamete of the interspecific hybrid. Although such meiotic anomalies are detrimental to pollen development, the early rescue of microspores carrying the diploid gamete resulted in the fertile regenerated plants. The five fertile plants carrying tetraploid genomes with heterozygous alleles of the HS loci produced fertile diploid pollens, implying that the diploid gametes circumvented the allelic conflicts at the HS loci. We also proposed how diploid male gametes avoid HS with the killer-protector model.

plant biology↗

Brain/MINDS Beyond Human Brain MRI Study: Multi-Site Harmonization for Brain Disorders Throughout the Lifespan

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (FY2018 [~] FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. The Harmonization protocol (HARP) was established for five high-quality 3T scanners to obtain multimodal brain images including T1 and T2-weighted, resting state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, providing high reproducibility and sensitivity to subject-specific connectome. A total of 75 TS, as well as patients with various psychiatric and neurological disorders, are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a rigorous, prospective harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

neuroscience↗