MiR-135a-5p suppresses breast cancer cell proliferation, migration, and invasion by regulating BAG3

Background: MicroRNAs (miRNAs) are involved in the progression of diverse human cancers. This work aimed to delve into how microRNA-135a-5p (miR-135a-5p) affects the biological behaviors of Breast Cancer (BC) cells. Methods: Gene Expression Omnibus (GEO) datasets were used to analyze the expression differences of miR-135a-5p in cancer tissues of BC patients. Quantitative real-time PCR and western blot were conducted to detect miR-135a-5p and Bcl-2 Associated Athanogene (BAG3) expression levels in BC tissues and cells, respectively. The proliferation, migration, invasion, and cell cycle of BC cells were detected by cell counting kit-8 assay, BrdU assay, wound healing assay, transwell assay, and flow cytometry. The targeted relationship between miR-135a-5p and BAG3 mRNA 3′UTR predicted by bioinformatics was further testified by a dual-luciferase reporter gene assay. Pearson's correlation analysis was adopted to analyze the correlation between miR-135a-5p expression and BAG3 expression. The downstream pathways of BAG3 were analyzed by the LinkedOmics database. Results: MiR-135a-5p was significantly down-regulated and BAG3 expression was significantly raised in BC tissues. MiR-135a-5p overexpression repressed the viability, migration and invasion of BC cells, and blocked cell cycle progression in G0/G1 phase while inhibiting miR-135a-5p worked oppositely. BAG3 was verified as a target of miR-135a-5p. Overexpression of BAG3 reversed the impacts of miR-135a-5p on the malignant biological behaviors of BC cells. The high expression of BAG3 was associated with the activation of the cell cycle, mTOR and TGF-β signaling pathways. Conclusion: MiR-135a-5p regulates BAG3 to repress the growth, migration, invasion, and cell cycle progression of BC cells

G-protein-coupled estrogen receptor agonist G-1 inhibits the proliferation of breast cancer cells through induction of apoptosis and cycle arrest

Purpose: To determine the effect of G-1, a G-protein-linked estrogen receptor (GPER) agonist on apoptosis, cell cycle, and proliferative potential of mammary tumor cells, and the associated mechanisms of action. Methods: Three groups of human breast cancer cell line MDA-MB-231 were used: control group, estradiol (E2) group and G-1 group. Control group was not treated. The effects of treatment (10 M G1) on cell proliferation were determined and compared amongst the groups. Cell cycle distribution and apoptosis were determined while expression levels of proteins related to pi3k/AKT/MAPK were assessed by western blotting. Results: Apoptosis was significantly reduced in E2 group relative to control, but was enhanced in G-1 group, when compared to the other 2 groups (p < 0.05). There were marked down-regulations in protein levels of cylinb1, p21, caspase 6, p53, p-ERK in E2 group, relative to the corresponding expression levels in the control group. Conclusion: GPER agonist G-1 suppresses the proliferation of mammary tumor cells and induces apoptotic changes and cycle blockage in the cells via inhibition of pi3k/AKT pathway and activation of MAPKs pathway. Thus, GPER is a potential target in breast tumor treatment, and G-1 is a potential new anti-tumor drug

Atrial Fibrillation Follow-up Investigation to Recover Memory and Learning Trial (AFFIRMING): Rationale and Design of a Multi-center, Double-blind, Randomized Controlled Trial

Background: People with atrial fibrillation (AF) have elevated risk of developing cognitive impairment. At present, there is a dearth of randomized controlled trials investigating cognitive impairment management in patients with AF. The Atrial Fibrillation Follow-up Investigation to Recover Memory and learning (AFFIRMING) study is aimed at evaluating the potential for computerized cognitive training to improve cognitive function in patients with AF. Methods: The study is a multi-center, double-blind, randomized controlled study using a 1:1 parallel design. A total of 200 patients with AF and mild cognitive decline without dementia are planned to be recruited. The intervention group will use the adaptive training software with changes in difficulty, whereas the positive control group will use basic training software with minimal or no variation in difficulty level. At the end of 12 weeks, the participants will be unblinded, and the positive control group will stop training. The intervention group will be rerandomized 1:1 to stop training or continue training. All participants will be followed up until 24 weeks. The primary endpoint is the proportion of the improvement of the global cognitive function at week 12 compared with baseline, using the Basic Cognitive Ability Test (BCAT)

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe

Binding of Tetracycline and Chlortetracycline to the Enzyme Trypsin: Spectroscopic and Molecular Modeling Investigations

Tetracycline (TC) and chlortetracycline (CTC) are common members of the widely used veterinary drug tetracyclines, the residue of which in the environment can enter human body, being potentially harmful. In this study, we establish a new strategy to probe the binding modes of TC and CTC with trypsin based on spectroscopic and computational modeling methods. Both TC and CTC can interact with trypsin with one binding site to form trypsin-TC (CTC) complex, mainly through van der Waals' interactions and hydrogen bonds with the affinity order: TC>CTC. The bound TC (CTC) can result in inhibition of trypsin activity with the inhibition order: CTC>TC. The secondary structure and the microenvironment of the tryptophan residues of trypsin were also changed. However, the effect of CTC on the secondary structure content of trypsin was contrary to that of TC. Both the molecular docking study and the trypsin activity experiment revealed that TC bound into S1 binding pocket, competitively inhibiting the enzyme activity, and CTC was a non-competitive inhibitor which bound to a non-active site of trypsin, different from TC due to the Cl atom on the benzene ring of CTC which hinders CTC entering into the S1 binding pocket. CTC does not hinder the binding of the enzyme substrate, but the CTC-trypsin-substrate ternary complex can not further decompose into the product. The work provides basic data for clarifying the binding mechanisms of TC (CTC) with trypsin and can help to comprehensively understanding of the enzyme toxicity of different members of tetracyclines in vivo

Data-driven approaches to resolving feedback processes driving the earth system over multi- spatial and temporal scales

Data is one of the essential components in analyzing complex earth system problems. With high-quality data more feasible to the researchers, more details of the system could be revealed by those data-intensive computational and data-driven approaches. The measurement and data collection devices have been developing dramatically, especially those used for earth system science. The high sampling resolution in all spatial, temporal and spectral scale have enabled the analysis of earth system problems into a data-driven era. Meanwhile, the fast development of computational ability and resources allow the emergence of innovative data-driven methods (e.g., information theory, traditional statistical learning models, deep learning models). The data-driven approach is different from the physical-based (or knowledge-based) modeling. It emphasizes learning and generalizing the rules from large amounts of representative data. It tries to fit the probability distribution function, for any questions, with the support of large numbers of observations with little constraining conditions like those from the physical-based model. However, before relying on purely data-driven methods, it is essential to remember that Earth systems are characterized as nonlinear, complex and dynamic systems with couplings and feedback among components and subsystems.Additionally, these coupled processes change depending on the status of the system and the spatial and temporal scale at which the system is analyzed. To understand the underlying mechanisms that drive complex systems, it is useful to conceptualize the system as a network of variables undergoing interactions and feedback. Traditional statistical analysis methods are ill-suited to capture the key attributes of this type of feedback processes due to the stochasticity of the variables, the nonlinearities of the couplings and the non-stationarity of the system. The limitation of the data (in terms of resolution and length in both spatial and temporal scale) and computational ability further narrow the effectiveness of those methods.The various science communities are now facing a new challenging problem. On the one hand, you have 1) more and more data being collected, 2) the significantly-improved ability to depict the status of a system and to describe the details of a relationship between the components within the system, and 3) the computational capacity and resources to be able to handle this large number of data, motivating the use of data-driven methods. In this dissertation, I will examine the potential for integrating data-driven techniques into earth systems science to improve our understanding of earth-surface processes. Specifically, I focus on applying data-driven techniques for resolving causal interactions of the several complex earth systems over multispectral and temporal scales. Four complex earth system problems with different spatial and temporal scales are discussed. First, we implement the data-driven methods in regional and decadal issues, streamflow prediction, as a case study. Our findings suggest that while information-flow identifies dominant streamflow controls, the results should not be limited to only “critical hydrologic timescales;” instead they should guide a range of timescales over which inputs, stores, and losses are filtered into catchment discharge. Second, we analyzed a regional and yearly problem, the feedback process between vegetation and topography in a lake delta ecosystem. The transfer entropy analysis suggests that different vegetation communities play functionally different roles in landscape evolution that should be differentiated in ecogeomorphic models. Within such models, it would be most imperative to resolve detailed flow characteristics at lower to low-middle island elevations.Furthermore, within elevation zones, it is likely essential to differentiate between the roles of multiple vegetation communities rather than treating the entire elevation zone as a single ecogeomorphic entity. Third, we analyzed global and millennium problems, the interaction among climatically variables over 42,000 years. We show that, during the past 420,000 years, orbital forcings trigger temperature and CO2 responses at short (5 kyr) time lags. Over longer timescales, internal feedback, mediated by interactions with dust, also plays a significant role in governing temperature and CO2 concentrations. The short-term influence of CO2 on temperature was stronger than dust’s long-term impact, consistent with on radiative forcing. However, dust remained an essential driver of temperature over 50-kyr time lags, the amount of time between sequential glacial maxima and minima during the latter portion of the Pleistocene. Last, we analyzed a global and decadal problem, the interaction between ocean and precipitation on land. We quantitatively demonstrate that Sea Surface Temperature (SST) over the Gulf of Guinea controls moisture advection and transport to the West Sahel region; strong bidirectional interaction exists between local vegetation dynamics and rainfall patterns. The spatial distribution map of time lag with most significant transfer entropy also shows the apparent trend of each climate indices tested in this research. The Niño 3+4 and Niño 4 have a relatively short time lag with significant transfer entropy to the west coast and have insignificant information transferred to the middle US. The Niño 1+2 and Niño 3 have a relatively short time lag with significant information transferred to the middle region but insignificant information transferred to the west coast.By testing the effectiveness and efficiency of the data-driven methods in complex earth system problems over multiple spatial and temporal scales, the results verified the ability of those methods in identifying and quantifying the strength, statistical significance, directionality and critical time lags of feedback (as well as one-way forcing) among variables. With these data-driven methods, we could identify which components comprise the system, and which dominate changes within the system. With the input of that knowledge, we could further predict the behavior of an element of interest or the stationery of the whole system and simulate the future behavior of the system under different scenario after fully understanding the rules and the connections of a system

Data-driven approaches to resolving feedback processes driving the earth system over multi- spatial and temporal scales

Data is one of the essential components in analyzing complex earth system problems. With high-quality data more feasible to the researchers, more details of the system could be revealed by those data-intensive computational and data-driven approaches. The measurement and data collection devices have been developing dramatically, especially those used for earth system science. The high sampling resolution in all spatial, temporal and spectral scale have enabled the analysis of earth system problems into a data-driven era. Meanwhile, the fast development of computational ability and resources allow the emergence of innovative data-driven methods (e.g., information theory, traditional statistical learning models, deep learning models). The data-driven approach is different from the physical-based (or knowledge-based) modeling. It emphasizes learning and generalizing the rules from large amounts of representative data. It tries to fit the probability distribution function, for any questions, with the support of large numbers of observations with little constraining conditions like those from the physical-based model. However, before relying on purely data-driven methods, it is essential to remember that Earth systems are characterized as nonlinear, complex and dynamic systems with couplings and feedback among components and subsystems.Additionally, these coupled processes change depending on the status of the system and the spatial and temporal scale at which the system is analyzed. To understand the underlying mechanisms that drive complex systems, it is useful to conceptualize the system as a network of variables undergoing interactions and feedback. Traditional statistical analysis methods are ill-suited to capture the key attributes of this type of feedback processes due to the stochasticity of the variables, the nonlinearities of the couplings and the non-stationarity of the system. The limitation of the data (in terms of resolution and length in both spatial and temporal scale) and computational ability further narrow the effectiveness of those methods.The various science communities are now facing a new challenging problem. On the one hand, you have 1) more and more data being collected, 2) the significantly-improved ability to depict the status of a system and to describe the details of a relationship between the components within the system, and 3) the computational capacity and resources to be able to handle this large number of data, motivating the use of data-driven methods. In this dissertation, I will examine the potential for integrating data-driven techniques into earth systems science to improve our understanding of earth-surface processes. Specifically, I focus on applying data-driven techniques for resolving causal interactions of the several complex earth systems over multispectral and temporal scales. Four complex earth system problems with different spatial and temporal scales are discussed. First, we implement the data-driven methods in regional and decadal issues, streamflow prediction, as a case study. Our findings suggest that while information-flow identifies dominant streamflow controls, the results should not be limited to only “critical hydrologic timescales;” instead they should guide a range of timescales over which inputs, stores, and losses are filtered into catchment discharge. Second, we analyzed a regional and yearly problem, the feedback process between vegetation and topography in a lake delta ecosystem. The transfer entropy analysis suggests that different vegetation communities play functionally different roles in landscape evolution that should be differentiated in ecogeomorphic models. Within such models, it would be most imperative to resolve detailed flow characteristics at lower to low-middle island elevations.Furthermore, within elevation zones, it is likely essential to differentiate between the roles of multiple vegetation communities rather than treating the entire elevation zone as a single ecogeomorphic entity. Third, we analyzed global and millennium problems, the interaction among climatically variables over 42,000 years. We show that, during the past 420,000 years, orbital forcings trigger temperature and CO2 responses at short (5 kyr) time lags. Over longer timescales, internal feedback, mediated by interactions with dust, also plays a significant role in governing temperature and CO2 concentrations. The short-term influence of CO2 on temperature was stronger than dust’s long-term impact, consistent with on radiative forcing. However, dust remained an essential driver of temperature over 50-kyr time lags, the amount of time between sequential glacial maxima and minima during the latter portion of the Pleistocene. Last, we analyzed a global and decadal problem, the interaction between ocean and precipitation on land. We quantitatively demonstrate that Sea Surface Temperature (SST) over the Gulf of Guinea controls moisture advection and transport to the West Sahel region; strong bidirectional interaction exists between local vegetation dynamics and rainfall patterns. The spatial distribution map of time lag with most significant transfer entropy also shows the apparent trend of each climate indices tested in this research. The Niño 3+4 and Niño 4 have a relatively short time lag with significant transfer entropy to the west coast and have insignificant information transferred to the middle US. The Niño 1+2 and Niño 3 have a relatively short time lag with significant information transferred to the middle region but insignificant information transferred to the west coast.By testing the effectiveness and efficiency of the data-driven methods in complex earth system problems over multiple spatial and temporal scales, the results verified the ability of those methods in identifying and quantifying the strength, statistical significance, directionality and critical time lags of feedback (as well as one-way forcing) among variables. With these data-driven methods, we could identify which components comprise the system, and which dominate changes within the system. With the input of that knowledge, we could further predict the behavior of an element of interest or the stationery of the whole system and simulate the future behavior of the system under different scenario after fully understanding the rules and the connections of a system