Polyporaceae is an important fungal family that has been a source of natural products with a range of pharmaceutical activities in China. In our previous study, two polysaccharides, PCWPW and PCWPS, with significant antioxidant and antidepressant activity were obtained from Poria cocos. In this study, we evaluated their potential molecular mechanisms in the immunomodulation of macrophages. PCWPW and PCWPS were characterized by GC–MS analysis to contain 1,3-linked Glcp. ELISA assays results demonstrated that the secretion of TNF-α was significantly enhanced by PCWPW/PCWPS. RNA-seq data demonstrated that PCWPS treatment modulated the expression of immune-related genes in macrophages, which was further confirmed by RT-qPCR assays. The activation of TNF-α secretion was found to be mannose receptor (MR) dependent and suppressed by MR inhibitor pretreatment. Moreover, the amount of TNF-α cytokine secretion in PCWPW/PCWPS-induced RAW264.7 cells was decreased when pretreated with NF-κB or MAPK signaling pathway inhibitors. Collectively, our results suggested that PCWPW and PCWPS possessed immunomodulatory activity that regulates TNF-α expression through the NF-κB/MAPK signaling pathway by binding to mannose receptors. Therefore, PCWPW and PCWPS isolated from Poria cocos have potential as drug candidates for immune-related disease treatment.

The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput compound-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between herb/disease inducible gene expression profiles and specific gene signatures for 8233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal component—direct target—indirect target by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.

A natural α-1,6-glucan named BBWPW was identified from black beans. Cell viability assay showed that BBWPW inhibited the proliferation of different cancer cells, especially HeLa cells. Flow cytometry analysis indicated that BBWPW suppressed the HeLa cell cycle in the G2/M phase. Consistently, RT-PCR experiments displayed that BBWPW significantly impacts the expression of four marker genes related to the G2/M phase, including p21, CDK1, Cyclin B1, and Survivin. To explore the molecular mechanism of BBWPW to induce cell cycle arrest, a transcriptome-based target inference approach was utilized to predict the potential upstream pathways of BBWPW and it was found that the PI3K-Akt and MAPK signal pathways had the potential to mediate the effects of BBWPW on the cell cycle. Further experimental tests confirmed that BBWPW increased the expression of BAD and AKT and decreased the expression of mTOR and MKK3. These results suggested that BBWPW could regulate the PI3K-Akt and MAPK pathways to induce cell cycle arrest and ultimately inhibit the proliferation of HeLa cells, providing the potential of the black bean glucan to be a natural anticancer drug.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show that TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference (RNAi), clustered regularly interspaced short palindromic repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.