Comprehensive databases of microRNA–disease associations are continuously demanded in biomedical researches. The recently launched version 3.0 of Human MicroRNA Disease Database (HMDD v3.0) manually collects a significant number of miRNA–disease association entries from literature. Comparing to HMDD v2.0, this new version contains 2-fold more entries. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories (genetics, epigenetics, target, circulation, tissue and other) covering 20 types of detailed evidence code. Furthermore, we added new functionalities like network visualization on the web interface. To exemplify the utility of the database, we compared the disease spectrum width of miRNAs (DSW) and the miRNA spectrum width of human diseases (MSW) between version 3.0 and 2.0 of HMDD. HMDD is freely accessible at http://www.cuilab.cn/hmdd. With accumulating evidence of miRNA–disease associations, HMDD database will keep on growing in the future.
Nitrogen (N) fertilization affects the rate of soil organic carbon (SOC) decomposition by regulating extracellular enzyme activities (EEA). Extracellular enzymes have not been represented in global biogeochemical models. Understanding the relationships among EEA and SOC, soil N (TN), and soil microbial biomass carbon (MBC) under N fertilization would enable modeling of the influence of EEA on SOC decomposition. Based on 65 published studies, we synthesized the activities of α-1,4-glucosidase (AG), β-1,4-glucosidase (BG), β-D-cellobiosidase (CBH), β-1,4-xylosidase (BX, β-1,4-N-acetyl-glucosaminidase (NAG), leucineamino peptidase (LAP), urease (UREA), acid phosphatase (AP), phenol oxidase (PHO), and peroxidase (PEO) in response to N fertilization. The proxy variables for hydrolytic C acquisition enzymes (C-acq), N acquisition (N-acq), and oxidative decomposition (OX) were calculated as the sum of AG, BG, CBH and BX; AG and LAP; PHO and PEO, respectively. The relationships between response ratios (RRs) of EEA and SOC, TN, or MBC were explored when they were reported simultaneously. Results showed that N fertilization significantly increased CBH, C-acq, AP, BX, BG, AG, and UREA activities by 6.4, 9.1, 10.6, 11.0, 11.2, 12.0, and 18.6%, but decreased PEO, OX and PHO by 6.1, 7.9 and 11.1%, respectively. N fertilization enhanced SOC and TN by 7.6% and 15.3%, respectively, but inhibited MBC by 9.5%. Significant positive correlations were found only between the RRs of C-acq and MBC, suggesting that changes in combined hydrolase activities might act as a proxy for MBC under N fertilization. In contrast with other variables, the RRs of AP, MBC, and TN showed unidirectional trends under different edaphic, environmental, and physiological conditions. Our results provide the first comprehensive set of evidence of how hydrolase and oxidase activities respond to N fertilization in various ecosystems. Future large-scale model projections could incorporate the observed relationship between hydrolases and microbial biomass as a proxy for C acquisition under global N enrichment scenarios in different ecosystems.
Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.
BackgroundCircular RNAs (circRNAs) have been found to play critical roles in the development and progression of various cancers. However, little is known about the effects of the circular RNA network on glioblastoma multiforme (GBM).MethodsA microarray was used to screen circRNA expression in GBM. Quantitative real-time PCR was used to detect the expression of circMMP9. GBM cells were transfected with a circMMP9 overexpression vector or siRNA, and cell proliferation, migration and invasion, as well as tumorigenesis in nude mice, were assessed to examine the effect of circMMP9 in GBM. Biotin-coupled miRNA capture, fluorescence in situ hybridization and luciferase reporter assays were conducted to confirm the relationship between circMMP9 and miR-124.ResultsIn this study, we screened differentially expressed circRNAs and identified circMMP9 in GBM. We found that circMMP9 acted as an oncogene, was upregulated in GBM and promoted the proliferation, migration and invasion abilities of GBM cells. Next, we verified that circMMP9 served as a sponge that directly targeted miR-124; circMMP9 accelerated GBM cell proliferation, migration and invasion by targeting miR-124. Furthermore, we found that cyclin-dependent kinase 4 (CDK4) and aurora kinase A (AURKA) were involved in circMMP9/miR-124 axis-induced GBM tumorigenesis. Finally, we found that eukaryotic initiation factor 4A3 (eIF4A3), which binds to the MMP9 mRNA transcript, induced circMMP9 cyclization and increased circMMP9 expression in GBM.ConclusionsOur findings indicate that eIF4A3-induced circMMP9 is an important underlying mechanism in GBM cell proliferation, invasion and metastasis through modulation of the miR-124 signaling pathway, which could provide pivotal potential therapeutic targets for the treatment of GBM.Graphical abstract Electronic supplementary materialThe online version of this article (10.1186/s12943-018-0911-0) contains supplementary material, which is available to authorized users.
Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO 2 uptake period (CUP) and the seasonal maximal capacity of CO 2 uptake (GPP max ). The product of CUP and GPP max explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r 2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r 2 = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPP max than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPP max and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space. ecosystem carbon uptake | growing season length | photosynthetic capacity | spatiotemporal variability | climate extreme L arge variability exists among estimates of terrestrial carbon sequestration, resulting in substantial uncertainty in modeled dynamics of atmospheric CO 2 concentration and predicted future climate change (1). The variability in carbon sequestration is partially caused by variation in terrestrial gross primary productivity (GPP) (2), which is the cumulative rate over time of gross plant Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO 2 fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index-the product of the length of CO 2 uptake period and the seasonal maximal photosynthesis-to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.
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