Biology subjects

Yoshioka, T.

Publications and source records attributed to Yoshioka, T..

6 recordsLinked to original sources

bioRxiv2026

Multifaceted and evolutionarily dynamic interactions between Caenorhabditis elegans SPO-11 and its cofactors ensure proper formation of meiotic DNA double-strand breaks

DNA double-strand breaks (DSBs) generated during meiotic prophase by the topoisomerase-like protein SPO11 are essential to create crossovers between homologous chromosomes. Since crossovers are required to biorient chromosomes at the first meiotic division, DSB formation is essential for meiosis in most sexually-reproducing organisms. Since excess DSBs have the potential to destabilize the genome, SPO-11 activity must be strictly regulated by many cofactors. Recent studies have established that SPO11 must dimerize to cut DNA, whereas soluble SPO11 and SPO11-TOPOVIBL complexes are predominantly monomeric (1-3). This contrast suggested that a major role of SPO11 cofactors could be to promote SPO11 dimerization, through means such as increasing local concentration or co-orienting SPO11 protomers. However, the mechanism of this regulation is not well-understood. Here, by taking advantage of phylogenomic analysis in the nematode genus Caenorhabditis, we show that the conserved cofactor DSB-1Rec114 evolved to replace TOPOVIBL function in C. elegans. We provide genetic and biochemical evidence that multiple interactions between SPO-11 and DSB-1 stabilize protein complex formation and promote SPO-11 dimerization. Our results shed light on the regulatory mechanism of programmed DSB formation, which ensures crossover formation and meiotic chromosome segregation while protecting genomic stability. Significance StatementProgrammed DNA double-strand breaks catalyzed by SPO11 are essential for meiosis, but how SPO11 and its cofactors cooperate to cut DNA is not understood. SPO11 only cuts DNA as a homodimer, but soluble SPO11, with or without its core component TOPOVIBL, is predominantly monomeric. We show here that DSB-1, a conserved cofactor of C. elegans SPO-11, has evolved to replace TOPOVIBL to make direct, multifaceted interactions with SPO-11. We provide evidence that DSB-1 simultaneously binds both SPO-11 protomers, and this binding is critical for DNA cleavage, implying a major role of DSB-1 in promoting SPO-11 dimerization. Our phylogenetic analysis also highlights the evolutionary flexibility of a conserved, essential protein complex after the loss of one of its members, TOPOVIBL.

cell biology↗

bioRxiv2024

Inter-individual variability in motor learning due to differences in effective learning rates between generalist and specialist memory stores

Humans exhibit large interindividual differences in motor learning ability. However, most previous studies have examined properties common across populations, with less emphasis on interindividual differences. We hypothesized here, based on our previous experimental and computational motor adaptation studies, that individual differences in effective learning rates between a generalist memory module that assumes environmental continuity and specialist modules that are responsive to trial-by-trial environmental changes could explain both large population-wise and individual-wise differences in dual tasks adaptation under block and random schedules. Participants adapted to two opposing force fields, either sequentially with alternating training blocks or simultaneously with random sequences. As previously reported, in the block training schedule, all participants adapted to the force field presented in a block but showed large interference in the subsequent opposing force field blocks, such that adapting to the two force fields was impossible. In contrast, in the random training schedule, participants could adapt to the two conflicting tasks simultaneously as a group; however, large interindividual variability was observed. A modified MOSAIC computational model of motor learning equipped with one generalist module and two specialist modules explained the observed behavior and variability for wide parameter ranges: when the predictions errors were large and consistent as in block schedules, the generalist module was selected to adapt quickly. In contrast, the specialist modules were selected when they more accurately predicted the changing environment than the generalist, as during random schedules; this resulted in consolidated memory specialized to each environment, but only when the ratio of learning rates of the generalist to specialists was relatively small. This dynamic selection process plays a crucial role in explaining the individual differences observed in motor learning abilities.

neuroscience↗

bioRxiv2024

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↗

bioRxiv2024

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↗

bioRxiv2021

Leaders are made: Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions.

Are leaders made or born? Leader-follower roles have been well characterized in social science, but they remain somewhat obscure in sensory-motor coordination. Furthermore, it is unknown how and why leader-follower relationships are acquired, including innate versus acquired controversies. We developed a novel asymmetrical coordination task in which two participants (dyad) need to collaborate in transporting a simulated beam while maintaining its horizontal attitude. This experimental paradigm was implemented by twin robotic manipulanda, simulated beam dynamics, haptic interactions, and a projection screen. Clear leader-follower relationships were learned despite participants not being informed that they were interacting with each other, but only when strong haptic feedback was introduced. For the first time, we demonstrate the emergence of consistent leader-follower relationships in sensory-motor coordination, and further show that haptic interaction is essential for dyadic co-adaptation. These results provide insights into neural mechanisms responsible for the formation of leader-follower relationships in our society.

neuroscience↗

bioRxiv2020

Hierarchical motor adaptations negotiate failures during force field learning

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of internal model adaptation. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.

neuroscience↗