Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus.

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

Soil Microbiomes and their Arsenic Functional Genes in Chronically High-Arsenic Contaminated Soils.

Microbial arsenic transformations play essential roles in controlling pollution and ameliorating risk. This study combined high-throughput sequencing and PCR-based approaches targeting both the 16 S rRNA and arsenic functional genes to investigate the temporal and spatial dynamics of the soil microbiomes impacted by high arsenic contamination (9.13 to 911.88 mg/kg) and to investigate the diversity and abundance of arsenic functional genes in soils influenced by an arsenic gradient. The results showed that the soil microbiomes were relatively consistent and mainly composed of Actinobacteria (uncultured Gaiellales and an unknown_67 - 14 bacterium), Proteobacteria, Firmicutes (particularly, Bacillus), Chloroflexi, and Acidobacteria (unknown_Subgroup_6). Although a range of arsenic functional genes (e.g., arsM, arsC, arrA, and aioA) were identified by shotgun metagenomics, only the arsM gene was detected by the PCR-based method. The relative abundance of the arsM gene accounted for 0.20%-1.57% of the total microbial abundance. Combining all analyses, arsenic methylation mediated by the arsM gene was proposed to be a key process involved in the arsenic biogeochemical cycle and mitigation of arsenic toxicity. This study advances our knowledge about arsenic mechanisms over the long-term in highly contaminated soils.

Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database.

The mammalian target of rapamycin (mTOR) is a protein kinase of the PI3K/Akt signaling pathway that regulates cell growth and division and is an attractive target for cancer therapy. Many reports on finding alternative mTOR inhibitors available in a database contain a mixture of active compound data with different mechanisms, which results in an increased complexity for training the machine learning models based on the chemical features of active compounds. In this study, a deep learning model supported by principal component analysis (PCA) and structural methods was used to search for an alternative mTOR inhibitor from mushrooms. The mTORC1 active compound data set from the PubChem database was first filtered for only the compounds resided near the first-generation inhibitors (rapalogs) within the first two PCA coordinates of chemical features. A deep learning model trained by the filtered data set captured the main characteristics of rapalogs and displayed the importance of steroid cores. After that, another layer of virtual screening by molecular docking calculations was performed on ternary complexes of FKBP12-FRB domains and six compound candidates with high "active" probability scores predicted by the deep learning models. Finally, all-atom molecular dynamics simulations and MMPBSA binding energy analysis were performed on two selected candidates in comparison to rapamycin, which confirmed the importance of ring groups and steroid cores for interaction networks. Trihydroxysterol from Lentinus polychrous Lev. was predicted as an interesting candidate due to the small but effective interaction network that facilitated FKBP12-FRB interactions and further stabilized the ternary complex.

Effects of Arsenic and Iron on the Community and Abundance of Arsenite-Oxidizing Bacteria in an Arsenic-Affected Groundwater Aquifer.

Arsenic (As) contamination of groundwater aquifers is a global environmental problem, especially in South and Southeast Asian regions, and poses a risk to human health. Arsenite-oxidizing bacteria that transform As(III) to less toxic As(V) can be potentially used as a groundwater As remediation strategy. This study aimed to examine the community and abundance of arsenite-oxidizing bacteria in groundwater with various As concentrations from Rayong Province, Thailand using PCR-cloning-sequencing and quantitative PCR (qPCR) of catalytic subunit of arsenite oxidase gene (aioA). Key factors influencing their community and abundance were also identified. The results demonstrated that arsenite-oxidizing bacteria retrieved from groundwater were phylogenetically related to Betaproteobacteria and Alphaproteobacteria. The aioA gene abundances ranged from 8.6 × 101 to 1.1 × 104 copies per ng of genomic DNA, accounting for 0.16-1.37% of the total 16S rRNA bacterial gene copies. Although the abundance of arsenite-oxidizing bacteria in groundwater was low, groundwater with As(III) dominance likely promoted their abundance which possibly played an important role in chemolithoautotrophic oxidation of As(III) to As(V). Fe and As(III) were the major environmental factors influencing the community and abundance of arsenite-oxidizing bacteria. The knowledge gained from this study can be used to further contribute to the development of bioremediation strategies for As removal from groundwater resources.

A model of infection in honeybee colonies with social immunity.

Honeybees (Apis mellifera) play a significant role in the pollination of various food crops and plants. In the past decades, honeybee management has been challenged with increased pathogen and environmental pressure associating with increased beekeeping costs, having a marked economic impact on the beekeeping industry. Pathogens have been identified as a contributing cause of colony losses. Evidence suggested a possible route of pathogen transmission among bees via oral-oral contacts through trophallaxis. Here we propose a model that describes the transmission of an infection within a colony when bee members engage in the trophallactic activity to distribute nectar. In addition, we examine two important features of social immunity, defined as collective disease defenses organized by honeybee society. First, our model considers the social segregation of worker bees. The segregation limits foragers, which are highly exposed to pathogens during foraging outside the nest, from interacting with bees residing in the inner parts of the nest. Second, our model includes a hygienic response, by which healthy nurse bees exterminate infected bees to mitigate horizontal transmission of the infection to other bee members. We propose that the social segregation forms the first line of defense in reducing the uptake of pathogens into the colony. If the first line of defense fails, the hygienic behavior provides a second mechanism in preventing disease spread. Our study identifies the rate of egg-laying as a critical factor in maintaining the colony's health against an infection. We propose that winter conditions which cease or reduce the egg-laying activity combined with an infection in early spring can compromise the social immunity defenses and potentially cause colony losses.

Microbial community structure in aquifers associated with arsenic: analysis of 16S rRNA and arsenite oxidase genes.

The microbiomes of deep and shallow aquifers located in an agricultural area, impacted by an old tin mine, were explored to understand spatial variation in microbial community structures and identify environmental factors influencing microbial distribution patterns through the analysis of 16S rRNA and aioA genes. Although Proteobacteria, Cyanobacteria, Actinobacteria, Patescibacteria, Bacteroidetes, and Epsilonbacteraeota were widespread across the analyzed aquifers, the dominant taxa found in each aquifer were unique. The co-dominance of Burkholderiaceae and Gallionellaceae potentially controlled arsenic immobilization in the aquifers. Analysis of the aioA gene suggested that arsenite-oxidizing bacteria phylogenetically associated with Alpha-, Beta-, and Gamma proteobacteria were present at low abundance (0.85 to 37.13%) and were more prevalent in shallow aquifers and surface water. The concentrations of dissolved oxygen and total phosphorus significantly governed the microbiomes analyzed in this study, while the combination of NO3 --N concentration and oxidation-reduction potential significantly influenced the diversity and abundance of arsenite-oxidizing bacteria in the aquifers. The knowledge of microbial community structures and functions in relation to deep and shallow aquifers is required for further development of sustainable aquifer management.

Decision-making in honeybee swarms based on quality and distance information of candidate nest sites.

In the nest-site selection process of honeybee swarms, an individual bee performs a waggle dance to communicate information about direction, quality, and distance of a discovered site to other bees at the swarm. Initially, different groups of bees dance to represent different potential sites, but eventually the swarm usually reaches an agreement for only one site. Here, we model the nest-site selection process in honeybee swarms of Apis mellifera and show how the swarms make adaptive decisions based on a trade-off between the quality and distance to candidate nest sites. We use bifurcation analysis and stochastic simulations to reveal that the swarm's site distance preference is moderate>near>far when the swarms choose between low quality sites. However, the distance preference becomes near>moderate>far when the swarms choose between high quality sites. Our simulations also indicate that swarms with large population size prefer nearer sites and, in addition, are more adaptive at making decisions based on available information compared to swarms with smaller population size.