Biology subjects

Kanaya, S.

Publications and source records attributed to Kanaya, S..

5 recordsLinked to original sources

bioRxiv2024

Soil nutrition-dependent dynamics of the root-associated microbiome in paddy rice

O_LIPlants accommodate diverse microbial communities (microbiomes), which can change dynamically during plant adaptation to varying environmental conditions. However, the direction of these changes and the underlying mechanisms driving them, particularly in crops adapting to the field conditions, remain poorly understood. C_LIO_LIWe investigate the root-associated microbiome of rice (Oryza sativa L.) using 16S rRNA gene amplicon and metagenome sequencing, across four consecutive cultivation seasons in a high-yield, non-fertilized, and pesticide-free paddy field, compared to a neighboring fertilized and pesticide-treated field. C_LIO_LIOur findings reveal that root microbial community shifts and diverges based on soil fertilization status and plant developmental stages. Notably, nitrogen-fixing bacteria such as Telmatospirillum, Bradyrhizobium and Rhizomicrobium were over-represented in rice grown in the non-fertilized field, implying that the assembly of these microbes supports rice adaptation to nutrient-deficient environments. C_LIO_LIA machine learning model trained on the microbiome data successfully predicted soil fertilization status, highlighting the potential of root microbiome analysis in forecasting soil nutrition levels. Additionally, we observed significant changes in the root microbiome of ccamk mutants, which lack a master regulator of mycorrhizal symbiosis, under laboratory conditions but not in the field, suggesting a condition-dependent role for CCaMK in establishing microbiomes in paddy rice. C_LI

plant biology↗

bioRxiv2021

Integrated Metabolomics to Reveal the Impacts of Common Antibiotics based on Drug Resistance Prediction of Gut Microbiota

Due to the close interaction between the host and the gut microbiota, the alterations in gut microbiota metabolism may therefore contribute to various diseases. How to use antibiotics more wisely in clinical practice is a promising task in the field of pathophysiology related to gut microbiota. The hope fueling this research is that the alteration of gut microbial communities are paralleled by their capacity on metabolomic from the combined perspective of microbiome and metabolomics. In order to reveal the impacts of antibiotics on microbiota-associated host metabolomic phenotypes, a feasible methodology should be well developed to assess the pervasive effects of antibiotics on the population structure of gut microbial communities. Our attempt starts from predicting specific resistance phenotypes of the individuals in isolation from the rest of the gut microbiota community, according to their resistant genotypes. Once resistance phenotypes of microbiome is determined, we integrated metabolomics with machine learning by applying various analysis algorithms to explore the relationship between the predicted resistance and metabolites, including what the microbial community is after medication, which microbes produce metabolites, and how these metabolites enrich.

bioinformatics↗

bioRxiv2020

Multidrug treatment with nelfinavir and cepharanthine against COVID-19

Antiviral treatments targeting the emerging coronavirus disease 2019 (COVID-19) are urgently required. We screened a panel of already-approved drugs in a cell culture model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and identified two new antiviral agents: the HIV protease inhibitor Nelfinavir and the anti-inflammatory drug Cepharanthine. In silico modeling shows Nelfinavir binds the SARS-CoV-2 main protease consistent with its inhibition of viral replication, whilst Cepharanthine inhibits viral attachment and entry into cells. Consistent with their different modes of action, in vitro assays highlight a synergistic effect of this combined treatment to limit SARS-CoV-2 proliferation. Mathematical modeling in vitro antiviral activity coupled with the known pharmacokinetics for these drugs predicts that Nelfinavir will facilitate viral clearance. Combining Nelfinavir/Cepharanthine enhanced their predicted efficacy to control viral proliferation, to ameliorate both the progression of disease and risk of transmission. In summary, this study identifies a new multidrug combination treatment for COVID-19.

microbiology↗

bioRxiv2019

The Feasibility of Predicting Impending Malignant Ventricular Arrhythmias on the basis of Signal Complexity of Heartbeat Intervals

Malignant ventricular arrhythmias (MAs), such as ventricular tachycardia (VT) that presages cardiac arrest, present the highest hurdle for the healthcare community to overcome. Given that MAs occur unpredictably and lead to emergencies, convenient tracking devices, e.g. photoplethysmogram (PPG), that could predict MAs would be irreplaceably valuable. Since the use of heartbeat intervals (HbI) to predict the occurrence of arrhythmias is becoming more feasible, a further attempt to establish a new convenient approach for predicting impending MAs with HbI is worth trying. Assuming that intrinsic characteristics of MAs (VT and ventricular fibrillation: VF) can be revealed by a suitable approach on the basis of signal complexity, we propose an approach that first expresses the physiological status of the heart by HbI; then delineates the patterns of HbI by a new complexity metric (refined composite multi-scale entropy: RCMsEn); and finally trains a nonlinear machine learning model (random forest: RF) to learn the specific patterns of MAs so as to differentiate them from the normal sinus heart rhythm(N) and other prevalent arrhythmias (atrial fibrillation: AF, and premature ventricular contraction: V). For calculating entropy values and predicting MAs as early as possible (which is the aim of this study), two specifications are of interest: the minimal length of HbI needed to delineate the MAs patterns sufficiently (lenmin), and the maximum time length at which our model can predict impending MAs (timemax). We compared the RF model with support vector machine (SVM) models based on linear and Gaussian kernels. Results show that the RF model performs the best, reaching a 99.24% recall and a 99.87% precision for a HbI of 500 heartbeats (the lenmin) 374 seconds (the timemax) preceding the occurrence of MAs. The HbI samples in this study were extracted from an electrocardiograph (ECG). However, given the subtle difference (0.1 ms typically) between the R-R interval of ECG and the P-P interval of PPG, this approach could be extended to HbI acquired by the PPG sensor and thus should be of substantial theoretical and practical significance in cardiac arrest prevention.

bioinformatics↗

bioRxiv2019

The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals

It is well known that the cardiac system is controlled by the complex nonlinear self-regulation and the heartbeat variability (HRV) is an independent indicator of the autonomic regulation. With the assumption that intrinsic differences of atrial fibrillation (A) and ventricular ectopic arrhythmias (V) can be unveiled by a proper approach based on of signal complexity, we examine the feasibility of detecting these arrhythmias of different pathological origins using metrics of complexity for heartbeat intervals (HRI). Specifically, the normal sinus rhythm (N), the A type and the V type are used as the targeted types of heartbeat. By extracting the entropy-based features from HRI of different lengths, i.e., from 300 heartbeats to 1000 heartbeats, we examined the distinguishability of these 3 types of heartbeat. By applying the features to the random forest model, the HRI signal of 600-heartbeat-length can be used to detect the A and V completely, i.e., with 100% of type-wise recall and precision. What is more, this approach is sensitive to the existence of the corresponding arrhythmias. The results substantiate our assumption about the intrinsic difference of the A and V type. A further investigation applying this approach to a wider spectrum and a finer stratification of arrhythmias/ cardiac diseases and may lead to the systematic understanding in the context of complexity and better insight for its practical use for wearable/unconstrained monitors.

physiology↗