OBJECTIVES: Therefore, the objective of this review was twofold; to identify and critically evaluate anti-depressant properties of medicinal plants or those incorporated in traditional medicine; and to discuss their possible mechanism of action as well as challenges and way forward for this alternative treatment approach.
METHODS: Relevant research articles were retrieved from various databases, including Scopus, PubMed, and Web of Science, for the period from 2018 to 2020, and the search was updated in September 2024. The inclusion criterion was relevance to antidepressants, while the exclusion criteria included duplicates, lack of full-text availability, and non-English publications.
RESULTS: Through an extensive literature review, more than 40 medicinal plant species with antidepressant effects were identified, some of which are part of traditional medicine. The list of the said plant species included Albizia zygia (DC.) J.F.Macbr., Calculus bovis Sativus, Celastrus paniculatus Willd., Cinnamomum sp., Erythrina velutina Willd., Ficus platyphylla Delile, Garcinia mangostana Linn., Hyptis martiusii Benth, and Polygonum multiflorum Thunb. Anti-depressant mechanisms associated with those plants were further characterised based on their modes of action such as anti-oxidation system, anti-inflammation action, modulation of various neurotransmitters, neuroprotective effect, the regulation of hypothalamic-pituitary-adrenal (HPA) axis and anti-depressant mechanism. The challenges and future outlook of this alternative and complementary medicine are also explored and discussed.
CONCLUSION: This pool of identified plant species is hoped to offer health care professionals the best possible alternatives of anti-depressants from natural phytocompounds that are efficacious, safe and affordable for applications in future clinical settings.
METHOD: In this study, Rad50 mutations were retrieved from SNPeffect 4.0 database and literature. Each of the mutations was analyzed using various bioinformatic analyses such as PredictSNP, MutPred, SNPeffect 4.0, I-Mutant and MuPro to identify its impact on molecular mechanism, biological function and protein stability, respectively.
RESULTS: We identified 103 mostly occurred mutations in the Rad50 protein domains and motifs, which only 42 mutations were classified as most deleterious. These mutations are mainly situated at the specific motifs such as Walker A, Q-loop, Walker B, D-loop and signature motif of the Rad50 protein. Some of these mutations were predicted to negatively affect several important functional sites that play important roles in DNA repair mechanism and cell cycle signaling pathway, highlighting Rad50 crucial role in this process. Interestingly, mutations located at non-conserved regions were predicted to have neutral/non-damaging effects, in contrast with previous experimental studies that showed deleterious effects. This suggests that software used in this study may have limitations in predicting mutations in non-conserved regions, implying further improvement in their algorithm is needed. In conclusion, this study reveals the priority of acid substitution associated with the genetic disorders. This finding highlights the vital roles of certain residues such as K42E, C681A/S, CC684R/S, S1202R, E1232Q and D1238N/A located in Rad50 conserved regions, which can be considered for a more targeted future studies.
Methods: The HCPCA chemical structure was determined using nuclear magnetic resonance spectroscopy. We conducted whole genome sequencing for the identification of the gene cluster(s) believed to be responsible for phenazine biosynthesis in order to map its corresponding pathway, in addition to bioinformatics analysis to assess the potential of S. kebangsaanensis in producing other useful secondary metabolites.
Results: The S. kebangsaanensis genome comprises an 8,328,719 bp linear chromosome with high GC content (71.35%) consisting of 12 rRNA operons, 81 tRNA, and 7,558 protein coding genes. We identified 24 gene clusters involved in polyketide, nonribosomal peptide, terpene, bacteriocin, and siderophore biosynthesis, as well as a gene cluster predicted to be responsible for phenazine biosynthesis.
Discussion: The HCPCA phenazine structure was hypothesized to derive from the combination of two biosynthetic pathways, phenazine-1,6-dicarboxylic acid and 4-methoxybenzene-1,2-diol, originated from the shikimic acid pathway. The identification of a biosynthesis pathway gene cluster for phenazine antibiotics might facilitate future genetic engineering design of new synthetic phenazine antibiotics. Additionally, these findings confirm the potential of S. kebangsaanensis for producing various antibiotics and secondary metabolites.