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ABSTRACT The plant–mycorrhizal fungi relationship can range from mutualistic to parasitic as a function of the fungal taxa involved, plant ontogeny, as well as the availability of resources. Despite the implications this relationship may have on forest carbon cycling and storage, we know little about how mature trees may be impacted by mycorrhizae and how this impact may vary across the landscape. We collected growth data of two arbuscular mycorrhizal fungi (AMF)‐associated tree species,Acer rubrumandA. saccharum, and one ectomycorrhizal fungi (EMF)‐associated tree species,Quercus rubra, to assess how the mycorrhizal fungi–plant association may vary along a gradient of nitrogen (N) availability. Individual assessments of fungal taxa relative abundances showed non‐linear associations with tree growth; positive associations for the two AMF‐associated trees were mostly under low N, whereas positive to neutral associations for the EMF‐associated tree mainly took place at high N. OnlyA. rubrumexhibited greater tree growth with its tree soil‐specific mycorrhizal community when compared with predictions under a random mycorrhizal soil community. Because mycorrhizal fungi are likely to mediate how plants respond to warming, increasing levels of N deposition and of atmospheric CO₂, understanding these relationships is critical to accurately forecasting tree growth. 

ABSTRACT G‐protein‐coupled receptors (GPCRs) constitute one of the most prominent families of integral membrane receptor proteins that mediate most transmembrane signaling processes. Malfunction of these signal transduction processes is one of the underlying causes of many human pathologies (Parkinson's, Huntington's, heart diseases, etc), provoking that GPCRs are the largest family of druggable proteins. However, these receptors have been targeted traditionally by orthosteric ligands, which usually causes side effects due to the simultaneous targeting of homologous receptor subtypes. Allosteric modulation offers a promising alternative approach to circumvent this problematic and, thus, comprehending its details is a most important task. Here we use the Cannabinoid type‐1 receptor (CB1R) in trying to shed light on this issue, focusing on positive allosteric modulation. This is done by using the protein‐dipole Langevin‐dipole (PDLD) within the linear response approximation (LRA) framework (PDLD/S‐2000) along with our coarse‐grained (CG) model of membrane proteins to evaluate the dissociation constants (K_Bs) and cooperativity factors (αs) for a diverse series of CB1R positive allosteric modulators belonging to the 2‐phenylindole structural class, considering CP55940 as an agonist. The agreement with the experimental data evinces that significantly populated allosteric modulator:CB1R and allosteric modulator:CP55940:CB1R complexes have been identified and characterized successfully. Analyzing them, it has been determined that CB1R positive allosteric modulation lies in an outwards displacement of transmembrane α helix (TM) 4 extracellular end and in the regulation of the range of motion of a compound TM7 movement for binary and ternary complexes, respectively. In this respect, we achieved a better comprehension of the molecular architecture of CB1R positive allosteric site, identifying Lys192^3.28and Gly194^3.30as key residues regarding electrostatic interactions inside this cavity, and to rationalize (at both structural and molecular level) the exhibited stereoselectivity in relation to positive allosteric modulation activity by considered CB1R allosteric modulators. Additionally, putative/postulated allosteric binding sites have been screened successfully, identifying the real CB1R positive allosteric site, and most structure–activity relationship (SAR) studies of CB1R 2‐phenylindole allosteric modulators have been rationalized. All these findings point out towards the predictive value of the methodology used in the current work, which can be applied to other biophysical systems of interest. The results presented in this study contribute significantly to understand GPCRs allosteric modulation and, hopefully, will encourage a more thorough exploration of the topic. 

Abstract Arbuscular mycorrhizal fungi (AMF) are widespread obligate symbionts of plants. This dynamic symbiosis plays a large role in successful plant performance, given that AMF help to ameliorate plant responses to abiotic and biotic stressors. Although the importance of this symbiosis is clear, less is known about what may be driving this symbiosis, the plant's need for nutrients or the excess of plant photosynthate being transferred to the AMF, information critical to assess the functionality of this relationship. Characterizing the AMF community along a natural plant productivity gradient is a first step in understanding how this symbiosis may vary across the landscape. We surveyed the AMF community diversity at 12 sites along a plant productivity gradient driven by soil nitrogen availability. We found that AMF diversity in soil environmental DNA significantly increased along with the growth of the host plantsAcerrubrumandA. saccharum., a widespread tree genus. These increases also coincided with a natural soil inorganic N availability gradient. We hypothesize photosynthate from the increased tree growth is being allocated to the belowground AMF community, leading to an increase in diversity. These findings contribute to understanding this complex symbiosis through the lens of AMF turnover and suggest that a more diverse AMF community is associated with increased host–plant performance. 

Introduction: Studies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer’s disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations. Methods: In a retrospective cohort study, EHRdata from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM). Results: The VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM). Conclusion: Approaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.