RBMS1 Coordinates with the m6A Reader YTHDF1 to Promote NSCLC Metastasis through Stimulating S100P Translation.

Metastasis is the leading cause for the high mortality of lung cancer, however, effective anti-metastatic drugs are still limited. Here it is reported that the RNA-binding protein RBMS1 is positively associated with increased lymph node metastasis in non-small cell lung cancer (NSCLC). Depletion of RBMS1 suppresses cancer cell migration and invasion in vitro and inhibits cancer cell metastasis in vivo. Mechanistically, RBMS1 interacts with YTHDF1 to promote the translation of S100P, thereby accelerating NSCLC cell metastasis. The RRM2 motif of RBMS1 and the YTH domain of YTHDF1 are required for the binding of RBMS1 and YTHDF1. RBMS1 ablation inhibits the translation of S100P and suppresses tumor metastasis. Targeting RBMS1 with NTP, a small molecular chemical inhibitor of RBMS1, attenuates tumor metastasis in a mouse lung metastasis model. Correlation studies in lung cancer patients further validate the clinical relevance of the findings. Collectively, the study provides insight into the molecular mechanism by which RBMS1 promotes NSCLC metastasis and offers a therapeutic strategy for metastatic NSCLC.

Revealing the spatiotemporal patterns of water vapor and its link to North Atlantic Oscillation over Greenland using GPS and ERA5 data.

Precipitation plays an important role in the interannual mass variations of Greenland Ice Sheet (GrIS) and is highly influenced by atmospheric circulation change. The relationship between precipitation and North Atlantic Oscillation (NAO) has been revealed by many studies, but the role of water vapor transportation in the NAO-precipitation relationship was rarely investigated. Therefore, to fill the knowledge gap of how water vapor changes and responds to NAO in space and time, we applied Multichannel Singular Spectral Analysis (MSSA) to the Global Positioning System (GPS) and the fifth-generation reanalysis dataset of the European Center for Medium-Range Weather Forecasting (ERA5) Precipitable Water Vapor (PWV) data to extract the interannual PWV signals in Greenland. Results show that the interannual PWV signals overall increased in 2008-2011, decreased in 2011-2015, and increased in 2015-2021. The amplitudes of the interannual signals derived from both the GPS PWV and ERA5 basin-averaged PWV exhibited an overall southwest-northeast decreasing gradient. We also found anticorrelation between the interannual PWV signals and the NAO signal over Greenland but the correlation coefficients are not statistically significant, and the correlation coefficients in most cases were less than -0.65, indicating that positive (negative) NAO phase decreased (increased) the water vapor content. The Fast Fourier Transform (FFT) results illustrated that the interannual signals derived from both the GPS site-dependent and the ERA5 basin-averaged PWV had similar dominant frequencies to that of the NAO signal, reinforcing their correlations. This study reveals the spatiotemporal pattern of the interannual water vapor and its linkage to the NAO, providing a new perspective for understanding the climate change on Greenland.

Feedback Modulation between Human INO80 Chromatin Remodeling Complex and miR-372 in HCT116 Cells.

Human INO80 chromatin remodeling complex (INO80 complex) as a transcription cofactor is widely involved in gene transcription regulation and is frequently highly expressed in tumor cells. However, few reports exist on the mutual regulatory mechanism between INO80 complex and non-coding microRNAs. Herein, we showed evidence that the INO80 complex transcriptionally controls microRNA-372 (miR-372) expression through RNA-Seq analysis and a series of biological experiments. Knocking down multiple subunits in the INO80 complex, including the INO80 catalytic subunit, YY1, Ies2, and Arp8, can significantly increase the expression level of miR-372. Interestingly, mimicking miR-372 expression in HCT116 cells, in turn, post-transcriptionally suppressed INO80 and Arp8 expression at both mRNA and protein levels, indicating the existence of a mutual regulatory mechanism between the INO80 complex and miR-372. The target relationship between miR-372 and INO80 complex was verified using luciferase assays in HCT116 colon cancer cells. As expected, miR-372 mimics significantly suppressed the luciferase activity of pMIR-luc/INO80 and pMIR-luc/Arp8 3'-UTR in cells. In contrast, the miR-372 target sites in the 3'-UTRs linked to the luciferase reporter were mutagenized, and both mutant sites lost their response to miR-372. Furthermore, the mutual modulation between the INO80 complex and miR-372 was involved in cell proliferation and the p53/p21 signaling pathway, suggesting the synergistic anti-tumor role of the INO80 complex and miR372. Our results will provide a solid theoretical basis for exploring miR-372 as a biological marker of tumorigenesis.

RBMS1 regulates lung cancer ferroptosis through translational control of SLC7A11.

Ferroptosis, an iron-dependent nonapoptotic cell death, is a highly regulated tumor suppressing process. However, functions and mechanisms of RNA-binding proteins in regulation of evasion of ferroptosis during lung cancer progression are still largely unknown. Here, we report that the RNA-binding protein RBMS1 participates in lung cancer development via mediating ferroptosis evasion. Through an shRNA-mediated systematic screen, we discovered that RBMS1 is a key ferroptosis regulator. Clinically, RBMS1 was elevated in lung cancer and its high expression was associated with reduced patient survival. Conversely, depletion of RBMS1 inhibited lung cancer progression both in vivo and in vitro. Mechanistically, RBMS1 interacted with the translation initiation factor eIF3d directly to bridge the 3'- and 5'-UTR of SLC7A11. RBMS1 ablation inhibited the translation of SLC7A11, reduced SLC7A11-mediated cystine uptake, and promoted ferroptosis. In a drug screen that targeted RBMS1, we further uncovered that nortriptyline hydrochloride decreased the level of RBMS1, thereby promoting ferroptosis. Importantly, RBMS1 depletion or inhibition by nortriptyline hydrochloride sensitized radioresistant lung cancer cells to radiotherapy. Our findings established RBMS1 as a translational regulator of ferroptosis and a prognostic factor with therapeutic potential and clinical value.

Short-term rainfall forecast model based on the improved BP-NN algorithm.

The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP-NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP-NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010-2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP-NN algorithm.

Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area.

Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman-Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is -0.66, while that between T and ET for cases of T larger or less than 0 °C are -0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979-2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

Hourly PWV Dataset Derived from GNSS Observations in China.

The rapid variation of atmospheric water vapor is important for a regional hydrologic cycle and climate change. However, it is rarely investigated in China, due to the lack of a precipitable water vapor (PWV) dataset with high temporal resolution. Therefore, this study focuses on the generation of an hourly PWV dataset using Global Navigation Satellite System (GNSS) observations derived from the Crustal Movement Observation Network of China. The zenith total delay parameters estimated by GAMIT/GLOBK software are used and validated with an average root mean square (RMS) error of 4-5 mm. The pressure (P) and temperature (T) parameters used to calculate the zenith hydrostatic delay (ZHD) and weighted average temperature of atmospheric water vapor (Tm) are derived from the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting (ECMWF ERA5) products. The values of P and T at the GNSS stations are obtained by interpolation in the horizontal and vertical directions using empirical formulas. Tm is calculated at the GNSS stations using the improved global pressure and temperature 2 wet (IGPT2w) model in China with an RMS of 3.32 K. The interpolated P and T are validated by interpolating the grid-based ERA5 data into radiosonde stations. The average RMS and bias of P and T in China are 2.71/-1.11 hPa and 1.88/-0.51 K, respectively. Therefore, the error in PWV with a theoretical RMS of 1.85 mm over the period of 2011-2017 in China can be obtained. Finally, the hourly PWV dataset in China is generated and the practical accuracy of the generated PWV dataset is validated using the corresponding AERONET and radiosonde data at specific stations. Numerical results reveal that the average RMS values of the PWV dataset in the four geographical regions of China are less than 3 mm. A case analysis of the PWV diurnal variations as a response to the EI Niño event of 2015-2016 is performed. Results indicate the capability of the hourly PWV dataset of monitoring the rapid water vapor changes in China.

SRSF1 modulates PTPMT1 alternative splicing to regulate lung cancer cell radioresistance.

BACKGROUND: Radioresistance is the major cause of cancer treatment failure. Additionally, splicing dysregulation plays critical roles in tumorigenesis. However, the involvement of alternative splicing in resistance of cancer cells to radiotherapy remains elusive. We sought to investigate the key role of the splicing factor SRSF1 in the radioresistance in lung cancer. METHODS: Lung cancer cell lines, xenograft mice models, and RNA-seq were employed to study the detailed mechanisms of SRSF1 in lung cancer radioresistance. Clinical tumor tissues and TCGA dataset were utilized to determine the expression levels of distinct SRSF1-regulated splicing isoforms. KM-plotter was applied to analyze the survival of cancer patients with various levels of SRSF1-regulated splicing isoforms. FINDINGS: Splicing factors were screened to identify their roles in radioresistance, and SRSF1 was found to be involved in radioresistance in cancer cells. The level of SRSF1 is elevated in irradiation treated lung cancer cells, whereas knockdown of SRSF1 sensitizes cancer cells to irradiation. Mechanistically, SRSF1 modulates various cancer-related splicing events, particularly the splicing of PTPMT1, a PTEN-like mitochondrial phosphatase. Reduced SRSF1 favors the production of short isoforms of PTPMT1 upon irradiation, which in turn promotes phosphorylation of AMPK, thereby inducing DNA double-strand break to sensitize cancer cells to irradiation. Additionally, the level of the short isoform of PTPMT1 is decreased in cancer samples, which is correlated to cancer patients' survival. CONCLUSIONS: Our study provides mechanistic analyses of aberrant splicing in radioresistance in lung cancer cells, and establishes SRSF1 as a potential therapeutic target for sensitization of patients to radiotherapy.

Real-time precise point positioning-based zenith tropospheric delay for precipitation forecasting.

GPS-based Zenith Tropospheric Delay (ZTD) estimation should be easily obtained in a cost-effective way, however, the most previous studies focus on post-processed ZTD estimates using satellite orbit and clock products with at least 3-9 hours latency provided by International GNSS Service (IGS), which limits the GNSS meteorological application for nowcasting. With the development of IGS's real-time pilot project (RTPP), this limitation was removed by April, 2013 as real-time satellite orbit and clock products can be obtained on-line. In this paper, on the one hand, the GPS-derived ZTD estimation was evaluated using the IGS final and real-time satellite products based on independently developed PPP software. On the other hand, the analysis of the time series of GPS-derived ZTD by least-square fitting of a broken line tendency for a full year of observations, and a forecasting method for precipitation is proposed based on the ZTD slope in the ascending period. The agreement between ZTD slope and the ground rainfall records suggested that the proposed method is useful for the assisted forecasting, especially for short-term alarms.

Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application.

ABSTARCT: Global Navigation Satellite System (GNSS) can effectively retrieve precipitable water vapor (PWV) with high precision and high-temporal resolution. GNSS-derived PWV can be used to reflect water vapor variation in the process of strong convection weather. By studying the relationship between time-varying PWV and rainfall, it can be found that PWV contents increase sharply before raining. Therefore, a short-term rainfall forecasting method is proposed based on GNSS-derived PWV. Then the method is validated using hourly GNSS-PWV data from Zhejiang Continuously Operating Reference Station (CORS) network of the period 1 September 2014 to 31 August 2015 and its corresponding hourly rainfall information. The results show that the forecasted correct rate can reach about 80%, while the false alarm rate is about 66%. Compared with results of the previous studies, the correct rate is improved by about 7%, and the false alarm rate is comparable. The method is also applied to other three actual rainfall events of different regions, different durations, and different types. The results show that the method has good applicability and high accuracy, which can be used for rainfall forecasting, and in the future study, it can be assimilated with traditional weather forecasting techniques to improve the forecasted accuracy.