Pro-angiogenic photo-crosslinked silk fibroin hydrogel: a potential candidate for repairing alveolar bone defects.

OBJECTIVE: This study aimed to develop a pro-angiogenic hydrogel with in situ gelation ability for alveolar bone defects repair. METHODOLOGY: Silk fibroin was chemically modified by Glycidyl Methacrylate (GMA), which was evaluated by proton nuclear magnetic resonance (1H-NMR). Then, the photo-crosslinking ability of the modified silk fibroin was assessed. Scratch and transwell-based migration assays were conducted to investigate the effect of the photo-crosslinked silk fibroin hydrogel on the migration of human umbilical vein endothelial cells (HUVECs). In vitro angiogenesis was conducted to examine whether the photo-crosslinked silk fibroin hydrogel would affect the tube formation ability of HUVECs. Finally, subcutaneous implantation experiments were conducted to further examine the pro-angiogenic ability of the photo-crosslinked silk fibroin hydrogel, in which the CD31 and α-smooth muscle actin (α-SMA) were stained to assess neovascularization. The tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) were also stained to evaluate inflammatory responses after implantation. RESULTS: GMA successfully modified the silk fibroin, which we verified by our 1H-NMR and in vitro photo-crosslinking experiment. Scratch and transwell-based migration assays proved that the photo-crosslinked silk fibroin hydrogel promoted HUVEC migration. The hydrogel also enhanced the tube formation of HUVECs in similar rates to Matrigel®. After subcutaneous implantation in rats for one week, the hydrogel enhanced neovascularization without triggering inflammatory responses. CONCLUSION: This study found that photo-crosslinked silk fibroin hydrogel showed pro-angiogenic and inflammation inhibitory abilities. Its photo-crosslinking ability makes it suitable for matching irregular alveolar bone defects. Thus, the photo-crosslinkable silk fibroin-derived hydrogel is a potential candidate for constructing scaffolds for alveolar bone regeneration.

The mechanisms of Qizhu Tangshen formula in the treatment of diabetic kidney disease: Network pharmacology, machine learning, molecular docking and experimental assessment.

BACKGROUND: Qizhu Tangshen Formula (QZTS) has been shown therapeutic effects on diabetic kidney disease (DKD). However, to date, the pharmacological mechanisms remain vague. METHODS: To explore the underlying mechanisms of QZTS in treating DKD using network pharmacology, machine learning, molecular docking and experimental assessment. RESULTS: First, we found that QZTS improved glycolipid metabolism disorder, decreased proteinuria and alleviated kidney tissue injury in DKD model KKAy mice. Then, by integrating multiple databases, a total of 96 targets of 74 active compounds in QZTS and 759 DKD-related genes were acquired. Next, we identified 13 hub targets of QZTS in DKD by three rank algorithms, including functional similarity, topological similarity and shortest path. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that the pathways mainly centered on the processes of glycolipid metabolism disorder, inflammation and angiogenesis. Among them, VEGF signaling pathway was significantly enriched. Molecular docking showed that key active compounds of QZTS all had relatively good binding affinity with predicted hub targets. Finally, animal experiments found that QZTS significantly inhibited the secretion of plasma VEGF and downregulated the protein and mRNA expression levels of AKT, p38MAPK and VEGFR2. CONCLUSION: Our results indicated that QZTS treated DKD via multiple targets and pathways and the VEGF signaling pathway may be highly involved in this process.

Pro-angiogenic photo-crosslinked silk fibroin hydrogel: a potential candidate for repairing alveolar bone defects

Abstract Objective: This study aimed to develop a pro-angiogenic hydrogel with in situ gelation ability for alveolar bone defects repair. Methodology: Silk fibroin was chemically modified by Glycidyl Methacrylate (GMA), which was evaluated by proton nuclear magnetic resonance (1H-NMR). Then, the photo-crosslinking ability of the modified silk fibroin was assessed. Scratch and transwell-based migration assays were conducted to investigate the effect of the photo-crosslinked silk fibroin hydrogel on the migration of human umbilical vein endothelial cells (HUVECs). In vitro angiogenesis was conducted to examine whether the photo-crosslinked silk fibroin hydrogel would affect the tube formation ability of HUVECs. Finally, subcutaneous implantation experiments were conducted to further examine the pro-angiogenic ability of the photo-crosslinked silk fibroin hydrogel, in which the CD31 and α-smooth muscle actin (α-SMA) were stained to assess neovascularization. The tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) were also stained to evaluate inflammatory responses after implantation. Results: GMA successfully modified the silk fibroin, which we verified by our 1H-NMR and in vitro photo-crosslinking experiment. Scratch and transwell-based migration assays proved that the photo-crosslinked silk fibroin hydrogel promoted HUVEC migration. The hydrogel also enhanced the tube formation of HUVECs in similar rates to Matrigel®. After subcutaneous implantation in rats for one week, the hydrogel enhanced neovascularization without triggering inflammatory responses. Conclusion: This study found that photo-crosslinked silk fibroin hydrogel showed pro-angiogenic and inflammation inhibitory abilities. Its photo-crosslinking ability makes it suitable for matching irregular alveolar bone defects. Thus, the photo-crosslinkable silk fibroin-derived hydrogel is a potential candidate for constructing scaffolds for alveolar bone regeneration.

A network-based machine-learning framework to identify both functional modules and disease genes.

Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.

Integrated network analysis of symptom clusters across disease conditions.

Identifying the symptom clusters (two or more related symptoms) with shared underlying molecular mechanisms has been a vital analysis task to promote the symptom science and precision health. Related studies have applied the clustering algorithms (e.g. k-means, latent class model) to detect the symptom clusters mostly from various kinds of clinical data. In addition, they focused on identifying the symptom clusters (SCs) for a specific disease, which also mainly concerned with the clinical regularities for symptom management. Here, we utilized a network-based clustering algorithm (i.e., BigCLAM) to obtain 208 typical SCs across disease conditions on a large-scale symptom network derived from integrated high-quality disease-symptom associations. Furthermore, we evaluated the underlying shared molecular mechanisms for SCs, i.e., shared genes, protein-protein interaction (PPI) and gene functional annotations using integrated networks and similarity measures. We found that the symptoms in the same SCs tend to share a higher degree of genes, PPIs and have higher functional homogeneities. In addition, we found that most SCs have related symptoms with shared underlying molecular mechanisms (e.g. enriched pathways) across different disease conditions. Our work demonstrated that the integrated network analysis method could be used for identifying robust SCs and investigate the molecular mechanisms of these SCs, which would be valuable for symptom science and precision health.

Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia.

Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data resources might be promising to help investigate the molecular mechanisms of pain phenotypes. Here, we integrated large-scale bibliographic literatures and well-established data sources to obtain high-quality pain relevant herbal data (i.e. 426 pain related herbs with their targets). We used machine learning method to identify three distinct herb categories with their specific indications of symptoms, targets and enriched pathways, which were characterized by the efficacy of treatment to the chronic cough related neuropathic pain, the reproduction and autoimmune related pain, and the cancer pain, respectively. We further detected the novel pathophysiological mechanisms of the pain subtypes by network medicine approach to evaluate the interactions between herb targets and the pain disease modules. This work increased the understanding of the underlying molecular mechanisms of pain subtypes that herbal drugs are participating and with the ultimate aim of developing novel personalized drugs for pain disorders.

Protective functions of myricetin in LPS-induced cardiomyocytes H9c2 cells injury by regulation of MALAT1.

BACKGROUND: Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a crucial mediator in response to inflammation. Myricetin protects cardiomyocytes against inflammatory injury. However, it's still unexplored whether myricetin exerted anti-inflammatory properties via MALAT1. The purpose of our study was to validate the cardio-protective function of myricetin against myocarditis and its underlying mechanism in vitro. METHODS: H9c2 cells were pre-incubated with myricetin before stimulation with lipopolysaccharide (LPS). Enforced silence of MALAT1 was achieved by transducing short hairpin (sh)-MALAT1 into H9c2 cells. Next, cell viability and apoptotic cells were detected with cell counting kit-8 (CCK-8) and Annexin V-fluorescein isothiocyanate/propidium iodide (Annexin V-FITC/PI) apoptosis detection kit, respectively. Western blot assay was conducted to examine apoptosis-relative proteins, pro-inflammatory factors, and signaling regulators. Quantitative real-time PCR (qRT-PCR) was performed to quantify pro-inflammatory factors and MALAT1 at mRNA levels. Enzyme-linked immune sorbent assay (ELISA) was employed to determine protein concentration of pro-inflammatory factors. RESULTS: Myricetin ameliorated LPS-elicited reduction of cell viability, augment of apoptosis, and overexpression of monocyte chemo-attractant protein-1 (MCP-1) and interleukin-6 (IL-6) in H9c2 cells. Meanwhile, phosphorylation of p65 and inhibitor of nuclear factor kappa B alpha (IκBα) were suppressed. Besides, myricetin enhanced the expression of MALAT1 which was originally down-regulated by LPS. However, the protective effects of myricetin against LPS-caused inflammatory lesions were abrogated in MALAT1-deficiency cells, with the restored phosphorylation of p65 and IκBα. CONCLUSION: Myricetin possessed an anti-inflammatory function against LPS-induced lesions in cardiomyocytes. Mechanically, myricetin up-regulated MALAT1, blocked LPS-evoked activation of nuclear factor-κB (NF-κB) inflammatory pathway, and, finally, exerted cardio-protective effects.

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways.

BACKGROUND AND OBJECTIVE: Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment. METHODS: Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups. RESULTS: From the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup. CONCLUSIONS: Our results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.

Genetics of rheumatoid arthritis contributes to biology and drug discovery.

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Human genetics in rheumatoid arthritis guides a high-throughput drug screen of the CD40 signaling pathway.

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10(-9)). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10(-9)), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA-approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA.

Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks.

OBJECTIVE: The amount of biomedical data in different disciplines is growing at an exponential rate. Integrating these significant knowledge sources to generate novel hypotheses for systems biology research is difficult. Traditional Chinese medicine (TCM) is a completely different discipline, and is a complementary knowledge system to modern biomedical science. This paper uses a significant TCM bibliographic literature database in China, together with MEDLINE, to help discover novel gene functional knowledge. MATERIALS AND METHODS: We present an integrative mining approach to uncover the functional gene relationships from MEDLINE and TCM bibliographic literature. This paper introduces TCM literature (about 50,000 records) as one knowledge source for constructing literature-based gene networks. We use the TCM diagnosis, TCM syndrome, to automatically congregate the related genes. The syndrome-gene relationships are discovered based on the syndrome-disease relationships extracted from TCM literature and the disease-gene relationships in MEDLINE. Based on the bubble-bootstrapping and relation weight computing methods, we have developed a prototype system called MeDisco/3S, which has name entity and relation extraction, and online analytical processing (OLAP) capabilities, to perform the integrative mining process. RESULTS: We have got about 200,000 syndrome-gene relations, which could help generate syndrome-based gene networks, and help analyze the functional knowledge of genes from syndrome perspective. We take the gene network of Kidney-Yang Deficiency syndrome (KYD syndrome) and the functional analysis of some genes, such as CRH (corticotropin releasing hormone), PTH (parathyroid hormone), PRL (prolactin), BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset), to demonstrate the preliminary results. The underlying hypothesis is that the related genes of the same syndrome will have some biological functional relationships, and will constitute a functional network. CONCLUSION: This paper presents an approach to integrate TCM literature and modern biomedical data to discover novel gene networks and functional knowledge of genes. The preliminary results show that the novel gene functional knowledge and gene networks, which are worthy of further investigation, could be generated by integrating the two complementary biomedical data sources. It will be a promising research field through integrative mining of TCM and modern life science literature.