Mutagenesis and computer modeling studies of a GPCR conserved residue W5.43(194) in ligand recognition and signal transduction for CB2 receptor

W5.43(194), a conserved tryptophan residue among G-protein coupled receptors (GPCRs) and cannabinoid receptors (CB), was examined in the present report for its significance in CB2 receptor ligand binding and adenylyl cyclase (AC) activity. Computer modeling postulates that this site in CB2 may be involved in the affinity of WIN55212-2 and SR144528 through aromatic contacts. In the present study, we reported that a CB2 receptor mutant, W5.43(194)Y, which had a tyrosine (Y) substitution for tryptophan (W), retained the binding affinity for CB agonist CP55940, but reduced binding affinity for CB2 agonist WIN55212-2 and inverse agonist SR144528 by 8-fold and 5-fold, respectively; the CB2 W5.43(194)F and W5.43(194)A mutations significantly affect the binding activities of CP55940, WIN55212-2 and SR144528. Furthermore, we found that agonist-mediated inhibition of the forskolin-induced cAMP production was dramatically diminished in the CB2 mutant W5.43(194)Y, whereas W5.43(194)F and W5.43(194)A mutants resulted in complete elimination of downstream signaling, suggesting that W5.43(194) was essential for the full activation of CB2. These results indicate that both aromatic interaction and hydrogen bonding are involved in ligand binding for the residue W5.43(194), and the mutations of this tryptophan site may affect the conformation of the ligand binding pocket and therefore control the active conformation of the wild type CB2 receptor. W5.43(194)Y/F/A mutations also displayed noticeable enhancement of the constitutive activation probably attributed to the receptor conformational changes resulted from the mutations.

Impacts of changing climate and land use on vegetation dynamics in a Mediterranean ecosystem: insights from paleoecology and dynamic modeling

Forests near the Mediterranean coast have been shaped by millennia of human disturbance. Consequently, ecological studies relying on modern observations or historical records may have difficulty assessing natural vegetation dynamics under current and future climate. We combined a sedimentary pollen record from Lago di Massacciucoli, Tuscany, Italy with simulations from the LandClim dynamic vegetation model to determine what vegetation preceded intense human disturbance, how past changes in vegetation relate to fire and browsing, and the potential of an extinct vegetation type under present climate. We simulated vegetation dynamics near Lago di Massaciucoli for the last 7,000 years using a local chironomid-inferred temperature reconstruction with combinations of three fire regimes (small infrequent, large infrequent, small frequent) and three browsing intensities (no browsing, light browsing, and moderate browsing), and compared model output to pollen data. Simulations with low disturbance support pollen-inferred evidence for a mixed forest dominated by Quercus ilex (a Mediterranean species) and Abies alba (a montane species). Whereas pollen data record the collapse of A. alba after 6000 cal yr bp, simulated populations expanded with declining summer temperatures during the late Holocene. Simulations with increased fire and browsing are consistent with evidence for expansion by deciduous species after A. alba collapsed. According to our combined paleo-environmental and modeling evidence, mixed Q. ilex and A. alba forests remain possible with current climate and limited disturbance, and provide a viable management objective for ecosystems near the Mediterranean coast and in regions that are expected to experience a mediterranean-type climate in the future.

Building a soil spectral library for Tajikistan comparing local and global modeling approaches

Over the last ~20 years, soil spectral libraries storing near-infrared reflectance (NIR) spectra from diverse soil samples have been built for many places, since almost 10 years also for Tajikistan. Many calibration approaches have been reported and used for prediction from large and heterogeneous libraries, but most are hampered by the high diversity of the soils, where the mineral background is heavily influencing spectral features. In such cases, local learning strategies have the advantage of building locally adapted calibrations, which can deal better with nonlinearities. Therefore, it was our major aim to identify the most efficient approach to develop an accurate and stable locally weigthed calibration model using a spectral library compiled over the past years. Keywords: Tajikistan, Near-Infrared spectroscopy (NIRS), soil organic carbon, locally weighted regression, regional and local spectral library.