Salisburysauer9756

Estuaries are important in terms of biodiversity, biogeochemical function, and ecological balance due to their intense land-sea interactions. The sustainability of estuarine ecosystem function relies on a good understanding of the ecological processes related to microbial communities. However, microbial community assembly in such ecosystems is still not well understood. Here, based on 16S rRNA sequencing, we investigated the bacterioplankton community structure in the Pearl River Estuarine system during the wet and dry seasons. Results showed that there were significant seasonal and spatial variations in the bacterioplankton communities of the estuary, with seasonal variations being more remarkable. Multiple bacterioplankton with different abundances in the wet and dry seasons were observed, e.g., the class Actinobacteria and Oxyphotobacteria were enriched in the wet season, whereas Alphaproteobacteria and Saccharimonadia were more abundant in the dry season. Both variation partitioning and null model analysis revealed that environmental filtering dominated the bacterioplankton community assembly in the Pearl River Estuary. Water physical properties (e.g., salinity and temperature), nutrient content (e.g., nitrate), and upstream land use (e.g., urban land cover) together determined the distribution of the bacterioplankton composition in this highly urbanized estuarine ecosystem. These findings would help improve our understanding of the bacterioplankton communities in estuarine ecosystems and provide a theoretical foundation for estuarine ecological health management.Retinoic acid receptors (RARs) and retinoid X receptors (RXRs) form heterodimers that activate target gene transcription by recruiting co-activator complexes in response to ligand binding. The nuclear receptor (NR) co-activator TIF2 mediates this recruitment by interacting with the ligand-binding domain (LBD) of NRs trough the nuclear receptor interaction domain (TIF2NRID) containing three highly conserved α-helical LxxLL motifs (NR-boxes). The precise binding mode of this domain to RXR/RAR is not clear due to the disordered nature of TIF2. Here we present the structural characterization of TIF2NRID by integrating several experimental (NMR, SAXS, Far-UV CD, SEC-MALS) and computational data. Collectively, the data are in agreement with a largely disordered protein with partially structured regions, including the NR-boxes and their flanking regions, which are evolutionary conserved. NMR and X-ray crystallographic data on TIF2NRID in complex with RXR/RAR reveal a multisite binding of the three NR-boxes as well as an active role of their flanking regions in the interaction.Striated muscle responds to mechanical overload by rapidly up-regulating the expression of the cardiac ankyrin repeat protein, CARP, which then targets the sarcomere by binding to titin N2A in the I-band region. To date, the role of this interaction in the stress response of muscle remains poorly understood. Here, we characterise the molecular structure of the CARP-receptor site in titin (UN2A) and its binding of CARP. We find that titin UN2A contains a central three-helix bundle fold (ca 45 residues in length) that is joined to N- and C-terminal flanking immunoglobulin domains by long, flexible linkers with partial helical content. CARP binds titin by engaging an α-hairpin in the three-helix fold of UN2A, the C-terminal linker sequence, and the BC loop in Ig81, which jointly form a broad binding interface. Ebselen Mutagenesis showed that the CARP/N2A association withstands sequence variations in titin N2A and we use this information to evaluate 85 human single nucleotide variants. In addition, actin co-sedimentation, co-transfection in C2C12 cells, proteomics on heart lysates, and the mechanical response of CARP-soaked myofibrils imply that CARP induces the cross-linking of titin and actin myofilaments, thereby increasing myofibril stiffness. We conclude that CARP acts as a regulator of force output in the sarcomere that preserves muscle mechanical performance upon overload stress.The crowded cellular milieu affect molecular diffusion through hard (occluded space) and soft (weak, non-specific) interactions. Multiple methods have been developed to measure diffusion coefficients at physiological protein concentrations within cells, each with its limitations. Here, we show that Line-FRAP, combined with rigours data analysis, is able to determine diffusion coefficients in a variety of environments, from in vitro to in vivo. The use of Line mode greatly improves time resolution of FRAP data acquisition, from 20-100 Hz in the classical mode to 800 Hz in the line mode. This improves data analysis, as intensity and radius of the bleach at the first post-bleach frame is critical. We evaluated the method on different proteins labelled chemically or fused to YFP in a wide range of environments. The diffusion coefficients measured in HeLa and in E. coli were ~2.5-fold and 15-fold slower than in buffer, and were comparable to previously published data. Increasing the osmotic pressure on E. coli further decreases diffusion, to the point at which proteins virtually stop moving. The method presented here, which requires a confocal microscope equipped with dual scanners, can be applied to study a large range of molecules with different sizes, and provides robust results in a wide range of environments and protein concentrations for fast diffusing molecules.A large fraction of peptides or protein regions are disordered in isolation and fold upon binding. These regions, also called MoRFs, SLiMs or LIPs, are often associated with signaling and regulation processes. However, despite their importance, only a limited number of examples are available in public databases and their automatic detection at the proteome level is problematic. Here we present FLIPPER, an automatic method for the detection of structurally linear sub-regions or peptides that interact with another chain in a protein complex. FLIPPER is a random forest classification that takes the protein structure as input and provides the propensity of each amino acid to be part of a LIP region. Models are built taking into consideration structural features such as intra- and inter-chain contacts, secondary structure, solvent accessibility in both bound and unbound state, structural linearity and chain length. FLIPPER is accurate when evaluated on non-redundant independent datasets, 99% precision and 99% sensitivity on PixelDB-25 and 87% precision and 88% sensitivity on DIBS-25.