Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds

Predicting herb-disease associations using network-based measures in human protein interactome

BACKGROUND

Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations.

RESULTS

In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences.

CONCLUSIONS

This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

Combination of UHPLC-MS/MS with context-specific network and cheminformatic approaches for identifying bioactivities and active components of propolis

Discovering new bioactivities and identifying active compounds of food materials are major fields of study in food science. However, the process commonly requires extensive experiments and can be technically challenging. In the current study, we employed network biology and cheminformatic approaches to predict new target diseases, active components, and related molecular mechanisms of propolis. Applying UHPLC-MS/MS analysis results of propolis to Context-Oriented Directed Associations (CODA) and Combination-Oriented Natural Product Database with Unified Terminology (COCONUT) systems indicated atopic dermatitis as a novel target disease. Experimental validation using cell- and human tissue-based models confirmed the therapeutic potential of propolis against atopic dermatitis. Moreover, we were able to find the major contributing compounds as well as their combinatorial effects responsible for the bioactivity of propolis. The CODA/COCONUT system also provided compound-associated genes explaining the underlying molecular mechanism of propolis. These results highlight the potential use of big data-driven network biological approaches to aid in analyzing the impact of food constituents at a systematic level.

Detailed Chemical Characterization and Biological Propensities of Malabaila lasiocarpa Extracts: An Endemic Plant to Turkey

This study focused on the biological evaluation and chemical characterization of Malabaila lasiocarpa Boiss. (M. lasiocarpa) (Family: Apiaceae). The phytochemical profile, antioxidant, enzyme inhibitory of the methanolic, aqueous, dichloromethane, hexane extracts were investigated. Based on UHPLC-HRMS analyses, a total of 101 peaks were annotated or identified for the first time in M. lasiocarpa extracts. They include hydroxybenzoic, hydroxycinnamic, acylquinic acids and their glycosides, C- and O-glycosyl and O-diglycosyl flavonoids. In addition, 10 simple mono- and disubstituted coumarins together with 10 furanocoumarins were tentatively annotated. The methanolic extract possessing the highest phenolic (24.36±0.60 mg gallic acid equivalent/g extract) and flavonoid (69.15±0.37 mg rutin equivalent/g extract) content also exhibited the strongest radical scavenging potential against 2,2-diphenyl-1 picrylhydrazyl (21.73±0.42 mg Trolox equivalent/g extract, respectively), and highest reducing capacity (57.81±0.97 and 28.00±0.40 mg Trolox equivalent/g extract, for cupric reducing antioxidant capacity and ferric reducing antioxidant power, respectively). The dichloromethane extract substantially depressed the tyrosinase (73.92±5.37 mg kojic acid equivalent/g extract), α-amylase (0.63±0.01 mmol acarbose equivalent/g extract) and α-glucosidase (0.69±0.02 mmol acarbose equivalent/g extract) enzymes. This study has produced critical scientific data on M. lasiocarpa which are potential contenders for the development of novel phyto-pharmaceuticals.

Phytochemical Insights into Ficus sur Extracts and Their Biological Activity

This study focused on the biological evaluation and chemical characterisation of Ficus sur Forssk. (F. sur) (Family: Moraceae). The methanolic and aqueous extracts’ phytochemical profile, antioxidant, and enzyme inhibitory properties were investigated. The aqueous stem bark extract yielded the highest phenolic content (115.51 ± 1.60 mg gallic acid equivalent/g extract), while the methanolic leaves extract possessed the highest flavonoid content (27.47 ± 0.28 mg Rutin equivalent/g extract). In total, 118 compounds were identified in the tested extracts. The methanolic stem bark extract exhibited the most potent radical scavenging potential against 2,2-diphenyl-1 picrylhydrazyl and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (475.79 ± 6.83 and 804.31 ± 4.52 mg Trolox equivalent/g extract, respectively) and the highest reducing Cu²⁺ capacity (937.86 ± 14.44 mg Trolox equivalent/g extract). The methanolic stem bark extract substantially depressed tyrosinase (69.84 ± 0.35 mg kojic acid equivalent/g extract), α-amylase (0.77 ± 0.01 mmol acarbose equivalent/g extract), acetylcholinesterase and butyrylcholinesterase (2.91 ± 0.07 and 6.56 ± 0.34 mg galantamine equivalent/g extract, respectively) enzymes. F. sur extracts were tested for anticancer properties and antiviral activity towards human herpes virus type 1 (HHV-1). Stem bark infusion and methanolic extract showed antineoplastic activity against cervical adenocarcinoma and colon cancer cell lines, whereas leaf methanolic extract exerted moderate antiviral activity towards HHV-1. This investigation yielded important scientific data on F. sur which might be used to generate innovative phytopharmaceuticals.

SYmptom-Based STratification of DiabEtes Mellitus by Renal Function Decline (SYSTEM): A Retrospective Cohort Study and Modeling Assessment

Background: Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine. Methods: This retrospective cohort study aimed to evaluate the predictive power of a symptom-based stratification framework and individual symptoms for diabetes. Three hundred two adult diabetic patients were consecutively sampled from outpatient clinics in Hong Kong for prospective symptom assessment. Demographics and longitudinal measures of biochemical parameters were retrospectively extracted from linked medical records. The association between estimated glomerular filtration rate (GFR) (independent variable) and biochemistry, epidemiological factors, and individual symptoms was assessed by mixed regression analyses. A symptom-based stratification framework of diabetes using symptom clusters was formulated by Delphi consensus method. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were compared between statistical models with different combinations of biochemical, epidemiological, and symptom variables. Results: In the 4.2-year follow-up period, baseline presentation of edema (-1.8 ml/min/1.73m2, 95%CI: -2.5 to -1.2, p < 0.001), epigastric bloating (-0.8 ml/min/1.73m2, 95%CI: -1.4 to -0.2, p = 0.014) and alternating dry and loose stool (-1.1 ml/min/1.73m2, 95%CI: -1.9 to -0.4, p = 0.004) were independently associated with faster annual GFR decline. Eleven symptom clusters were identified from literature, stratifying diabetes predominantly by gastrointestinal phenotypes. Using symptom clusters synchronized by Delphi consensus as the independent variable in statistical models reduced complexity and improved explanatory power when compared to using individual symptoms. Symptom-biologic-epidemiologic combined model had the lowest AIC (4,478 vs. 5,824 vs. 4,966 vs. 7,926) and BIC (4,597 vs. 5,870 vs. 5,065 vs. 8,026) compared to the symptom, symptom-epidemiologic and biologic-epidemiologic models, respectively. Patients co-presenting with a constellation of fatigue, malaise, dry mouth, and dry throat were independently associated with faster annual GFR decline (-1.1 ml/min/1.73m2, 95%CI: -1.9 to -0.2, p = 0.011). Conclusions: Add-on symptom-based diagnosis improves the predictive power on renal function decline among diabetic patients based on key biochemical and epidemiological factors. Dynamic change of symptoms should be considered in clinical practice and research design.

A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds

Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.

Systems Biology Approaches Reveal Potential Phenotype-Modifier Genes in Neurofibromatosis Type 1

Neurofibromatosis type (NF1) is a syndrome characterized by varied symptoms, ranging from mild to more aggressive phenotypes. The variation is not explained only by genetic and epigenetic changes in the NF1 gene and the concept of phenotype-modifier genes in extensively discussed in an attempt to explain this variability. Many datasets and tools are already available to explore the relationship between genetic variation and disease, including systems biology and expression data. To suggest potential NF1 modifier genes, we selected proteins related to NF1 phenotype and NF1 gene ontologies. Protein–protein interaction (PPI) networks were assembled, and network statistics were obtained by using forward and reverse genetics strategies. We also evaluated the heterogeneous networks comprising the phenotype ontologies selected, gene expression data, and the PPI network. Finally, the hypothesized phenotype-modifier genes were verified by a random-walk mathematical model. The network statistics analyses combined with the forward and reverse genetics strategies, and the assembly of heterogeneous networks, resulted in ten potential phenotype-modifier genes: AKT1, BRAF, EGFR, LIMK1, PAK1, PTEN, RAF1, SDC2, SMARCA4, and VCP. Mathematical models using the random-walk approach suggested SDC2 and VCP as the main candidate genes for phenotype-modifiers.