Biology subjects

Masuda, N.

Publications and source records attributed to Masuda, N..

8 recordsLinked to original sources

bioRxiv2024

Evolvability through segmental duplication follows species-specific dynamics

Segmental duplications are major drivers of evolutionary innovation, yet their dynamics across vertebrates remain poorly understood. Here, we identify segmental duplications from long-read sequenced genomes of 117 vertebrates and the starfish, generating the largest multi-species dataset of its kind. We find that vertebrate genomes show a higher propensity for tandem duplications than for interspersed duplications. However, when focusing only on subtelomeric regions, avian and mammalian genomes show the opposite propensity toward interspersed duplications. We also observe that, across vertebrates, tandem duplications tend to be larger than interspersed duplications. Next, we construct a segmental duplication network for each species, and use network-derived properties to quantify the duplication landscape for that species. Functional enrichment analysis of hyper-duplicated genes reveals a strong enrichment in platypus for pheromone response, driven by the expansion of the vomeronasal pheromone receptor V1R gene family. Overall, our results uncover the general properties of vertebrate segmental duplication, demonstrate the rapid evolution of segmental duplication landscapes, and highlight the utility of network-based approaches for studying genome evolution. SignificanceGene and regulatory region duplications are a fundamental source of evolutionary raw material. Here we generate segmental duplication calls from 117 vertebrate species. We find that vertebrate genomes have a bias towards tandem duplications relative to interspersed duplications. However, in the subtelomeric regions, birds and mammals exhibit an opposite bias toward interspersed duplications. Our analysis of segmental duplication networks demonstrate that duplication landscapes evolve rapidly, following species-specific rather than phylogenetic patterns. These findings indicate that the genomic architecture underlying segmental duplications is highly dynamic, uniquely shaping each lineages potential to adapt. Our study provides the most comprehensive view of vertebrate segmental duplications to date and establishes a network-based framework for studying genomic structural evolution.

evolutionary biology↗

bioRxiv2024

Switch-like Gene Expression Modulates Disease Susceptibility

A fundamental challenge in biomedicine is understanding the mechanisms predisposing individuals to disease. While previous research has suggested that switch-like gene expression is crucial in driving biological variation and disease susceptibility, a systematic analysis across multiple tissues is still lacking. By analyzing transcriptomes from 943 individuals across 27 tissues, we identified 1,013 switch-like genes. We found that only 31 (3.1%) of these genes exhibit switch-like behavior across all tissues. These universally switch-like genes appear to be genetically driven, with large exonic genomic structural variants explaining five ([~]18%) of them. The remaining switch-like genes exhibit tissue-specific expression patterns. Notably, tissue-specific switch-like genes tend to be switched on or off in unison within individuals, likely under the influence of tissue-specific master regulators, including hormonal signals. Among our most significant findings, we identified hundreds of concordantly switched-off genes in the stomach and vagina that are linked to gastric cancer (41-fold, p<10-4) and vaginal atrophy (44-fold, p<10-4), respectively. Experimental analysis of vaginal tissues revealed that low systemic levels of estrogen lead to a significant reduction in both the epithelial thickness and the expression of the switch-like gene ALOX12. We propose a model wherein the switching off of driver genes in basal and parabasal epithelium suppresses cell proliferation therein, leading to epithelial thinning and, therefore, vaginal atrophy. Our findings underscore the significant biomedical implications of switch-like gene expression and lay the groundwork for potential diagnostic and therapeutic applications.

bioinformatics↗

bioRxiv2021

Network-based analysis of allele frequency distribution among multiple populations identifies adaptive genomic structural variants

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a network-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 577 structural variants that show high population distribution. We further showed that 39 and 20 of these putatively adaptive structural variants overlap with coding sequences or are significantly associated with GWAS traits, respectively. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to (i) population differentiation of rapidly evolving multi-allelic variants, (ii) incomplete sweeps, and (iii) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.

genomics↗

bioRxiv2019

Division of labor promotes the spread of information in colony emigrations by the ant Temnothorax rugatulus

The fitness of group-living animals often depends on how well members share information needed for collective decision-making. Theoretical studies have shown that collective choices can emerge in a homogeneous group of individuals following identical rules, but real animals show much evidence for heterogeneity in the degree and nature of their contribution to group decisions. In social insects, for example, the transmission and processing of information is influenced by a well-organized division of labour. Studies that accurately quantify how this behavioural heterogeneity affects the spread of information among group members are still lacking. In this paper, we look at nest choices during colony emigrations of the ant Temnothorax rugatulus and quantify the degree of behavioural heterogeneity of workers. Using methods from both machine learning and network analysis, we identify and characterize four behavioural castes of workers - primary, secondary, passive, and wandering - covering distinct roles in the spread of information during each emigration. This detailed characterization of the contribution of each worker can improve models of collective decision-making in this species and promises a deeper understanding of behavioural variation at the colony level.

animal behavior and cognition↗

bioRxiv2019

Frontoparietal network dynamics impairments in juvenile myoclonic epilepsy revealed by MEG energy landscape

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy affecting brain activity. It is unclear to what extent JME leads to abnormal network dynamics across functional networks. Here, we proposed a method to characterise network dynamics in MEG resting-state data, combining a pairwise maximum entropy model (pMEM) and the associated energy landscape analysis. Fifty-two JME patients and healthy controls underwent a resting-state MEG recording session. We fitted the pMEM to the oscillatory power envelopes in theta (4-7 Hz), alpha (8-13 Hz), beta (15-25 Hz) and gamma (30-60 Hz) bands in three source-localised resting-state networks: the frontoparietal network (FPN), the default mode network (DMN), and the sensorimotor network (SMN). The pMEM provided an accurate fit to the MEG oscillatory activity in both patient and control groups, and allowed estimation of the occurrence probability of each network state, with its regional activity and pairwise regional co-activation constrained by empirical data. We used energy values derived from the pMEM to depict an energy landscape of each network, with a higher energy state corresponding to a lower occurrence probability. When comparing the energy landscapes between groups, JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, numerical simulation of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of characteristic energy minima was shortened in JME patients. These network alterations were confirmed by a significant leave-one-out classification of individual participants based on a support vector machine employing the energy values of pMEM as features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices.\n\nHighlightsO_LIAn energy landscape analysis characterises the dynamics of MEG oscillatory activity\nC_LIO_LIPatients with JME exhibit fewer local minima of the energy in their energy landscapes\nC_LIO_LIJME affects the network dynamics in the frontoparietal network.\nC_LIO_LIEnergy landscape measures allow good single-patient classification.\nC_LI

neuroscience↗

bioRxiv2019

Critical brain dynamics and human intelligence

According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.

neuroscience↗

bioRxiv2019

Mindfulness meditation changes the boundary and configuration of the primary functional networks in the brain

Research has shown that meditation not only improves our cognitive and motivational functioning (e.g., attention, mental health), it influences the way how our brain networks [e.g., default mode network (DMN), fronto-parietal network (FPN), and sensory-motor network (SMN)] function and operate. However, surprisingly little attention has been paid to the possibility that meditation alters the structure (composition) of these functional brain networks. Here, using a single-case experimental design with longitudinal intensive data, we examined the effect of mediation practice on intra-individual changes in the composition of whole-brain networks. The results showed that meditation (1) changed the community size (with a number of regions in the FPN being merged into the DMN after meditation), (2) changed the brain regions composing the SMN community without changing its size, and (3) led to instability in the community allegiance of the regions in the FPN. These results suggest that, in addition to altering specific functional connectivity, meditation leads to reconfiguration of whole-brain network structure. The reconfiguration of community structure in the brain provides fruitful information about the neural mechanisms of meditation.

neuroscience↗

bioRxiv2019

Immune state networks of wild and laboratory mice

The mammalian immune system protects individuals from infection and disease. It is a complex system of interacting cells and molecules and extensive work, principally with laboratory mice, has investigated its function. Wild and laboratory animals lead very different lives, and this is reflected in there being substantial immunological differences between them. Here we use network analyses to study a unique data set of 120 immune measures of wild and laboratory mice, where immune measures define nodes and correlations of immune measures across individual mice define edges between immune measures. To date, there has only been very limited network analyses of the immune system, which is surprising because such analyses may be important to better understand its organisation and functionality. We found that the immunological networks of wild and laboratory mice were similar in some aspects of their mesoscale structure, particularly concerning cytokine response communities. However, we also identified notable differences in node membership of network communities between the wild and laboratory networks, pointing to how the same immune system acts and interacts differently in wild and in laboratory mice. These results show the utility of network analysis in understanding immune responses and also the importance of studying wild animals in additional to laboratory animals.\n\nAuthor summaryThe mammalian immune system is a complex system that protects individuals from infection and disease. Most of our understanding of the immune system comes from studies of laboratory animals, particularly mice. However, wild and laboratory animals lead very different lives, potentially leading to substantial immunological differences between them and so possibly limiting the utility of laboratory animals as informative model systems. As a complex interacting set of cells and molecules, the immune system is a biological network. Therefore, we used network analyses to study the immune system, specifically a unique data set of immune measures of wild and laboratory mice, where 120 different immune measures define nodes of the network. We found that the networks of wild and laboratory mice were similar in some aspects of their grouping structure, particularly concerning communities of nodes of cytokine responses. However, we also identified notable differences in node membership of communities between the wild and laboratory networks, pointing to how the same immune system behaves differently in wild and in laboratory mice. These results show the utility of network analysis in understanding immune responses and also the importance of studying wild animals in addition to laboratory animals.

systems biology↗