RESUMEN
Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network. Information on the traditional therapeutic use of the plants was integrated in the network in order to provide further evidence for the predicted biologically active natural compounds. We hereby present a number of examples where the traditional medicinal use of the plant matches with the medicinal use of the drug that is structurally similar to a plant component. With this approach, we have brought to light a number of obscure compounds of natural origin (e.g. kanugin, norruffscine, isoazadirolide) that could provide the basis and inspiration for further lead development. Apart from the identification of novel natural leads in drug discovery, we envisage that this integrated in silico ethnopharmacology approach could find applications in the elucidation of the molecular basis of Ayurveda medicine and in drug repurposing.
RESUMEN
UNLABELLED: To study the pathogenic importance of the rheumatoid factor (RF) in rheumatoid arthritis (RA) and to identify genes differentially expressed in patients and healthy individuals, total RNA was isolated from peripheral blood mononuclear cells (PBMC) from eight RF-positive and six RF-negative RA patients, and seven healthy controls. Gene expression of about 10,000 genes were examined using oligonucleotide-based DNA chip microarrays. The analyses showed no significant differences in PBMC expression patterns from RF-positive and RF-negative patients. However, comparisons of gene expression patterns from all fourteen RA patients and healthy controls identified a subset of discriminative genes. These results were validated by real time reverse transcription polymerase chain reaction (RT-PCR) on another group of RA patients and healthy controls. This confirmed that the following genes had a significantly higher expression in RA patients than in healthy controls: CD14 antigen, defensin alpha-1 and alpha-3 (DEFA), fatty-acid-Coenzyme A ligase, long-chain 2 (FACL), ribonuclease 2 (RNASE2), S100 calcium-binding protein A8 and A12 (S100A8 and S100A12). In contrast, the expression of MHC class II, DQ beta1 (HLA-DQB1) was significantly reduced in RA patients compared to healthy controls. CONCLUSIONS: With the analytical procedure employed, we did not find any indication that RF-positive and RF-negative RA are two fundamentally different diseases. Most of the genes discriminative between RA patients and healthy individuals are known to be involved in immunoinflammatory responses, especially those related to altered phagocytic functions.