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IntroductionMany studies have identified stand age and soil microbial communities as key factors influencing soil respiration (Rs). However, the effects of stand age on Rs and soil microbial communities throughout the growth cycle of poplar (Populus euramevicana cv.‘I-214’) plantations remain unclear.MethodsIn this study, we adopted a spatial approach instead of a temporal one to investigate Rs and soil microbial communities in poplar plantations of 15 different ages (1–15 years old).ResultsThe results showed that Rs exhibited clear seasonal dynamics, with the highest rates observed in the first year of stand age (1-year-old). As stand age increased, Rs showed a significant decreasing trend. We further identified r-selected microbial communities (copiotrophic species) as key biological factors influencing the decline in Rs with increasing stand age. Other abiotic factors, such as soil temperature (ST), pH, soil organic carbon (SOC), nitrate nitrogen (NO3−-N), and the C/N ratio of plant litter (Litter C/N), were also significantly correlated with Rs. Increased stand age promoted fungal community diversity but suppressed bacterial community diversity. Bacterial and fungal communities differed significantly in abundance, composition, and function, with the Litter C/N ratio being a key variable affected by microbial community changes.ConclusionThis study provides crucial empirical evidence on how stand age affects Rs, highlighting the connection between microbial community assemblages, their trophic strategies, and Rs over the growth cycle of poplar plantations.

Abstract Background Recognition of enhancer–promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don’t take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance. Results Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively. Conclusions This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.

Informative molecular representation is a vital prerequisite in artificial intelligence-driven de novo drug discovery, however, mapping the pharmacophoric information is underexploited by the atom-level based molecular graph representation. Here, the authors design a multi-level based Pharmacophoric-constrained heterogeneous graph transformer (PharmHGT) to better capture the pharmacophore structure and chemical information.

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.

Breast cancer is considered the most prevalent malignancy due to its high incidence rate, recurrence, and metastasis in women that makes it one of the deadliest cancers. The current study aimed to predict the genes associated with the recurrence and metastasis of breast cancer and to validate their effect on MDA-MB-231 cells. Through the bioinformatics analysis, the transcription factor 7 cofactor (MLLT11) as the target gene was obtained. MLLT11-specific siRNA was synthesized and transfected into MDA-MB-231 cells. The results demonstrated that the siRNA significantly reduced the MLLT11 mRNA levels. Moreover, cell migration and invasion, as well as the protein levels of phosphatidylinositol 3-kinase (PI3K), AKT, matrix metalloproteinase (MMP) 2, and MMP9, were significantly lower in the groups treated with siRNA while the apoptosis was augmented. Collectively, MLLT11 siRNA elicited ameliorative properties on breast cancer cells, possibly via the inhibition of the PI3K/AKT signaling pathway.

IntroductionA number of evidences have proved that “Nostoc commune” Vauch can improve human immunity and prevent diseases, however, the specific mechanism remains unclear. The biological activity of the main protein component of “Nostoc commune” Vauch extracellular matrix– a water-stress protein (WSP) still needs to be elucidated.MethodsIn our study, we validated the role of WSP in gastric cancer metastasis at the cellular level, the organoid level and in mouse models, and also studied the role of EGFRVIII and downstream signaling molecules after WSP treatment.ResultsWe found that WSP can significantly inhibit the metastasis of gastric cancer cells. Interestingly, we found that the anti-metastasis ability of WSP on gastric cancer was related to membrane protein receptor EGFRVIII, which was realized by inhibiting the downstream EGFRVIII signaling pathway. In terms of mechanism, WSP can inhibit the downstream EGFRVIII signaling pathway Akt-PI3K and further inhibit the secretion of cancer-related metastasis proteins such as MMP2 and MMP9, thus, significantly affecting the metastasis of gastric cancer cells.DiscussionGiven the anticancer properties of WSP, drug developers and manufacturers can further develop protein drugs for cancer patients using protein engineering techniques based on the properties of WSP.

Abstract The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory‐related studies, thus hindering the possibility of supervised learning. A spatial‐temporal pre‐training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self‐supervised learning tasks: atom‐level prompt‐based denoising generative task and conformation‐level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time‐dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task‐specific fine‐tuning. A huge improvement from current state‐of‐the‐art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision‐recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.

The biodegradation of tar-rich coal in the Ordos Basin was carried out by Bacillus licheniformis (B. licheniformis) under actions of four kinds of surfactants, namely, a biological surfactant (Rh), a nonionic surfactant (Triton X-100), an anionic surfactant (LAS), and a cationic surfactant (DTAB). The biodegradation rates under the actions of Triton X-100, LAS, Rh, DTAB, and the control group (without surfactant) were 59.8%, 54.3%, 51.6%, 17.3%, and 43.5%, respectively. The biodegradation mechanism was studied by examining the influences of surfactants on coal samples, bacteria, and degradation products in the degradation process. The results demonstrated that Rh, Triton X-100, and LAS could promote bacterial growth, while DTAB had the opposite effect. Four surfactants all increased the cell surface hydrophobicity (CSH) of B. licheniformis, and Triton X-100 demonstrated the most significant promotion of CSH. The order of improvement in microbial cell permeability by surfactants was DTAB > TritonX-100 > LAS > Rh > control group. In the presence of four surfactants, Triton X-100 exhibited the best hydrophilicity improvement for oxidized coal. Overall, among the four surfactants, Triton X-100 ranked first in enhancing the CSH of bacteria and the hydrophilicity of oxidized coal and second in improving microbial cell permeability; thus, Triton X-100 was the most suitable surfactant for promoting B. licheniformis’s biodegradation of tar-rich coal. The GC-MS showed that, after the action of Triton X-100, the amount of the identified degradation compounds in the toluene extract of the liquid product decreased by 16 compared to the control group, the amount of dichloromethane extract decreased by 6, and the amount of ethyl acetate extract increased by 6. Simultaneously, the contents of alkanes in the extracts of toluene and dichloromethane decreased, lipids increased, and ethyl acetate extract exhibited little change. The FTIR analysis of the coal sample suggested that, under the action of Triton X-100, compared to oxidized coal, the Har/H and A(CH2)/A(CH3) of the remaining coal decreased by 0.07 and 1.38, respectively, indicating that Triton X-100 enhanced the degradation of aromatic and aliphatic structures of oxidized coal. Therefore, adding a suitable surfactant can promote the biodegradation of tar-rich coal and enrich its degradation product.

Abstract Background Microglia are key regulators of the inflammatory response in the brain. Adenosine in RNAs can be converted to m6A (N6-methyladenosine), which regulates RNA metabolism and functions as a key epitranscriptomic modification. The m6A modification pattern and m6A-related signatures under pro-inflammatory and anti-inflammatory conditions of microglia remain unclear. Methods Primary rat microglia were differentiated into pro-inflammatory M1-like (M1-L), anti-inflammatory M2-like (M2-L), and resting, unstimulated (M0-L) phenotypes. m6A mRNA and lncRNA epitranscriptomic microarray analyses were performed, and pathway analysis was conducted to understand the functional implications of m6A methylation in mRNAs and lncRNAs. The m6A methylation level and gene expression of mRNAs and lncRNAs were subsequently verified by m6A Me-RIP and qRT-PCR. Results A total of 1588 mRNAs and 340 lncRNAs, 315 mRNAs and 38 lncRNAs, and 521 mRNAs and 244 lncRNAs were differentially m6A methylated between M1-L and M0-L (M1-L/M0-L), M2-L and M0-L (M2-L/M0-L), M2-L and M1-L (M2-L/M1-L), respectively. Furthermore, 4902 mRNAs, 4676 mRNAs, and 5095 mRNAs were identified distinctively expressed in M1-L/M0-L, M2-L/M0-L, and M2-L/M1-L, respectively. Pathway analysis of differentially m6A methylated mRNAs and lncRNAs in M1-L/M0-L identified immune system, signal transduction, and protein degradation processes. In contrast, the distinct m6A methylated mRNAs in M2-L/M0-L were involved in genetic information processing, metabolism, cellular processes, and neurodegenerative disease-related pathways. We validated m6A methylation and the expression levels of five mRNAs and five lncRNAs, which were involved in upregulated pathways in M1-L/M0-L, and five mRNAs involved in upregulated pathways in M2-L/M0-L. Conclusions These findings identify a distinct m6A epitranscriptome in microglia, and which may serve as novel and useful regulator during pro-inflammatory and anti-inflammatory response of microglia.

Breast cancer (BC) remains the most prevalent malignancy due to its incidence rate, recurrence, and metastasis in women. Conventional strategies of cancer detection– mammography and tissue biopsy lack the capacity to detect the complete cancer genomic landscape. Besides, they often give false- positive or negative results. The presence of this and other disadvantages such as invasiveness, high-cost, and side effects necessitates developing new strategies to overcome the BC burden. Liquid biopsy (LB) has been brought to the fore owing to its early detection, screening, prognosis, simplicity of the technique, and efficient monitoring. Remarkably, microRNAs (miRNAs)– gene expression regulators seem to play a major role as biomarkers detected in the samples of LB. Particularly, miR-21 and miR-155 among other possible candidates seem to serve as favorable biomarkers in the diagnosis and prognosis of BC. Hence, this review will assess the potential utility of miRNAs as biomarkers and will highlight certain promising candidates for the LB approach in the diagnosis and management of BC that may optimize the patient outcome.

Abstract Background Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington’s disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins. Results In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses “support vector machine” subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew’s correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools. Conclusion The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/