Galectin from Trichinella spiralis alleviates DSS-induced colitis in mice by regulating the intestinal microbiota.

According to numerous reports, Trichinella spiralis (T. spiralis) and its antigens can reduce intestinal inflammation by modulating regulatory immunological responses in the host to maintain immune homeostasis. Galectin has been identified as a protein that is produced by T. spiralis, and its characterization revealed this protein has possible immune regulatory activity. However, whether recombinant T. spiralis galectin (rTs-gal) can cure dextran sulfate sodium (DSS)-induced colitis remains unknown. Here, the ability of rTs-gal to ameliorate experimental colitis in mice with inflammatory bowel disease (IBD) as well as the potential underlying mechanism were investigated. The disease activity index (DAI), colon shortening, inflammatory cell infiltration, and histological damage were used as indicators to monitor clinical symptoms of colitis. The results revealed that the administration of rTs-gal ameliorated these symptoms. According to Western blotting and ELISA results, rTs-gal may suppress the excessive inflammatory response-mediated induction of TLR4, MyD88, and NF-κB expression in the colon. Mice with colitis exhibit disruptions in the gut flora, including an increase in gram-negative bacteria, which in turn can result in increased lipopolysaccharide (LPS) production. However, injection of rTs-gal may inhibit changes in the gut microbiota, for example, by reducing the prevalence of Helicobacter and Bacteroides, which produce LPS. The findings of the present study revealed that rTs-gal may inhibit signalling pathways that involve enteric bacteria-derived LPS, TLR4, and NF-κB in mice with DSS-induced colitis and attenuate DSS-induced colitis in animals by modulating the gut microbiota. These findings shed additional light on the immunological processes underlying the beneficial effects of helminth-derived proteins in medicine.

A Multimodal Protein Representation Framework for Quantifying Transferability Across Biochemical Downstream Tasks.

Proteins are the building blocks of life, carrying out fundamental functions in biology. In computational biology, an effective protein representation facilitates many important biological quantifications. Most existing protein representation methods are derived from self-supervised language models designed for text analysis. Proteins, however, are more than linear sequences of amino acids. Here, a multimodal deep learning framework for incorporating ≈1 million protein sequence, structure, and functional annotation (MASSA) is proposed. A multitask learning process with five specific pretraining objectives is presented to extract a fine-grained protein-domain feature. Through pretraining, multimodal protein representation achieves state-of-the-art performance in specific downstream tasks such as protein properties (stability and fluorescence), protein-protein interactions (shs27k/shs148k/string/skempi), and protein-ligand interactions (kinase, DUD-E), while achieving competitive results in secondary structure and remote homology tasks. Moreover, a novel optimal-transport-based metric with rich geometry awareness is introduced to quantify the dynamic transferability from the pretrained representation to the related downstream tasks, which provides a panoramic view of the step-by-step learning process. The pairwise distances between these downstream tasks are also calculated, and a strong correlation between the inter-task feature space distributions and adaptability is observed.

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model.

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.

How Perceived Sensory Dimensions of Forest Park Are Associated with Stress Restoration in Beijing?

A growing number of studies suggest that the perceived sensory dimensions (PSDs) of green space are associated with stress restoration offered by restorative environment. However, there is little known about PSDs and stress restoration as well as their relationship to forest park. To fill this gap, an on-site questionnaire survey was conducted in three forest parks in Beijing, as a result of which a total number of 432 completed responses were collected and analyzed. The mean values of PSDs were used to represent PSDs of forest park. Using independent sample t-test and ANOVA, this study analyzed the individual characteristics that affected PSDs and stress restoration. Linear mixed model was used to identify the relationship between PSDs and stress restoration of forest park, which took into account the interactions of stress level and PSDs. The results showed that: (1) the perceived degree of PSDs in forest park from strong to weak was Serene, Space, Nature, Rich in species, Prospect, Refuge, Social and Culture, which varied with visitors' gender, age, level of stress, visit frequency, activity intensity, visit duration and commuting time; (2) in PSDs, Refuge, Serene, Social and Prospect had significantly positive effects on the stress restoration of forest parks (3) there was no significant difference in the effect of the eight PSDs on the stress restoration between different stress groups; (4) stress restoration was influenced by visitors' gender, age, visit frequency and visit duration. These findings can offer references for managers to improve the health benefits of forest park for visitors, and can enrich the knowledge about PSDs and stress restoration.

The Relationship between the Restorative Perception of the Environment and the Physiological and Psychological Effects of Different Types of Forests on University Students.

Short-term exposure to a forest environment is beneficial to human physiological and psychological health. However, there is little known about the relationship between the restorative perception of environment and physiological and psychological restoration achieved by experiencing the forest environment. This study evaluated the relationship between the restorative perception of different types of forests and human physiological and psychological effects. A sample of 30 young adult students from Beijing Forestry University was exposed to coniferous, deciduous, and mixed forests as well as an urban site. Restorative perception of the environment was measured using the PRS questionnaire. Restorative effects were measured using physiological indicators (blood pressure and heart rate) and three psychological questionnaires (Restorative Outcome Scale; Subjective Vitality Scale; Warwick-Edinburgh Mental Well-being Scale). The results demonstrated the following: (1) There were significant differences in the perceived restorative power of the three types of forests, with the highest level in the mixed forest, followed by the coniferous forest and the deciduous forest. (2) All types of forests were beneficial to physiological and psychological restoration. The mixed forest had the greatest effect in lowering blood pressure and heart rate as well as increasing vitality, while the coniferous forest had the strongest increases in psychological restoration and positive mental health. (3) The level of perceived restorative power of environment was positively related to the physiological and psychological restoration. These findings provide practical evidence for forest therapy that can maximize the restorative potential of forests.

A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe.

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here, we propose a novel framework, named AIMEE, integrating AI model and enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2), which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value <3 µM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE.