EMD273316 & EMD95833, type 4 phosphodiesterase inhibitors, stimulate fibroblastic-colony formation by bone marrow cells via direct inhibition of PDE4 and the induction of endogenous prostaglandin synthesis

BACKGROUND: Type 4 phosphodiesterase (PDE4) inhibitors have been shown to stimulate bone formation in vivo and to stimulate osteoblastic differentiation in vitro. As one possible mechanism for the stimulation of bone formation is the recruitment of osteoprogenitor cells from the bone marrow, we have investigated the effect of the PDE4 inhibitors EMD273316, EMD95833, EMD249615 and EMD 219906 on fibroblastic colony formation by whole bone marrow cells and on the ability of these colonies to adopt an osteoblastic phenotype. RESULTS: All four agents stimulated colony formation in a concentration dependent manner, however, in the case of EMD273316 & EMD95833, the effect was evident at lower concentrations and the addition of prostaglandin E2 (PGE2) was not necessary for maximal stimulation. It was subsequently found that co-incubation with indomethacin reduced the stimulatory effects of EMD273316 & EMD95833 but had no effect on the actions of EMD249615 and EMD 219906 and that EMD273316 & EMD95833 stimulated the synthesis of endogenous PGE2 by whole bone marrow cells whereas EMD249615 and EMD 219906 had no significant effect. CONCLUSIONS: These data suggest that EMD249615, EMD 219906, EMD273316 & EMD95833 can promote the recruitment of bone marrow osteoprogenitor cells leading to a stimulation of bone formation via their direct inhibitory effects on PDE4. The actions of EMD273316 & EMD95833 however, are augmented by their ability to stimulate endogenous prostanoids synthesis which acts synergistically with their direct effects on PDE4

Analyzing Machupo virus-receptor binding by molecular dynamics simulations

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-virus protein-protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.Comment: 33 pages, 8 figures, 5 table

Preface

This special issue of the Journal of Geophysical Research presents 47 papers developed from research on the two deep ice cores drilled in central Greenland during the years 1989-1993 by the U.S. Greenland Ice Sheet Project 2 (GISP2) and the European Greenland Ice Core Program (GRIP). In this grand experiment, two large ice-core-drilling programs were combined. A major reason was to validate the presence of fast climate oscillations that could not be verified by a single deep ice core

What Are You Feeling? Using Functional Magnetic Resonance Imaging to Assess the Modulation of Sensory and Affective Responses during Empathy for Pain

BACKGROUND: Recent neuroscientific evidence suggests that empathy for pain activates similar neural representations as the first-hand experience of pain. However, empathy is not an all-or-none phenomenon but it is strongly malleable by interpersonal, intrapersonal and situational factors. This study investigated how two different top-down mechanisms - attention and cognitive appraisal - affect the perception of pain in others and its neural underpinnings. METHODOLOGY/PRINCIPAL FINDINGS: We performed one behavioral (N = 23) and two functional magnetic resonance imaging (fMRI) experiments (N = 18). In the first fMRI experiment, participants watched photographs displaying painful needle injections, and were asked to evaluate either the sensory or the affective consequences of these injections. The role of cognitive appraisal was examined in a second fMRI experiment in which participants watched injections that only appeared to be painful as they were performed on an anesthetized hand. Perceiving pain in others activated the affective-motivational and sensory-discriminative aspects of the pain matrix. Activity in the somatosensory areas was specifically enhanced when participants evaluated the sensory consequences of pain. Perceiving non-painful injections into the anesthetized hand also led to signal increase in large parts of the pain matrix, suggesting an automatic affective response to the putatively harmful stimulus. This automatic response was modulated by areas involved in self/other distinction and valence attribution - including the temporo-parietal junction and medial orbitofrontal cortex. CONCLUSIONS/SIGNIFICANCE: Our findings elucidate how top-down control mechanisms and automatic bottom-up processes interact to generate and modulate other-oriented responses. They stress the role of cognitive processing in empathy, and shed light on how emotional and bodily awareness enable us to evaluate the sensory and affective states of others

50 Years of Cumulative Open-Source Data Confirm Stable and Robust Biodiversity Distribution Patterns for Macrofungi

Fungi are a hyper-diverse kingdom that contributes significantly to the regulation of the global carbon and nutrient cycle. However, our understanding of the distribution of fungal diversity is often hindered by a lack of data, especially on a large spatial scale. Open biodiversity data may provide a solution, but concerns about the potential spatial and temporal bias in species occurrence data arising from different observers and sampling protocols challenge their utility. The theory of species accumulation curves predicts that the cumulative number of species reaches an asymptote when the sampling effort is sufficiently large. Thus, we hypothesize that open biodiversity data could be used to reveal large-scale macrofungal diversity patterns if these datasets are accumulated long enough. Here, we tested our hypothesis with 50 years of macrofungal occurrence records in Norway and Sweden that were downloaded from the Global Biodiversity Information Facility (GBIF). We first grouped the data into five temporal subsamples with different cumulative sampling efforts (i.e., accumulation of data for 10, 20, 30, 40 and 50 years). We then predicted the macrofungal diversity and distribution at each subsample using the maximum entropy (MaxEnt) species distribution model. The results revealed that the cumulative number of macrofungal species stabilized into distinct distribution patterns with localized hotspots of predicted macrofungal diversity with sampling efforts greater than approximately 30 years. Our research demonstrates the utility and importance of the long-term accumulated open biodiversity data in studying macrofungal diversity and distribution at the national level.publishedVersio

Mining Patents with Large Language Models Demonstrates Congruence of Functional Labels and Chemical Structures

Predicting chemical function from structure is a major goal of the chemical sciences, from the discovery and repurposing of novel drugs to the creation of new materials. Recently, new machine learning algorithms are opening up the possibility of general predictive models spanning many different chemical functions. Here, we consider the challenge of applying large language models to chemical patents in order to consolidate and leverage the information about chemical functionality captured by these resources. Chemical patents contain vast knowledge on chemical function, but their usefulness as a dataset has historically been neglected due to the impracticality of extracting high-quality functional labels. Using a scalable ChatGPT-assisted patent summarization and word-embedding label cleaning pipeline, we derive a Chemical Function (CheF) dataset, containing 100K molecules and their patent-derived functional labels. The functional labels were validated to be of high quality, allowing us to detect a strong relationship between functional label and chemical structural spaces. Further, we find that the co-occurrence graph of the functional labels contains a robust semantic structure, which allowed us in turn to examine functional relatedness among the compounds. We then trained a model on the CheF dataset, allowing us to assign new functional labels to compounds. Using this model, we were able to retrodict approved Hepatitis C antivirals, uncover an antiviral mechanism undisclosed in the patent, and identify plausible serotonin-related drugs. The CheF dataset and associated model offers a promising new approach to predict chemical functionality.Comment: Under revie

In the blink of an eye: reading mental states from briefly presented eye regions

Faces provide not only cues to an individual’s identity, age, gender, and ethnicity but also insight into their mental states. The aim was to investigate the temporal aspects of processing of facial expressions of complex mental states for very short presentation times ranging from 12.5 to 100 ms in a four-alternative forced choice paradigm based on Reading the Mind in the Eyes test. Results show that participants are able to recognise very subtle differences between facial expressions; performance is better than chance, even for the shortest presentation time. Importantly, we show for the first time that observers can recognise these expressions based on information contained in the eye region only. These results support the hypothesis that the eye region plays a particularly important role in social interactions and that the expressions in the eyes are a rich source of information about other peoples’ mental states. When asked to what extent the observers guessed during the task, they significantly underestimated their ability to make correct decisions, yet perform better than chance, even for very brief presentation times. These results are particularly relevant in the light of the current COVID-19 pandemic and the associated wearing of face coverings. </jats:p