The use of high-throughput array technology is omnipresent in diverse areas specifically, early diagnosis of disease, discovery of
infectious agents, search for biological markers and screening of potential drug candidates. Here, we integrated gene expression
data with the network-based approach to identify novel genes that were playing central role in the network through
interconnecting to a number of differentially expressed breast cancer genes. The 62 cancerous genes retrieved from the Breast
Cancer Gene Database (BCGD) were mapped in the normalized data accessed from Stanford Microarray Database (SMD) to
analyze their pattern. Interaction networks for each gene were constructed to understand the biology of the metastasis at systems
level. The individual networks were fused together for the detection of interacting hubs, 38 novel genes were found to be deeply
intermingled with the central hub node. Gene Ontology studies were made to depict the biology of the hub nodes not alone
through gene ranking but by applying the Hyper geometric test with the Benjamini Hochberg False Discovery Rate (FDR)
correction method at a significance level of 0.05. Analyzing p-values from the statistical test indicated that most of the novel genes
were involved in the same biological function as the disordered genes like signal transducer, transcription regulator, enzyme
binding, molecular transducer and receptor signaling protein activity and same pathway as MAPK signaling, Apoptosis, Wnt
Signaling, ErbB signaling and Cell Cycle. Lastly, we identified 3 novel genes CHUK, INSR and CREBBP showing high connections
with the 12 novel genes reported in literatures as well with the perturbed genes. As a result, these genes can be considered as
significant finding in revealing the basis and pathways responsible for breast cancer.
Introduction: Porphyromonas Gingivalis (P. gingivalis) a primary periodontal disease pathogen. This bacterium affects sub-gingival tissue and leads to loss of teeth and alveolar bone destruction in the acute stage. In recent years, P. gingivalis is often connected with other diseases such as rheumatoid arthritis, diabetes, Alzheimer’s, and heart disease, though the aetiology is still unclear.
Objective: The use of commonly available drugs to treat periodontitis results in various side effects, in particular multi-drug resistant strains. As the development of multidrugresistant strains frequently urges the identification of novel drug targets, the aim of this study is to identify specific targets in the narrow spectrum to combat oral pathogens.
Methodology: This study used a comparative and subtractive pathway analysis approach to identify potential drug targets specific to P. gingivalis.
Results: The in-silico comparison of the P. gingivalis and Homo sapiens (H. sapiens) metabolic pathways resulted in 13 unique pathogen pathways. A homology search of the 67 enzymes in the unique bacterial pathway using the BLASTp program against the Homo sapiens proteome resulted in fifteen possible targets that are non-homologous to the human proteome. Thirteen genes among 15 potent target encoders are key DEG genes indispensable for P. gingivalis’s survival. A comprehensive analysis of the literature identified three potential therapeutic drug targets.
Conclusions: The three most relevant drug targets are Arabinose-5-phosphate isomerase, UDP-2,3-diacylglucosamine hydrolase, and Undecaprenyl diphosphatase. Upon corroboration, these targets may give rise to narrow-spectrum antibiotics that can specificallytreat thedental infection.
Bangladesh Journal of Medical Science Vol.20(4) 2021 p.887-896
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