Biology subjects

Nakaoka, S.

Publications and source records attributed to Nakaoka, S..

9 recordsLinked to original sources

bioRxiv2025

Machine Learning-based Identification and Characterization of High-risk Carriers of HTLV-1-Associated Myelopathy (HAM)

HTLV-1-associated myelopathy (HAM) develops in a part of HTLV-1-infected individuals while most of the individuals remain asymptomatic. This complicates the identification of HTLV-1 carriers at elevated risk. In this study, we integrated HTLV-1 proviral load and antibody titers against Tax, Env, Gag p15, p19, and p24 proteins in a machine learning (ML) framework to identify and characterize high-risk individuals likely to develop HAM. We stratified asymptomatic carrier samples employing an anomaly detection model. We further developed and validated classifier models capable of distinguishing three clinical subgroups, carrier, ATL, and HAM for assessing the anomaly carrier samples as unseen test data. With most anomaly carrier samples ([~]76.47%) predicted as HAM, further statistical and interpretative analysis revealed the HAM-like characteristics of the anomaly carrier samples indicating elevated risk. Additionally, significant heterogeneity in immune response was observed among other asymptomatic carriers. Our machine learning-based approach offers a novel and insightful tool for identifying and evaluating high-risk characteristics for HAM, providing a holistic view of the complex immune dynamics of asymptomatic carriers of HTLV- 1.

systems biology↗

bioRxiv2024

Invasive mammals disrupt native dung beetle community coexistence

Biological invasions are among the major drivers of biodiversity and are increasing worldwide. Among the invasive species, mammals have a particularly profound impact on native ecosystems. As primary decomposers of mammalian feces, dung beetles are critical in ecosystem functioning, and their community structure is closely linked to their services. However, the introduction of invasive mammals threatens these beetles and potentially disrupts their ecosystem services. Therefore, it is necessary to understand the resulting changes in communities. We developed a novel population dynamics model focusing on the interactions among mammals, feces, and dung beetles. Our results indicate that such invasions increase the risk of extinction of specialist dung beetles that cannot utilize the feces of invasive mammals. The risk of extinction is particularly high when generalist dung beetles show a preference for native feces, leading to intensified interspecific competition for resources. Additionally, the extinction risk of specialist dung beetles increases when invasive mammals display irruptive population dynamics. In conclusion, our findings demonstrated that non-native mammalian invasions disrupt the coexistence of native dung beetle communities, potentially leading to losses in biodiversity and ecosystem function. These risks should be considered in future empirical studies to evaluate the impact of invasive mammals on dung beetle communities.

ecology↗

bioRxiv2024

Male Dimorphism and Emergence Strategies: A Mathematical Model of Protandry in Fabriciana nerippe Felder, 1862

In many butterfly species, males emerge earlier than females as part of a strategy to maximize male reproductive success. Although behavioral ecological studies using mathematical models have been conducted to explain this phenomenon, certain emergence patterns remain unexplained. In the butterfly species Fabriciana nerippe, some males emerge at the same time as females, in addition to males that emerge earlier than the females. However, it is unclear what emergence patterns occur in populations with male dimorphism, as observed in this species. In this study, we showed the existence of male body size dimorphism in Fabriciana nerippe by conducting a comparative analysis of forewing lengths between males and females. In addition, we developed a comprehensive mathematical model to investigate emergence patterns in the presence of dimorphic males. By introducing a trade-off between large size and early emergence, the model considered a scenario where small early-emerging and large late-emerging males could coexist. Numerical analysis demonstrated the emergence patterns of these two male types with a switch in emergence time. Furthermore, the higher the death rate before emergence, the earlier the emergence switch. These findings suggested that the timing of the switch depends on the death rate and is influenced by environmental factors. This work contributes to ecological and theoretical studies on timing dimorphism in life-history strategies across a broader range of species. HighlightsO_LIWe confirmed the existence of male body-size dimorphism corresponding to emergence timing in Fabriciana nerippe. C_LIO_LIWe developed a comprehensive model of male emergence patterns incorporateing male dimorphism. C_LIO_LIWe assumed a trade-off between early emergence and large body size. C_LIO_LIThe timing of male emergence switching is influenced by death rate and reproductive advantage of large males. C_LI

ecology↗

bioRxiv2023

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation for analyzing extracellular viral markers

Chronic infection of hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. The quantifying and understanding dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, it requires a liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because of the ethical aspect. We here aimed to develop a non-invasive method for quantifying cccDNA in the liver using surrogate markers present in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations (PDEs), integrates experimental data from in vitro and in vivo investigations. By applying this model, we successfully predicted the amount and dynamics of intrahepatic cccDNA using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The non-invasive quantification of cccDNA using our proposed methodology holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

microbiology↗

bioRxiv2023

A novel Energy Landscape construction method incorporating the Latent Dirichlet Allocation topic model and the pairwise Maximum Entropy model, revealing the significant contribution of Bactericides to the development of Inflammatory Bowel Disease

1The dysbiosis of microbiota has been reported to be associated with numerous human pathophysiological processes, including Inflammatory Bowel Disease (IBD). With advancements in highthroughput sequencing, various methods have been developed to study the alteration of microbiota in the development and progression of diseases. However, a suitable approach to assess the global stability of the microbiota in disease states through time-series microbiome data is yet to be established. In this study, we introduce a novel Energy Landscape construction method, which incorporates the Latent Dirichlet Allocation (LDA) model and the pairwise Maximum Entropy (MaxEnt) model, and demonstrate its utility by applying it to an IBD time-series dataset. Through this method, we obtained the "energy" profile for the potential patterns of microbiota to occur under disease, indicating their stability and prevalence. The results suggest the potential contribution of several microbial genera, including Bacteroides, Alistipes, and Faecalibacterium, as well as their interactions, to the development of IBD. Our proposed method provides a novel and insightful tool for understanding the alteration and stability of the microbiota under disease states and offers a more holistic view of its complex dynamics at play in microbiota-mediated disease.

bioinformatics↗

bioRxiv2021

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↗

bioRxiv2021

Extended applicability of causal inference to compositional data by reciprocal logarithmic ratio transformation

Understanding state transition caused by several intrinsic and extrinsic factors such as environmental change and increased stress of human activities has been a significant focus in ecological studies. Analysis of time-series data is indispensable to identify a causal relationship between the possible factors and community change. Among several time-series analysis, a nonlinear time series analysis method called empirical dynamic modeling (EDM) has been recently applied to infer causality of community change that arose from intraspecies interactions. EDM allows model-free analysis to estimate the degree of action strength of intraspecific and interspecific interactions at the population level. Convergent cross-mapping (CCM) is an empirical dynamic analysis method used to suggest the existence of causality by reconstructing the state space of a dynamical system from a time series of observations without assuming any explicit mathematical equations. Although CCM allows for inferring directional interpretation of causal relationships from multivariate time series data, one of the major challenges is its non-applicability to compositional data, a common representation of next generation sequencing data such as microbiome. This study aimed to explore a practical approach applied explicitly to compositional data analysis. More specifically, we propose a heuristic but useful transformation that enables CCM to be applied to compositional data. The proposed transformation has demonstrated its applicability to compositional data equivalent to the conventional CCM to untransformed data. Application of the proposed transformation to sequence-based microbial community profiling data provides reasonable implication to the possible causal relationship during state transition. Author summarySeveral types of ecological studies such as trophic changes in lakes, marine plankton communities, forest ecosystems, terrestrial ecosystems, and interactions between plants and soils have employed time-series analyses for identifying the factors that might cause state changes. With the rise of next-generation sequencing, our understanding of these ecosystems is expanding to analyze sequence data. A nonlinear time series analysis method, termed empirical dynamic modeling, has been recently applied for analyzing time-series data. Among different empirical dynamic modeling methods, convergent cross-mapping (CCM) is frequently used to infer a causal relationship. Although CCM enables the directional interpretation of causal relationships, it cannot be applied to compositional data analysis. This study proposes a novel type of transformation, Reciprocal Logarithmic Ratio (RLR) transformation, that enables CCM to be applied to compositional data. With RLR-transformation, CCM results for compositional data are comparable to those for absolute data, and it is confirmed that the transformation is applicable to sequence data as well. The RLR-transformation is expected to provide a better understanding of ecological interactions by estimating causal relationships in compositional data.

ecology↗

bioRxiv2019

Two strategies underlying the trade-off of hepatitis C virus proliferation: stay-at-home or leaving-home?

Viruses proliferate through both genome replication inside infected cells and transmission to new target cells or to new hosts. Each viral genome molecule in infected cells is used either for amplifying the intracellular genome as a template (\"stay-at-home strategy\") or for packaging into progeny virions to be released extracellularly (\"leaving-home strategy\"). The balance between these strategies is important for both initial growth and transmission of viruses. In this study, we used hepatitis C virus (HCV) as a model system to study the functions of viral genomic RNA in both RNA replication in cells and in progeny virus assembly and release. Using viral infection assays combined with mathematical modelling, we characterized the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral assembly and release characteristics. We found that 1.27% and 3.28% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial growth rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leaving-home strategy provides a higher level of initial transmission for Jc1-n, while, in contrast, the stay-at-home strategy provides a higher initial growth rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses. Ours is the first study to analyze stay-leave trade-offs during the viral life cycle and their significance for viral proliferation.

evolutionary biology↗

bioRxiv2019

Energy landscape analysis of ecological communities elucidates the phase space of community assembly dynamics

Compositional multistability is widely observed in multispecies ecological communities. Since differences in community composition often lead to differences in community function, understanding compositional multistability is essential to comprehend the role of biodiversity in maintaining ecosystems. In community assembly studies, it has long been recognized that the order and timing of species migration and extinction influence structure and function of communities. The study of multistability in ecology has focused on the change in dynamical stability across environmental gradients, and was developed mainly for low-dimensional systems. As a result, methodologies for studying the compositional stability of empirical multispecies communities are not well developed. Here, we show that models previously used in ecology can be analyzed from a new perspective - the energy landscape - to unveil compositional stability in observational data. To show that our method can be applicable to real-world ecological communities, we simulated assembly dynamics driven by population level processes, and show that results were mostly robust to different simulation assumptions. Our method reliably captured the change in the overall compositional stability of multispecies communities over environmental change, and indicated a small fraction of community compositions that may be channels for transitions between stable states. When applied to murine gut microbiota, our method showed the presence of two alternative states whose relationship changes with age, and suggested mechanisms by which aging affects the compositional stability of the murine gut microbiota. Our method provides a practical tool to study the compositional stability of communities in a changing world, and will facilitate empirical studies that integrate the concept of multistability from different fields.

ecology↗