A novel network pharmacology strategy to decode mechanism of Wuling Powder in treating liver cirrhosis

BACKGROUND

Liver cirrhosis is a chronic liver disease with hepatocyte necrosis and lesion. As one of the TCM formulas Wuling Powder (WLP) is widely used in the treatment of liver cirrhosis. However, it's key functional components and action mechanism still remain unclear. We attempted to explore the Key Group of Effective Components (KGEC) of WLP in the treatment of Liver cirrhosis through integrative pharmacology combined with experiments.

METHODS

The components and potential target genes of WLP were extracted from published databases. A novel node importance calculation model considering both node control force and node bridging force is designed to construct the Function Response Space (FRS) and obtain key effector proteins. The genetic knapsack algorithm was employed to select KGEC. The effectiveness and reliability of KGEC were evaluated at the functional level by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the effectiveness and potential mechanism of KGEC were confirmed by CCK-8, qPCR and Western blot.

RESULTS

940 effective proteins were obtained in FRS. KEGG pathways and GO terms enrichments analysis suggested that effective proteins well reflect liver cirrhosis characteristics at the functional level. 29 components of WLP were defined as KGEC, which covered 100% of the targets of the effective proteins. Additionally, the pathways enriched for the KGEC targets accounted for 83.33% of the shared genes between the targets and the pathogenic genes enrichment pathways. Three components scopoletin, caryophyllene oxide, and hydroxyzinamic acid from KGEC were selected for in vivo verification. The qPCR results demonstrated that all three components significantly reduced the mRNA levels of COL1A1 in TGF-β1-induced liver cirrhosis model. Furthermore, the Western blot assay indicated that these components acted synergistically to target the NF-κB, AMPK/p38, cAMP, and PI3K/AKT pathways, thus inhibiting the progression of liver cirrhosis.

CONCLUSION

In summary, we have developed a new model that reveals the key components and potential mechanisms of WLP for the treatment of liver cirrhosis. This model provides a reference for the secondary development of WLP and offers a methodological strategy for studying TCM formulas.

Injectable Ozone-Rich Nanocomposite Hydrogel Loaded with D-Mannose for Anti-Inflammatory and Cartilage Protection in Osteoarthritis Treatment

Osteoarthritis (OA) is a dynamic condition characterized by cartilage damage and synovial inflammation. Ozone (O3 ) shows potential therapeutic effects owing to its anti-inflammatory properties; however, its high reactivity and short half-life substantially limit its effectiveness in OA treatment. In this study, an ozone-rich thermosensitive nanocomposite hydrogel loaded with D-mannose is developed for OA treatment. Briefly, O3 is encapsulated in nanoparticles (NPs) composed of perfluorotributylamine and fluorinated hyaluronic acid to improve its stability. Next, D-mannose is conjugated with α-amino of the hydroxypropyl chitin (HPCH) via Schiff base to prepare MHPCH. These nanoparticles are encapsulated in MHPCH to produce O3 NPs@MHPCH. In vitro cell experiments demonstrate that the O3 NPs@MHPCH treatment significantly reduced VEGF and inflammation levels, accompanied by a decrease in inflammatory factors such as IL-1β, IL-6, TNF-α, and iNOS. Furthermore, O3 NPs@MHPCH promotes the expression of collagen II and aggrecan and stimulates chondrocyte proliferation. Additionally, in vivo studies show that O3 NPs@MHPCH significantly alleviated OA by reducing synovial inflammation, cartilage destruction, and subchondral bone remodeling. O3 NPs@MHPCH offers a promising option for improving the efficacy of O3 therapy and reducing the risk of synovial inflammation and cartilage degeneration in OA.

A highly stretchable, adhesive, and antibacterial hydrogel with chitosan and tobramycin as dynamic cross-linkers for treating the infected diabetic wound

Diabetic wounds pose a significant challenge due to their susceptibility to bacterial infection in a high-glucose environment, which impedes the wound healing process. To address this issue, there is a pressing need to develop suitable hydrogels that can promote the regeneration of diabetic wounds in clinical practice. In this study, we designed and fabricated a highly stretchable, adhesive, transparent, and antibacterial hydrogel through a one-pot radical polymerization of N-[Tris (hydroxymethyl) methyl] acrylamide (THMA) and acrylic acid (AA), and with chitosan and the antibiotic tobramycin as the dynamic physical crosslinkers. The copolymer contains a large number of carboxyl and hydroxyl groups, which can form an interpenetrating network structure with chitosan and tobramycin through multiple dynamic non-covalent bonds. This hydrogel exhibited over 1600 % elongation through an energy dissipation mechanism and strong adhesion to various surfaces without any chemical reaction. In vivo, studies conducted on a staphylococcus aureus-infected full-thickness diabetic skin wound model demonstrated that the hydrogel loaded with tobramycin as one of the crosslinkers had a long-lasting antibacterial activity and effectively accelerated wound healing. Therefore, the antibiotic-loaded adhesive hydrogel we proposed holds great promise as a treatment for bacteria-infected diabetic wounds.

Screening of the key response component groups and mechanism verification of Huangqi-Guizhi-Wuwu-Decoction in treating rheumatoid arthritis based on a novel computational pharmacological model

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by the destruction of synovial tissue and articular cartilage. Huangqi-Guizhi-Wuwu-Decoction (HGWD), a formula of Traditional Chinese Medicine (TCM), has shown promising clinical efficacy in the treatment of RA. However, the synergistic effects of key response components group (KRCG) in the treatment of RA have not been well studied.

METHODS

The components and potential targets of HGWD were extracted from published databases. A novel node influence calculation model that considers both the node control force and node bridging force was designed to construct the core response space (CRS) and obtain key effector proteins. An increasing coverage coefficient (ICC) model was employed to select the KRCG. The effectiveness and potential mechanism of action of KRCG were confirmed using CCK-8, qPCR, and western blotting.

RESULTS

A total of 796 key effector proteins were identified in CRS. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses confirmed their effectiveness and reliability. In addition, 59 components were defined as KRCG, which contributed to 85.05% of the target coverage of effective proteins. Of these, 677 targets were considered key reaction proteins, and their enriched KEGG pathways accounted for 84.89% of the pathogenic genes and 87.94% of the target genes. Finally, four components (moupinamide, 6-Paradol, hydrocinnamic acid, and protocatechuic acid) were shown to inhibit the inflammatory response in RA by synergistically targeting the cAMP, PI3K-Akt, and HIF-1α pathways.

CONCLUSIONS

We have introduced a novel model that aims to optimize and analyze the mechanisms behind herbal formulas. The model revealed the KRCG of HGWD for the treatment of RA and proposed that KRCG inhibits the inflammatory response by synergistically targeting cAMP, PI3K-Akt, and HIF-1α pathways. Overall, the novel model is plausible and reliable, offering a valuable reference for the secondary development of herbal formulas.

System pharmacology-based determination of the functional components and mechanisms in chronic heart failure treatment: an example of Zhenwu decoction

Chronic heart failure (CHF) is the primary cause of death among patients with cardiovascular diseases, representing the advanced stage in the development of several cardiovascular conditions. Zhenwu decoction (ZWD) has gained widespread recognition as an efficacious remedy for CHF due to its potent therapeutic properties and absence of adverse effects. Nevertheless, the precise molecular mechanisms underlying its actions remain elusive. This study endeavors to unravel the intricate pharmacological underpinnings of five herbs within ZWD concerning CHF through an integrated approach. Initially, pertinent data regarding ZWD and CHF were compiled from established databases, forming the foundation for constructing an intricate network of active component-target interactions. Subsequently, a pioneering method for evaluating node significance was formulated, culminating in the creation of core functional association space (CFAS). To discern vital components, a novel dynamic programming algorithm was devised and used to determine the core component group (CCG) within the CFAS. Enrichment analysis of the CCG targets unveiled the potential coordinated molecular mechanisms of ZWD, illuminating its capacity to ameliorate CHF by modulating genes and related signaling pathways involved in pathological remodeling. Notable pathways encompass PI3K-Akt, diabetic cardiomyopathy, cAMP and MAPK signaling. Concluding the computational analyses, in vitro experiments were executed to assess the effects of vanillic acid, paradol, 10-gingerol and methyl cinnamate. Remarkably, these compounds demonstrated efficacy in reducing the production of ANP and BNP within isoprenaline-induced AC 16 cells, further validating their potential therapeutic utility. This investigation underscores the efficacy of the proposed model in enhancing the precision and reliability of CCG selection within ZWD, thereby presenting a novel avenue for mechanistic inquiries, compound refinement and the secondary development of TCM herbs.

Corrigendum: An integrative pharmacology model for decoding the underlying Therapeutic Mechanisms of Ermiao Powder for Rheumatoid arthritis

[This corrects the article DOI: 10.3389/fphar.2022.801350.].

Screening of key functional components of Taohong Siwu Decoction on ischemic stroke treatment based on multiobjective optimization approach and experimental validation

BACKGROUND

Taohong Siwu Decoction (THSWD) is a widely used traditional Chinese medicine (TCM) prescription in the treatment of ischemic stroke. There are thousands of chemical components in THSWD. However, the key functional components are still poorly understood. This study aimed to construct a mathematical model for screening of active ingredients in TCM prescriptions and apply it to THSWD on ischemic stroke.

METHODS

Botanical drugs and compounds in THSWD were acquired from multiple public TCM databases. All compounds were initially screened by ADMET properties. SEA, HitPick, and Swiss Target Prediction were used for target prediction of the filtered compounds. Ischemic stroke pathological genes were acquired from the DisGeNet database. The compound-target-pathogenic gene (C-T-P) network of THSWD was constructed and then optimized using the multiobjective optimization (MOO) algorithm. We calculated the cumulative target coverage score of each compound and screened the top compounds with 90% coverage. Finally, verification of the neuroprotective effect of these compounds was performed with the oxygen-glucose deprivation and reoxygenation (OGD/R) model.

RESULTS

The optimized C-T-P network contains 167 compounds, 1,467 predicted targets, and 1,758 stroke pathological genes. And the MOO model showed better optimization performance than the degree model, closeness model, and betweenness model. Then, we calculated the cumulative target coverage score of the above compounds, and the cumulative effect of 39 compounds on pathogenic genes reached 90% of all compounds. Furthermore, the experimental results showed that decanoic acid, butylphthalide, chrysophanol, and sinapic acid significantly increased cell viability. Finally, the docking results showed the binding modes of these four compounds and their target proteins.

CONCLUSION

This study provides a methodological reference for the screening of potential therapeutic compounds of TCM. In addition, decanoic acid and sinapic acid screened from THSWD were found having potential neuroprotective effects first and verified with cell experiments, however, further in vitro and in vivo studies are needed to explore the precise mechanisms involved.

Decoding the key compounds and mechanism of Shashen Maidong decoction in the treatment of lung cancer

BACKGROUND

Lung cancer is a malignant tumour with the fastest increase in morbidity and mortality around the world. The clinical treatments available have significant side effects, thus it is desirable to identify alternative modalities to treat lung cancer. Shashen Maidong decoction (SMD) is a commonly used traditional Chinese medicine (TCM) formula for treating lung cancer in the clinic. While the key functional components (KFC) and the underlying mechanisms of SMD treating lung cancer are still unclear.

METHODS

We propose a new integrated pharmacology model, which combines a novel node-importance calculation method and the contribution decision rate (CDR) model, to identify the KFC of SMD and to deduce their mechanisms in the treatment of lung cancer.

RESULTS

The enriched effective Gene Ontology (GO) terms selected from our proposed node importance detection method could cover 97.66% of enriched GO terms of reference targets. After calculating CDR of active components in key functional network, the first 82 components covered 90.25% of the network information, which were defined as KFC. 82 KFC were subjected to functional analysis and experimental validation. 5-40 μM protocatechuic acid, 100-400 μM paeonol or caffeic acid exerted significant inhibitory activity on the proliferation of A549 cells. The results show that KFC play an important therapeutic role in the treatment of lung cancer by targeting Ras, AKT, IKK, Raf1, MEK, and NF-κB in the PI3K-Akt, MAPK, SCLC, and NSCLC signaling pathways active in lung cancer.

CONCLUSIONS

This study provides a methodological reference for the optimization and secondary development of TCM formulas. The strategy proposed in this study can be used to identify key compounds in the complex network and provides an operable test range for subsequent experimental verification, which greatly reduces the experimental workload.

A Metabolic Driven Bio-Responsive Hydrogel Loading Psoralen for Therapy of Rheumatoid Arthritis

Overexpressed matrix metalloproteinases, hypoxia microenvironment, and metabolic abnormality are important pathological signs of rheumatoid arthritis (RA). Designing a delivery carrier according to the pathological characteristics of RA that can control drug release in response to disease severity may be a promising treatment strategy. Psoralen is the main active ingredient isolated from Psoralea corylifolia L. and possesses excellent anti-inflammatory activities as well as improving bone homeostasis. However, the specific underlying mechanisms, particularly the possible relationships between the anti-RA effects of psoralen and related metabolic network, remain largely unexplored. Furthermore, psoralen shows systemic side effects and has unsatisfactory solubility. Therefore, it is desirable to develop a novel delivery system to maximize psoralen's therapeutic effect. In this study, a self-assembled degradable hydrogel platform is developed that delivers psoralen and calcium peroxide to arthritic joints and controls the release of psoralen and oxygen according to inflammatory stimulation, to regulate homeostasis and the metabolic disorder of the anoxic arthritic microenvironment. Therefore, the hydrogel drug delivery system based on the responsiveness of the inflammatory microenvironment and regulation of metabolism provides a new therapeutic strategy for RA treatment.

Decoding the underlying mechanisms of Di-Tan-Decoction in treating intracerebral hemorrhage based on network pharmacology

BACKGROUND

Chinese medicine usually acts as "multi-ingredients, multi-targets and multi-pathways" on complex diseases, and these action modes reflect the coordination and integrity of the treatment process with traditional Chinese medicine (TCM). System pharmacology is developed based on the cross-disciplines of directional pharmacology, system biology, and mathematics, has the characteristics of integrity and synergy in the treatment process of TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Intracerebral Hemorrhage (ICH) is one of the leading causes of death in China, with the characteristics of high mortality and disability rate. Bring a significant burden on people and society. An increasing number of studies have shown that Chinese medicine prescriptions have good advantages in the treatment of ICH, and Ditan Decoction (DTT) is one of the commonly used prescriptions in the treatment of ICH. Modern pharmacological studies have shown that DTT may play a therapeutic role in treating ICH by inhibiting brain inflammation, abnormal oxidative stress reaction and reducing neurological damage, but the specific key ingredients and mechanism are still unclear.

METHODS

To solve this problem, we established PPI network based on the latest pathogenic gene data of ICH, and CT network based on ingredient and target data of DTT. Subsequently, we established optimization space based on PPI network and CT network, and constructed a new model for node importance calculation, and proposed a calculation method for PES score, thus calculating the functional core ingredients group (FCIG). These core functional groups may represent DTT therapy for ICH.

RESULTS

Based on the strategy, 44 ingredients were predicted as FCIG, results showed that 80.44% of the FCIG targets enriched pathways were coincided with the enriched pathways of pathogenic genes. Both the literature and molecular docking results confirm the therapeutic effect of FCIG on ICH via targeting MAPK signaling pathway and PI3K-Akt signaling pathway.

CONCLUSIONS

The FCIG obtained by our network pharmacology method can represent the effect of DTT in treating ICH. These results confirmed that our strategy of active ingredient group optimization and the mechanism inference could provide methodological reference for optimization and secondary development of TCM.

Corrigendum: Detecting key functional components group and speculating the potential mechanism of Xiao-Xu-Ming decoction in treating stroke

[This corrects the article DOI: 10.3389/fcell.2022.753425.].

Quantification of promoting efficiency and reducing toxicity of Traditional Chinese Medicine: A case study of the combination of Tripterygium wilfordii hook. f. and Lysimachia christinae hance in the treatment of lung cancer

Traditional Chinese medicine (TCM) usually acts in the form of compound prescriptions in the treatment of complex diseases. The herbs contained in each prescription have the dual nature of efficiency and toxicity due to their complex chemical component, and the principle of prescription is usually to increase efficiency and reduce toxicity. At present, the studies on prescriptions have mainly focused on the consideration of the material basis and possible mechanism of the action mode, but the quantitative research on the compatibility rule of increasing efficiency and reducing toxicity is still the tip of the iceberg. With the extensive application of computational pharmacology technology in the research of TCM prescriptions, it is possible to quantify the mechanism of synergism and toxicity reduction of the TCM formula. Currently, there are some classic drug pairs commonly used to treat complex diseases, such as Tripterygium wilfordii Hook. f. with Lysimachia christinae Hance for lung cancer, Aconitum carmichaelii Debeaux with Glycyrrhiza uralensis Fisch. in the treatment of coronary heart disease, but there is a lack of systematic quantitative analysis model and strategy to quantitatively study the compatibility rule and potential mechanism of synergism and toxicity reduction. To address this issue, we designed an integrated model which integrates matrix decomposition and shortest path propagation, taking into account both the crosstalk of the effective network and the propagation characteristics. With the integrated model strategy, we can quantitatively detect the possible mechanisms of synergism and attenuation of Tripterygium wilfordii Hook. f. and Lysimachia christinae Hance in the treatment of lung cancer. The results showed the compatibility of Tripterygium wilfordii Hook. f. and Lysimachia christinae Hance could increase the efficacy and decrease the toxicity of lung cancer treatment through MAPK pathway and PD-1 checkpoint pathway in lung cancer.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Mathematical algorithm–based identification of the functional components and mechanisms in depression treatment: An example of Danggui-Shaoyao-San

Depression, a complex epidemiological mental disorder, affects around 350 million people worldwide. Despite the availability of antidepressants based on monoamine hypothesis of depression, most patients suffer side effects from these drugs, including psychomotor impairment and dependence liability. Traditional Chinese medicine (TCM) is receiving more and more attention due to the advantages of high therapeutic performance and few side effects in depression treatment. However, complex multicomponents and multi-targets in TCM hinder our ability to identify the functional components and molecular mechanisms of its efficacy. In this study, we designed a novel strategy to capture the functional components and mechanisms of TCM based on a mathematical algorithm. To establish proof of principle, the TCM formula Danggui-Shaoyao-San (DSS), which possesses remarkable antidepressant effect but its functional components and mechanisms are unclear, is used as an example. According to the network motif detection algorithm, key core function motifs (CIM) of DSS in treating depression were captured, followed by a functional analysis and verification. The results demonstrated that 198 pathways were enriched by the target genes of the CIM, and 179 coincided with the enriched pathways of pathogenic genes, accounting for 90.40% of the gene enrichment pathway of the C-T network. Then the functional components group (FCG) comprising 40 components was traced from CIM based on the target coverage accumulation algorithm, after which the pathways enriched by the target genes of FCG were selected to elucidate the potential mechanisms of DSS in treating depression. Finally, the pivotal components in FCG of DSS and the related pathways were selected for experimental validation in vitro and in vivo. Our results indicated good accuracy of the proposed mathematical algorithm in sifting the FCG from the TCM formula, which provided a methodological reference for discovering functional components and interpreting molecular mechanisms of the TCM formula in treating complex diseases.

Computational Metabolomics Reveals the Potential Mechanism of Matrine Mediated Metabolic Network Against Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a complex issue in cancer treatment in the world at present. Matrine is the main active ingredient isolated from Sophora flavescens air and possesses excellent antitumor effects in HCC. However, the specific underlying mechanisms, especially the possible relationships between the anti-HCC effect of matrine and the related metabolic network of HCC, are not yet clear and need further clarification. In this study, an integrative metabolomic-based bioinformatics algorithm was designed to explore the underlying mechanism of matrine on HCC by regulating the metabolic network. Cell clone formation, invasion, and adhesion assay were utilized in HCC cells to evaluate the anti-HCC effect of matrine. A cell metabolomics approach based on LC-MS was used to obtain the differential metabolites and metabolic pathways regulated by matrine. The maximum activity contribution score model was developed and applied to calculate high contribution target genes of matrine, which could regulate a metabolic network based on the coexpression matrix of matrine-regulated metabolic genes and targets. Matrine significantly repressed the clone formation and invasion, enhanced cell–cell adhesion, and hampered cell matrix adhesion in SMMC-7721 cells. Metabolomics results suggested that matrine markedly regulated the abnormal metabolic network of HCC by regulating the level of choline, creatine, valine, spermidine, 4-oxoproline, D-(+)-maltose, L-(−)-methionine, L-phenylalanine, L-pyroglutamic acid, and pyridoxine, which are involved in D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, etc. Our proposed metabolomic-based bioinformatics algorithm showed that the regulating metabolic networks of matrine exhibit anti-HCC effects through acting on MMP7, ABCC1, PTGS1, etc. At last, MMP7 and its related target β-catenin were validated. Together, the metabolomic-based bioinformatics algorithm reveals the effects of the regulating metabolic networks of matrine in treating HCC relying on the unique characteristics of the multitargets and multipathways of traditional Chinese medicine.

Facile synthesis of chitosan-based nanogels through photo-crosslinking for doxorubicin delivery

Chitosan-based nanogels are effective carriers for drug delivery due to their biocompatibility and biodegradability. However, the chemically cross-linked nanogels usually require complicated procedures or tough conditions. Herein, we report a simple approach to generate chitosan-based nanogels by photo-crosslinking of poor solvent-induced nanoaggregates without requiring any emulsifying agent, catalyst, or external crosslinker. O-nitrobenzyl alcohol-modified carboxymethyl chitosan was synthesized and self-crosslinked into the nanogels in a mixed solution of ethanol and water under 365 nm light irradiation due to UV-induced primary amine and o-nitrobenzyl alcohol cyclization. The nanogels (CMC-NBA NPs) and lactobionic acid-decorated nanogels (LACMC-NBA NPs) displayed a uniform diameter (~200 nm) and excellent stability under physiological conditions. Notably, the nanogels exhibited a high loading content (~28 %) due to π-π stacking and electrostatic interactions between doxorubicin (DOX) and the carriers. These DOX-loaded nanogels showed rapid drug release under slightly acidic conditions. The cell and animal experiments confirmed that LACMC-NBA NPs increased cellular uptake, improved cytotoxicity in tumor cells, and enhanced growth inhibition in vivo than CMC-NBA NPs. Thus, these photo-crosslinked nanogels possess great potential for DOX delivery.

Detecting Key Functional Components Group and Speculating the Potential Mechanism of Xiao-Xu-Ming Decoction in Treating Stroke

Stroke is a cerebrovascular event with cerebral blood flow interruption which is caused by occlusion or bursting of cerebral vessels. At present, the main methods in treating stroke are surgical treatment, statins, and recombinant tissue-type plasminogen activator (rt-PA). Relatively, traditional Chinese medicine (TCM) has widely been used at clinical level in China and some countries in Asia. Xiao-Xu-Ming decoction (XXMD) is a classical and widely used prescription in treating stroke in China. However, the material basis of effect and the action principle of XXMD are still not clear. To solve this issue, we designed a new system pharmacology strategy that combined targets of XXMD and the pathogenetic genes of stroke to construct a functional response space (FRS). The effective proteins from this space were determined by using a novel node importance calculation method, and then the key functional components group (KFCG) that could mediate the effective proteins was selected based on the dynamic programming strategy. The results showed that enriched pathways of effective proteins selected from FRS could cover 99.10% of enriched pathways of reference targets, which were defined by overlapping of component targets and pathogenetic genes. Targets of optimized KFCG with 56 components can be enriched into 166 pathways that covered 80.43% of 138 pathways of 1,012 pathogenetic genes. A component potential effect score (PES) calculation model was constructed to calculate the comprehensive effective score of components in the components-targets-pathways (C-T-P) network of KFCGs, and showed that ferulic acid, zingerone, and vanillic acid had the highest PESs. Prediction and docking simulations show that these components can affect stroke synergistically through genes such as MEK, NFκB, and PI3K in PI3K-Akt, cAMP, and MAPK cascade signals. Finally, ferulic acid, zingerone, and vanillic acid were tested to be protective for PC12 cells and HT22 cells in increasing cell viabilities after oxygen and glucose deprivation (OGD). Our proposed strategy could improve the accuracy on decoding KFCGs of XXMD and provide a methodologic reference for the optimization, mechanism analysis, and secondary development of the formula in TCM.

Uncovering Hidden Mechanisms of Different Prescriptions Treatment for Osteoporosis via Novel Bioinformatics Model and Experiment Validation

Osteoporosis (OP) is a systemic disease susceptible to fracture due to the decline of bone mineral density and bone mass, the destruction of bone tissue microstructure, and increased bone fragility. At present, the treatments of OP mainly include bisphosphonates, hormone therapy, and RANKL antibody therapy. However, these treatments have observable side effects and cannot fundamentally improve bone metabolism. Currently, the prescription of herbal medicine and their derived proprietary Chinese medicines are playing increasingly important roles in the treatment of OP due to their significant curative effects and few side effects. Among these prescriptions, Gushukang Granules (GSK), Xianling Gubao Capsules (XLGB), and Er-xian Decoction (EXD) are widely employed at the clinic on therapy of OP, which also is in line with the compatibility principle of “different treatments for the same disease” in herbal medicine. However, at present, the functional interpretation of “different treatments for the same disease” in herbal medicine still lacks systematic quantitative research, especially on the detection of key component groups and mechanisms. To solve this problem, we designed a new bioinformatics model based on random walk, optimized programming, and information gain to analyze the components and targets to figure out the Functional Response Motifs (FRMs) of different prescriptions for the therapy of OP. The distribution of high relevance score, the number of reported evidence, and coverage of enriched pathways were performed to verify the precision and reliability of FRMs. At the same time, the information gain and target influence of each component was calculated, and the key component groups in all FRMs of each prescription were screened to speculate the potential action mode of different prescriptions on the same disease. Results show that the relevance score and the number of reported evidence of high reliable genes in FRMs were higher than those of the pathogenic genes of OP. Furthermore, the gene enrichment pathways in FRMs could cover 79.6, 81, and 79.5% of the gene enrichment pathways in the component-target (C-T) network. Functional pathway enrichment analysis showed that GSK, XLGB, and EXD all treat OP through osteoclast differentiation (hsa04380), calcium signaling pathway (hsa04020), MAPK signaling pathway (hsa04010), and PI3K-Akt signaling pathway (hsa04151). Combined with experiments, the key component groups and the mechanism of “different treatments for the same disease” in the three prescriptions and proprietary Chinese medicines were verified. This study provides methodological references for the optimization and mechanism speculation of Chinese medicine prescriptions and proprietary Chinese medicines.

An Integrative Pharmacology Model for Decoding the Underlying Therapeutic Mechanisms of Ermiao Powder for Rheumatoid Arthritis

As a systemic inflammatory arthritis disease, rheumatoid arthritis (RA) is complex and hereditary. Traditional Chinese medicine (TCM) has evident advantages in treating complex diseases, and a variety of TCM formulas have been reported that have effective treatment on RA. Clinical and pharmacological studies showed that Ermiao Powder, which consists of Phellodendron amurense Rupr. (PAR) and Atractylodes lancea (Thunb.) DC. (ALD), can be used in the treatment of RA. Currently, most studies focus on the anti-inflammatory mechanism of PAR and ALD and are less focused on their coordinated molecular mechanism. In this research, we established an integrative pharmacological strategy to explore the coordinated molecular mechanism of the two herbs of Ermiao Powder in treating RA. To explore the potential coordinated mechanism of PAR and ALD, we firstly developed a novel mathematical model to calculate the contribution score of 126 active components and 85 active components, which contributed 90% of the total contribution scores that were retained to construct the coordinated functional space. Then, the knapsack algorithm was applied to identify the core coordinated functional components from the 85 active components. Finally, we obtained the potential coordinated functional components group (CFCG) with 37 components, including wogonin, paeonol, ethyl caffeate, and magnoflorine. Also, functional enrichment analysis was performed on the targets of CFCG to explore the potential coordinated molecular mechanisms of PAR and ALD. The results indicated that the CFCG could treat RA by coordinated targeting to the genes involved in immunity and inflammation-related signal pathways, such as phosphatidylinositol 3‑kinase/protein kinase B signaling pathway, mitogen-activated protein kinase signaling pathway, tumor necrosis factor signaling pathway, and nuclear factor-kappa B signaling pathway. The docking and in vitro experiments were used to predict the affinity and validate the effect of CFCG and further confirm the reliability of our method. Our integrative pharmacological strategy, including CFCG identification and verification, can provide the methodological references for exploring the coordinated mechanism of TCM in treating complex diseases and contribute to improving our understanding of the coordinated mechanism.

A System Pharmacology Model for Decoding the Synergistic Mechanisms of Compound Kushen Injection in Treating Breast Cancer

Breast cancer (BC) is one of the most common malignant tumors among women worldwide and can be treated using various methods; however, side effects of these treatments cannot be ignored. Increasing evidence indicates that compound kushen injection (CKI) can be used to treat BC. However, traditional Chinese medicine (TCM) is characterized by “multi-components” and “multi-targets”, which make it challenging to clarify the potential therapeutic mechanisms of CKI on BC. Herein, we designed a novel system pharmacology strategy using differentially expressed gene analysis, pharmacokinetics synthesis screening, target identification, network analysis, and docking validation to construct the synergy contribution degree (SCD) and therapeutic response index (TRI) model to capture the critical components responding to synergistic mechanisms of CKI in BC. Through our designed mathematical models, we defined 24 components as a high contribution group of synergistic components (HCGSC) from 113 potentially active components of CKI based on ADME parameters. Pathway enrichment analysis of HCGSC targets indicated that Rhizoma Heterosmilacis and Radix Sophorae Flavescentis could synergistically target the PI3K-Akt signaling pathway and the cAMP signaling pathway to treat BC. Additionally, TRI analysis showed that the average affinity of HCGSC and targets involved in the key pathways reached -6.47 kcal/mmol, while in vitro experiments proved that two of the three high TRI-scored components in the HCGSC showed significant inhibitory effects on breast cancer cell proliferation and migration. These results demonstrate the accuracy and reliability of the proposed strategy.

Computational Network Pharmacology-Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression

Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus

Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

A Loop-Based and AGO-Incorporated Virtual Screening Model Targeting AGO-Mediated miRNA-mRNA Interactions for Drug Discovery to Rescue Bone Phenotype in Genetically Modified Mice

Several virtual screening models are proposed to screen small molecules only targeting primary miRNAs without selectivity. Few attempts have been made to develop virtual screening strategies for discovering small molecules targeting mature miRNAs. Mature miRNAs and their specific target mRNA can form unique functional loops during argonaute (AGO)-mediated miRNA-mRNA interactions, which may serve as potential targets for small-molecule drug discovery. Thus, a loop-based and AGO-incorporated virtual screening model is constructed for targeting the loops. The previously published studies have found that miR-214 can target ATF4 to inhibit osteoblastic bone formation, whereas miR-214 can target TRAF3 to promote osteoclast activity. By using the virtual model, the top ten candidate small molecules targeting miR-214-ATF4 mRNA interactions and top ten candidate small molecules targeting miR-214-TRAF3 mRNA interactions are selected, respectively. Based on both in vitro and in vivo data, one small molecule can target miR-214-ATF4 mRNA to promote ATF4 protein expression and enhance osteogenic potential, whereas one small molecule can target miR-214-TRAF3 mRNA to promote TRAF3 protein expression and inhibit osteoclast activity. These data indicate that the loop-based and AGO-incorporated virtual screening model can help to obtain small molecules specifically targeting miRNA-mRNA interactions to rescue bone phenotype in genetically modified mice.

Rapid Development of Targeting circRNAs in Cardiovascular Diseases

Circular RNAs (circRNAs) are circularized, single-stranded RNAs that are covalently linked. With their abundance in tissues and developmental stage-specific expression, circRNAs participate in a variety of physiological and pathological processes. In this review, we discuss the development of circRNAs used as biomarkers and therapeutic targets for cardiovascular diseases (CVDs), focusing on recent discoveries and applications of exosomal circRNAs that highlight opportunities and challenges. Some studies have identified a spectrum of circRNAs that are differentially expressed in CVDs, while other studies further manipulated specific circRNA expression and showed an ameliorated pathogenic state such as ischemic injury, hypertrophy, and cardiac fibrosis. Studies and applications of circRNAs are being rapidly developed. We expect to see clinical use of circRNAs as biomarkers and targets for disease treatment in the near future.

Imperatorin and β-sitosterol have synergistic activities in alleviating collagen-induced arthritis

Rheumatoid arthritis (RA) is a chronic disease with complex molecular network of pathophysiology, single drug is usually not full satisfactory because it is almost impossible to target the whole molecular network of the disease. Drug combinations that act synergistically with each another is an effective strategy in RA therapy. In this study, we aimed to establish a new strategy to search effective synergized compounds from Chinese herbal medicine (CHM) used in RA. Based on multi‐information integrative approaches, imperatorin (IMP) and β‐sitosterol (STO) were predicted as the most effective pair for RA therapy. Further animal experiments demonstrated that IMP+STO treatment ameliorated arthritis severity of collagen‐induced arthritis (CIA) rats in a synergistic manner, whereas IMP or STO administration separately had no such effect. RNA sequencing and IPA analysis revealed that the synergistic mechanism of IMP+STO treatment was related to its regulatory effect on 5 canonical signaling pathways, which were not found when IMP or STO used alone. Moreover, LTA, CD83, and SREBF1 were 3 important targets for synergistic mechanism of IMP+STO treatment. The levels of these 3 genes were significantly up‐regulated in IMP+STO group compared to model group, whereas IMP or STO administration separately had no effect on them. In conclusion, this study found that IMP and STO were 2 synergistic compounds from the CHM in RA therapy, whose synergistic mechanism was closely related to regulate the levels of LTA, CD83, and SREBF1.

Integrated transcriptomic and regulatory network analyses identify microRNA-200c as a novel repressor of human pluripotent stem cell-derived cardiomyocyte differentiation and maturation

Aims

MicroRNAs (miRNAs) are crucial for the post-transcriptional control of protein-encoding genes and together with transcription factors (TFs) regulate gene expression; however, the regulatory activities of miRNAs during cardiac development are only partially understood. In this study, we tested the hypothesis that integrative computational approaches could identify miRNAs that experimentally could be shown to regulate cardiomyogenesis.

Methods and results

We integrated expression profiles with bioinformatics analyses of miRNA and TF regulatory programs to identify candidate miRNAs involved with cardiac development. Expression profiling showed that miR-200c, which is not normally detected in adult heart, is progressively down-regulated both during cardiac development and in vitro differentiation of human embryonic stem cells (hESCs) to cardiomyocytes (CMs). We employed computational methodologies to predict target genes of both miR-200c and five key cardiac TFs to identify co-regulated gene networks. The inferred cardiac networks revealed that the cooperative action of miR-200c with these five key TFs, including three (GATA4, SRF and TBX5) targeted by miR-200c, should modulate key processes and pathways necessary for CM development and function. Experimentally, over-expression (OE) of miR-200c in hESC-CMs reduced the mRNA levels of GATA4, SRF and TBX5. Cardiac expression of Ca2+, K+ and Na+ ion channel genes (CACNA1C, KCNJ2 and SCN5A) were also significantly altered by knockdown or OE of miR-200c. Luciferase reporter assays validated miR-200c binding sites on the 3' untranslated region of CACNA1C. In hESC-CMs, elevated miR-200c increased beating frequency, and repressed both Ca2+ influx, mediated by the L-type Ca2+ channel and Ca2+ transients.

Conclusions

Our analyses demonstrate that miR-200c represses hESC-CM differentiation and maturation. The integrative computation and experimental approaches described here, when applied more broadly, will enhance our understanding of the interplays between miRNAs and TFs in controlling cardiac development and disease processes.

Efficacy of leflunomide combined with ligustrazine in the treatment of rheumatoid arthritis: prediction with network pharmacology and validation in a clinical trial

Background

Leflunomide (LEF) is a first-line disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, there are still a few nonresponders. It is logical to suggest that employing combinations including LEF that produce synergistic effects in terms of pharmacological activity is a promising strategy to improve clinical outcomes.

Methods

We propose a novel approach for predicting LEF combinations through investigating the potential effects of drug targets on the disease signaling network. We first constructed an RA signaling network with disease-associated driver genes. Thousands of available FDA-approved and investigational compounds were then selected based on a drug-RA network, which was generated using an algorithm model named synergistic score that combines chemical structure, functional prediction and target pathway. We then validated our predicted combination in a prospective clinical trial.

Results

Ligustrazine (LIG), a key component of the Chinese herb Chuanxiong and an approved drug in China, ranked first according to synergistic score. In the clinical trial, after 48 weeks, the American College of Rheumatology (ACR) 20 response rate was significantly lower (P < 0.05) in the LEF group [58.8% (45.4%, 72.3%)] than in the LEF + LIG group [78.7% (68.5%, 89.0%)]. Consistently, the erosion score was lower in patients treated with LEF + LIG than in those treated with LEF (0.34 ± 0.20 vs 1.12 ± 0.30, P < 0.05).

Conclusions

Our algorithm combines structure and target pathways into one model that predicted that the combination of LEF and LIG can reduce joint inflammation and attenuate bone erosion in RA patients. To our knowledge, this study is the first to apply this paradigm to evaluate drug combination hypotheses.

Electronic supplementary material

The online version of this article (10.1186/s13020-019-0247-8) contains supplementary material, which is available to authorized users.

Bushen Yijing Fang Reduces Fall Risk in Late Postmenopausal Women with Osteopenia: A Randomized Double-blind and Placebo-controlled Trial

Falls in late postmenopausal women with osteopenia usually cause fractures with severe consequences. This 36-month randomized, double-blind and placebo-controlled trial with a 10-year observational follow-up study aimed to investigate the long-term effect of herbal formula Bushen Yijing Fang (BSYJF) on fall risk in the late postmenopausal women with osteopenia. 140 late postmenopausal women (Femoral neck T-score, −2.5~−2 SD) were recruited and randomized to orally receive calcium carbonate 300 mg daily with either BSYJF or placebo for 36 months. The effect was further investigated for another 10-year follow-up. During the 36-month administration, there were 12 falls in BSYJF group and 28 falls in placebo group, respectively, indicating 64% lower risk of falls (RR 0.36 [95% CI, 0.18 to 0.71]; P = 0.004) in BSYJF group. During the 10-year follow-up, 36% lower fall risk (RR 0.64 [95% CI, 0.46 to 0.89]; P = 0.009) was observed in BSYJF group. No significant difference was found in safety profile between two groups. Thirty-six-month administration of BSYJF reduced fall risk with an increase in bone mass, and its latent effect on fall risk was continually observed in the 10-year follow-up in late postmenopausal women with osteopenia. This clinical trial was registered at Chinese clinical trial registry (ChiCTR-IOR-16008942).

Long Noncoding RNA lncMUMA Reverses Established Skeletal Muscle Atrophy following Mechanical Unloading

Reversing established muscle atrophy following mechanical unloading is of great clinical challenge. Long noncoding RNAs (lncRNAs) have been demonstrated to play important roles in myogenesis. Here we identified a lncRNA (mechanical unloading-induced muscle atrophy-related lncRNA [lncMUMA]) enriched in muscle, which was the most downregulated lncRNA during muscle atrophy development in hindlimb suspension (HLS) mice. The in vitro and in vivo data demonstrated that the decreased expression levels of lncMUMA closely associated with a reduction of myogenesis during mechanical unloading. Mechanistically, lncMUMA promoted myogenic differentiation by functioning as a miR-762 sponge to regulate the core myogenic regulator MyoD in vitro. The enforced expression of lncMUMA relieved the decreases in MyoD protein and muscle mass in miR-762 knockin mice. Therapeutically, the enforced expression of lncMUMA improved the in vitro myogenic differentiation of myoblasts under microgravity simulation, prevented the muscle atrophy development, and reversed the established muscle atrophy in HLS mice. These findings identify lncMUMA as an anabolic regulator to reverse established muscle atrophy following mechanical unloading.

Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures

The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases.

Inhibition of osteoblastic Smurf1 promotes bone formation in mouse models of distinctive age-related osteoporosis

Bone morphogenetic protein (BMP) signaling is essential for osteogenesis. However, recombinant human BMPs (rhBMPs) exhibit large inter-individual variations in local bone formation during clinical spinal fusion. Smurf1 ubiquitinates BMP downstream molecules for degradation. Here, we classify age-related osteoporosis based on distinct intraosseous BMP-2 levels and Smurf1 activity. One major subgroup with a normal BMP-2 level and elevated Smurf1 activity (BMP-2ⁿ/Smurf1^e) shows poor response to rhBMP-2 during spinal fusion, when compared to another major subgroup with a decreased BMP-2 level and normal Smurf1 activity (BMP-2^d/Smurf1ⁿ). We screen a chalcone derivative, i.e., 2-(4-cinnamoylphenoxy)acetic acid, which effectively inhibits Smurf1 activity and increases BMP signaling. For BMP-2ⁿ/Smurf1^e mice, the chalcone derivative enhances local bone formation during spinal fusion. After conjugating to an osteoblast-targeting and penetrating oligopeptide (DSS)₆, the chalcone derivative promotes systemic bone formation in BMP-2ⁿ/Smurf1^e mice. This study demonstrates a precision medicine-based bone anabolic strategy for age-related osteoporosis.

BMP promotes bone formation but its efficacy is limited in some patients. Here, the authors show that osteoporosis patients with a poor response to BMP have increased expression of Smurf1, which targets BMP effectors for degradation, and demonstrate that its chemical inhibition enhances BMP-mediated bone formation in mice.

A newly identified lncRNA MAR1 acts as a miR-487b sponge to promote skeletal muscle differentiation and regeneration

BACKGROUND

Skeletal muscle atrophy induced by either aging (sarcopenia) or mechanical unloading is associated with serious health consequences. Long non-coding RNAs (lncRNAs) are implicated as important regulators in numerous physiological and pathological processes.

METHODS

Microarray analysis was performed to identify the differentially expressed lncRNAs in skeletal muscle between adult and aged mice. The most decreased lncRNA in aged skeletal muscle was identified. The C2C12 mouse myoblast cells were used to assess the biological function of the lncRNA in vitro. The target microRNA of lncRNA and the target protein of microRNA were predicted by bioinformatics analysis and validated in vitro. Furthermore, the biology function of the lncRNA in vivo was investigated by local overexpression or knockdown the lncRNA in skeletal muscle. The therapeutic effect of the lncRNA overexpression in age-related or mechanical unloading-induced muscle atrophy was also evaluated.

RESULTS

We identified a novel lncRNA (muscle anabolic regulator 1, MAR1) which was highly expressed in mice skeletal muscle and positively correlated with muscle differentiation and growth in vitro and in vivo. We predicted and validated that microRNA-487b (miR-487b) was a direct target of MAR1. We also predicted and validated that Wnt5a, an important regulator during myogenesis, was a target of miR-487b in C2C12 cells. Our findings further demonstrated that enforced MAR1 expression in myoblasts led to derepression of Wnt5a. Moreover, MAR1 promoted skeletal muscle mass/strength and Wnt5a protein level in mice. Enforced MAR1 expression in mice attenuated muscle atrophy induced by either aging or unloading.

CONCLUSIONS

The newly identified lncRNA MAR1 acts as a miR-487b sponge to regulate Wnt5a protein, resulting in promoting muscle differentiation and regeneration. MAR1 could be a novel therapeutic target for treating muscle atrophy induced by either aging or mechanical unloading.

A water-soluble nucleolin aptamer-paclitaxel conjugate for tumor-specific targeting in ovarian cancer

Paclitaxel (PTX) is among the most commonly used first-line drugs for cancer chemotherapy. However, its poor water solubility and indiscriminate distribution in normal tissues remain clinical challenges. Here we design and synthesize a highly water-soluble nucleolin aptamer-paclitaxel conjugate (NucA-PTX) that selectively delivers PTX to the tumor site. By connecting a tumor-targeting nucleolin aptamer (NucA) to the active hydroxyl group at 2' position of PTX via a cathepsin B sensitive dipeptide bond, NucA-PTX remains stable and inactive in the circulation. NucA facilitates the uptake of the conjugated PTX specifically in tumor cells. Once inside cells, the dipeptide bond linker of NucA-PTX is cleaved by cathepsin B and then the conjugated PTX is released for action. The NucA modification assists the selective accumulation of the conjugated PTX in ovarian tumor tissue rather than normal tissues, and subsequently resulting in notably improved antitumor activity and reduced toxicity.

PARP1 in Carcinomas and PARP1 Inhibitors as Antineoplastic Drugs

Poly (ADP-ribose) polymerase 1 (PARP1), the best-studied isoform of the nuclear enzyme PARP family, plays a pivotal role in cellular biological processes, such as DNA repair, gene transcription, and so on. PARP1 has been found to be overexpressed in various carcinomas. These all indicate the clinical potential of PARP1 as a therapeutic target of human malignancies. Additionally, multiple preclinical research studies and clinical trials demonstrate that inhibition of PARP1 can repress tumor growth and metastasis. Up until now, PARP1 inhibitors are clinically used not only for monotherapy to suppress various tumors, but also for adjuvant therapy, to maintain or enhance therapeutic effects of mature antineoplastic drugs, as well as protect patients from chemotherapy and surgery-induced injury. To supply a framework for understanding recent research progress of PARP1 in carcinomas, we review the structure, expression, functions, and mechanisms of PARP1, and summarize the clinically mature PARP1-related anticancer agents, to provide some ideas for the development of other promising PARP1 inhibitors in antineoplastic therapy.

Molecular Mechanisms and Translational Therapies for Human Epidermal Receptor 2 Positive Breast Cancer

Breast cancer is the second leading cause of cancer death among women. Human epidermal receptor 2 (HER2) positive breast cancer (HER2+ BC) is the most aggressive subtype of breast cancer, with poor prognosis and a high rate of recurrence. About one third of breast cancer is HER2+ BC with significantly high expression level of HER2 protein compared to other subtypes. Therefore, HER2 is an important biomarker and an ideal target for developing therapeutic strategies for the treatment HER2+ BC. In this review, HER2 structure and physiological and pathological roles in HER2+ BC are discussed. Two diagnostic tests, immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH), for evaluating HER2 expression levels are briefly introduced. The current mainstay targeted therapies for HER2+ BC include monoclonal antibodies, small molecule tyrosine kinase inhibitors, antibody-drug conjugates (ADC) and other emerging anti-HER2 agents. In clinical practice, combination therapies are commonly adopted in order to achieve synergistic drug response. This review will help to better understand the molecular mechanism of HER2+ BC and further facilitate the development of more effective therapeutic strategies against HER2+ BC.

PlaMoM: a comprehensive database compiles plant mobile macromolecules

In plants, various phloem-mobile macromolecules including noncoding RNAs, mRNAs and proteins are suggested to act as important long-distance signals in regulating crucial physiological and morphological transition processes such as flowering, plant growth and stress responses. Given recent advances in high-throughput sequencing technologies, numerous mobile macromolecules have been identified in diverse plant species from different plant families. However, most of the identified mobile macromolecules are not annotated in current versions of species-specific databases and are only available as non-searchable datasheets. To facilitate study of the mobile signaling macromolecules, we compiled the PlaMoM (Plant Mobile Macromolecules) database, a resource that provides convenient and interactive search tools allowing users to retrieve, to analyze and also to predict mobile RNAs/proteins. Each entry in the PlaMoM contains detailed information such as nucleotide/amino acid sequences, ortholog partners, related experiments, gene functions and literature. For the model plant Arabidopsis thaliana, protein–protein interactions of mobile transcripts are presented as interactive molecular networks. Furthermore, PlaMoM provides a built-in tool to identify potential RNA mobility signals such as tRNA-like structures. The current version of PlaMoM compiles a total of 17 991 mobile macromolecules from 14 plant species/ecotypes from published data and literature. PlaMoM is available at http://www.systembioinfo.org/plamom/.

Timing of Tissue-specific Cell Division Requires a Differential Onset of Zygotic Transcription during Metazoan Embryogenesis

Metazoan development demands not only precise cell fate differentiation but also accurate timing of cell division to ensure proper development. How cell divisions are temporally coordinated during development is poorly understood. Caenorhabditis elegans embryogenesis provides an excellent opportunity to study this coordination due to its invariant development and widespread division asynchronies. One of the most pronounced asynchronies is a significant delay of cell division in two endoderm progenitor cells, Ea and Ep, hereafter referred to as E2, relative to its cousins that mainly develop into mesoderm organs and tissues. To unravel the genetic control over the endoderm-specific E2 division timing, a total of 822 essential and conserved genes were knocked down using RNAi followed by quantification of cell cycle lengths using in toto imaging of C. elegans embryogenesis and automated lineage. Intriguingly, knockdown of numerous genes encoding the components of general transcription pathway or its regulatory factors leads to a significant reduction in the E2 cell cycle length but an increase in cell cycle length of the remaining cells, indicating a differential requirement of transcription for division timing between the two. Analysis of lineage-specific RNA-seq data demonstrates an earlier onset of transcription in endoderm than in other germ layers, the timing of which coincides with the birth of E2, supporting the notion that the endoderm-specific delay in E2 division timing demands robust zygotic transcription. The reduction in E2 cell cycle length is frequently associated with cell migration defect and gastrulation failure. The results suggest that a tissue-specific transcriptional activation is required to coordinate fate differentiation, division timing, and cell migration to ensure proper development.

Integrated transcriptional profiling and genomic analyses reveal RPN2 and HMGB1 as promising biomarkers in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease that is associated with a gradual accumulation of genetic and epigenetic alterations. Among all CRC stages, stage II tumors are highly heterogeneous with a high relapse rate in about 20–25 % of stage II CRC patients following surgery. Thus, a comprehensive analysis of gene signatures to identify aggressive and metastatic phenotypes in stage II CRC is desired for a more accurate disease classification and outcome prediction. By utilizing a Cancer Array, containing 440 oncogenes and tumor suppressors to profile mRNA expression, we identified a larger number of differentially expressed genes in poorly differentiated stage II colorectal adenocarcinoma tissues, compared to their matched normal tissues. Ontology and Ingenuity Pathway Analysis (IPA) indicated that these genes are involved in functional mechanisms associated with several transcription factors. Genomic alterations of these genes were also investigated through The Cancer Genome Atlas (TCGA) database, utilizing 195 published CRC specimens. The percentage of genomic alterations in these genes was ranked based on their mRNA expression, copy number variations and mutations. This data was further combined with published microarray studies from a large set of CRC tumors classified based on prognostic features. This led to the identification of eight candidate genes including RPN2, HMGB1, AARS, IGFBP3, STAT1, HYOU1, NQO1 and PEA15 that were associated with the progressive phenotype. In particular, RPN2 and HMGB1 displayed a higher genomic alteration frequency in CRC, compared to eight other major solid cancers. Immunohistochemistry was performed on additional 78 stage I–IV CRC samples, where RPN2 protein immunostaining exhibited a significant association with stage III/IV tumors, distant metastasis, and poor differentiation, indicating that RPN2 expression is associated with poor prognosis. Further, our study revealed significant transcriptional regulatory mechanisms, networks and gene signatures, underlying CRC malignant progression and phenotype warranting future clinical investigations.

Transcriptomic and Functional Pathway Analysis of Human Cervical Carcinoma Cancer Cells Response to Microtubule Inhibitor

BACKGROUND

There clearly is a need for effective chemotherapy for early-stage, high-risk patients with human cervical carcinoma. Vinblastine (VBL) is a key microtubule inhibitor, but unproven in its mechanisms as an important antitumor agent in cervical carcinoma.

METHODS

We selected the concentration of vinblastine inducing 30% cell death for analyses assessing the DNA content, gene expression and transcriptional gene regulation of VBL-treated KB-3 cells.

RESULTS

Transcriptomic and hierarchical clustering analysis demonstrated that treatment of KB-3 cells with VBL altered the expression of a diverse group of genes with G2/M arrest, which regulated by four oncogenic or tumor suppresser transcription factors (AP1, NFKB1, RELA, and TP53). Functional pathway analysis revealed the disease response to the biological effects of vinblastine in cervical carcinoma chemotherapy including protein ubiquitination pathway, RhoGDI signaling, integrin signaling, agranulocyte adhesion and biapedesis, and actin nucleation pathways. Northern blots also confirmed that KRT-7, FN14, IER3, and ID1 were deregulated in VBL-treated KB-3 cells.

CONCLUSION

Transcriptional time series profiles and a functional pathway analysis of VBL-treated KB-3 cells will provide a new strategy for improving microtubule inhibitor chemotherapy for cervical carcinoma.

Systems-level quantification of division timing reveals a common genetic architecture controlling asynchrony and fate asymmetry

Coordination of cell division timing is crucial for proper cell fate specification and tissue growth. However, the differential regulation of cell division timing across or within cell types during metazoan development remains poorly understood. To elucidate the systems-level genetic architecture coordinating division timing, we performed a high-content screening for genes whose depletion produced a significant reduction in the asynchrony of division between sister cells (ADS) compared to that of wild-type during Caenorhabditis elegans embryogenesis. We quantified division timing using 3D time-lapse imaging followed by computer-aided lineage analysis. A total of 822 genes were selected for perturbation based on their conservation and known roles in development. Surprisingly, we find that cell fate determinants are not only essential for establishing fate asymmetry, but also are imperative for setting the ADS regardless of cellular context, indicating a common genetic architecture used by both cellular processes. The fate determinants demonstrate either coupled or separate regulation between the two processes. The temporal coordination appears to facilitate cell migration during fate specification or tissue growth. Our quantitative dataset with cellular resolution provides a resource for future analyses of the genetic control of spatial and temporal coordination during metazoan development.

PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data

Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein–protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.

CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data

ChIP-seq technology provides an accurate characterization of transcription or epigenetic factors binding on genomic sequences. With integration of such ChIP-based and other high-throughput information, it would be dedicated to dissecting cross-interactions among multilevel regulators, genes and biological functions. Here, we devised an integrative web server CMGRN (constructing multilevel gene regulatory networks), to unravel hierarchical interactive networks at different regulatory levels. The newly developed method used the Bayesian network modeling to infer causal interrelationships among transcription factors or epigenetic modifications by using ChIP-seq data. Moreover, it used Bayesian hierarchical model with Gibbs sampling to incorporate binding signals of these regulators and gene expression profile together for reconstructing gene regulatory networks. The example applications indicate that CMGRN provides an effective web-based framework that is able to integrate heterogeneous high-throughput data and to reveal hierarchical 'regulome' and the associated gene expression programs.

AVAILABILITY

http://bioinfo.icts.hkbu.edu.hk/cmgrn; http://www.byanbioinfo.org/cmgrn CONTACT: yanbinai6017@gmail.com or junwen@hku.hk Supplementary Information: Supplementary data are available at Bioinformatics online.

Unraveling regulatory programs for NF-kappaB, p53 and microRNAs in head and neck squamous cell carcinoma

In head and neck squamous cell carcinoma (HNSCC), mutations of p53 usually coexist with aberrant activation of NF-kappaB (NF-κB), other transcription factors and microRNAs, which promote tumor pathogenesis. However, how these factors and microRNAs interact to globally modulate gene expression and mediate oncogenesis is not fully understood. We devised a novel bioinformatics method to uncover interactive relationships between transcription factors or microRNAs and genes. This approach is based on matrix decomposition modeling under the joint constraints of sparseness and regulator-target connectivity, and able to integrate gene expression profiling and binding data of regulators. We employed this method to infer the gene regulatory networks in HNSCC. We found that the majority of the predicted p53 targets overlapped with those for NF-κB, suggesting that the two transcription factors exert a concerted modulation on regulatory programs in tumor cells. We further investigated the interrelationships of p53 and NF-κB with five additional transcription factors, AP1, CEBPB, EGR1, SP1 and STAT3, and microRNAs mir21 and mir34ac. The resulting gene networks indicate that interactions among NF-κB, p53, and the two miRNAs likely regulate progression of HNSCC. We experimentally validated our findings by determining expression of the predicted NF-κB and p53 target genes by siRNA knock down, and by examining p53 binding activity on promoters of predicted target genes in the tumor cell lines. Our results elucidating the cross-regulations among NF-κB, p53, and microRNAs provide insights into the complex regulatory mechanisms underlying HNSCC, and shows an efficient approach to inferring gene regulatory programs in biological complex systems.