Tubulysin Production by the Dead Cells of Archangium gephyra KYC5002.

Archangium gephyra KYC5002 produces tubulysins during the death phase. In this study, we aimed to determine whether dead cells produce tubulysins. Cells were cultured for three days until the verge of the death phase, disrupted via ultrasonication, incubated for 2 h, and examined for tubulysin production. Non-disrupted cells produced 0.14 mg/L of tubulysin A and 0.11 mg/L of tubulysin B. Notably, tubulysin A production was increased by 4.4-fold to 0.62 mg/L and that of tubulysin B was increased by 6.7-fold to 0.74 mg/L in the disrupted cells. The same increase in tubulysin production was observed when the cells were killed by adding hydrogen peroxide. However, when the enzymes were inactivated via heat treatment of the cultures at 65 °C for 30 min, no significant increase in tubulysin production due to cell death was observed. Reverse transcription-quantitative polymerase chain reaction analysis of tubB mRNA revealed that the expression levels of tubulysin biosynthetic enzyme genes increased during the death phase compared to those during the vegetative growth phase. Our findings suggest that A. gephyra produces biosynthetic enzymes and subsequently uses them for tubulysin production in the cell death phase or during cell lysis by predators.

Tubulysins are Essential for the Preying of Ciliates by Myxobacteria.

Tubulysins are bioactive secondary metabolites produced by myxobacteria that promote microtubule disassembly. Microtubules are required for protozoa such as Tetrahymena to form cilia and flagella. To study the role of tubulysins in myxobacteria, we co-cultured myxobacteria and Tetrahymena. When 4000 Tetrahymena thermophila and 5.0 × 108 myxobacteria were added to 1 ml of CYSE medium and co-cultured for 48 h, the population of T. thermophila increased to more than 75,000. However, co-culturing tubulysin-producing myxobacteria, including Archangium gephyra KYC5002, with T. thermophila caused the population of T. thermophila to decrease from 4000 to less than 83 within 48 h. Almost no dead bodies of T. thermophila were observed in the culture medium. Co-culturing of T. thermophila and the A. gephyra KYC5002 strain with inactivation of the tubulysin biosynthesis gene led to the population of T. thermophila increasing to 46,667. These results show that in nature, most myxobacteria are preyed upon by T. thermophila, but some myxobacteria prey on and kill T. thermophila using tubulysins. Adding purified tubulysin A to T. thermophila changed the cell shape from ovoid to spherical and caused cell surface cilia to disappear.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 µm (PM10) and <2.5 µm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia.

In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO2 and O3 from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)-were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO2 and O3 concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO2 model produced R2 of 0.63-0.70 and normalized root-mean-square-error (nRMSE) of 38.3-42.2% and the O3 model resulted in R2 of 0.65-0.78 and nRMSE of 19.6-24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3-2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO2 vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO2 and O3 models.

Stability of Skin Microbiome at Sacral Regions of Healthy Young Adults, Ambulatory Older Adults, and Bedridden Older Patients After 2 Years.

OBJECTIVE: The sacral skin of bedridden older patients often develops a dysbiotic condition. To clarify whether the condition changes or is sustained over time, we analyzed the skin microbiome and the skin physiological functions of the sacral skin in patients who completed our 2017 study. METHODS: In 2019, we collected the microbiome on the sacral region and measured sacral skin hydration, pH, and transepidermal water loss from 7 healthy young adults, 10 ambulatory older adults, and 8 bedridden older patients, all of whom had been recruited for the 2017 study. For microbiome analysis, 16S ribosomal RNA-based metagenomic analysis was used. RESULTS: No significant differences in the microbial compositions or any alpha diversity metrics were found in the bedridden older patients between the 2017 and 2019 studies; the higher gut-related bacteria were still observed on the sacral skin of the bedridden older patients even after 2 years. Only skin pH showed a significant decrease, approaching normal skin condition, in the bedridden older patients over 2 years. CONCLUSION: This study indicated that gut-related bacteria stably resided in the sacral skin in bedridden patients, even if the patient had tried to restore skin physiological functions using daily skin care. We propose the importance of skin care that focuses more on bacterial decontamination for the sacral region of bedridden older patients, in order to decrease the chances of skin/wound infection and inflammation.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models.

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM10 and PM2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM10 and PM2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R2 of 0.74 and prediction R2 of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R2 of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM10 and PM2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.