Synthesis of AgNPs coated with secondary metabolites of Acacia nilotica: An efficient antimicrobial and detoxification agent for environmental toxic organic pollutants.

This study concentrates on biosynthesis of Silver Nanoparticles (AgNPs) from stem extract of Acacia nilotica (A. nilotica). The reaction was completed at a temperature ~40-45 °C and time duration of 5 h. AgNPs were thoroughly investigated via advanced characterization techniques such as UV-Vis spectrophotometry (UV-Vis), Fourier Transform Infrared spectroscopy (FTIR), X-ray Diffractometry (XRD), Field Emission Scanning Electron Microscopy (FESEM), High Resolution Transmission Electron Microscopy (HRTEM), X-ray Photoelectron Spectroscopy (XPS), Thermo Gravimetric Analysis (TGA), Diffuse Reflectance Spectroscopy (DRS), Brunner-Emmett-Teller (BET), Dynamic Light Scattering (DLS), and Zeta potential analysis. AgNPs with average size below 50 nm were revealed by all the measuring techniques. Maximum surface area ~5.69 m2/g was reported for the as synthesized NPs with total pore volume ~0.0191 mL/g and average pore size ~1.13 nm. Physical properties such as size and shape have changed the surface plasmon resonance peak in UV-visible spectrum. Antimicrobial activity was reported due to denaturation of microbial ribosome's sulphur and phosphorus bond by silver ions against bacterium Methicillin Resistant Staphylococcus aureus (MRSA) and fungus Candida Albican (CA). Furthermore, AgNPs degraded toxic pollutants such as 4-nitrophenol (4-NP), 2-nitrophenol (2-NP) and various hazardous dyes such as Congo Red (CR), Methylene Blue (MB) and Methyl Orange (MO) up to 95%. The present work provided low cost, green and an effective way for synthesis of AgNPs which were utilized as potential antimicrobial agents as well as effective catalyst for detoxification of various pollutants and dyes.

Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network.

Obesity is connected to the activation of chronic inflammatory pathways in both adipocytes and macrophages located in adipose tissues. The nuclear factor (NF)-κB is a central molecule involved in inflammatory pathways linked to the pathology of different complex metabolic disorders. Investigating the gene expression data in the adipose tissue would potentially unravel disease relevant gene interactions. The present study is aimed at creating a signature molecular network and at prioritizing the potential biomarkers interacting with NF-κB family of proteins in obesity using system biology approaches. The dataset GSE88837 associated with obesity was downloaded from Gene Expression Omnibus (GEO) database. Statistical analysis represented the differential expression of a total of 2650 genes in adipose tissues (p = <0.05). Using concepts like correlation, semantic similarity, and theoretical graph parameters we narrowed down genes to a network of 23 genes strongly connected with NF-κB family with higher significance. Functional enrichment analysis revealed 21 of 23 target genes of NF-κB were found to have a critical role in the pathophysiology of obesity. Interestingly, GEM and PPP1R13L were predicted as novel genes which may act as potential target or biomarkers of obesity as they occur with other 21 target genes with known obesity relationship. Our study concludes that NF-κB and prioritized target genes regulate the inflammation in adipose tissues through several molecular signaling pathways like NF-κB, PI3K-Akt, glucocorticoid receptor regulatory network, angiogenesis and cytokine pathways. This integrated system biology approaches can be applied for elucidating functional protein interaction networks of NF-κB protein family in different complex diseases. Our integrative and network-based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for obesity.

Dissecting the Role of NF-κb Protein Family and Its Regulators in Rheumatoid Arthritis Using Weighted Gene Co-Expression Network.

Rheumatoid arthritis (RA) is a chronic synovial autoinflammatory disease that destructs the cartilage and bone, leading to disability. The functional regulation of major immunity-related pathways like nuclear factor kappa B (NF-κB), which is involved in the chronic inflammatory reactions underlying the development of RA, remains to be explored. Therefore, this study has adopted statistical and knowledge-based systemic investigations (like gene correlation, semantic similarity, and topological parameters based on graph theory) to study the gene expression status of NF-κB protein family (NKPF) and its regulators in synovial tissues to trace the molecular pathways through which these regulators contribute to RA. A complex protein-protein interaction map (PPIM) of 2,742 genes and 37,032 interactions was constructed from differentially expressed genes (p ≤ 0.05). PPIM was further decomposed into a Regulator Allied Protein Interaction Network (RAPIN) based on the interaction between genes (5 NKPF, 31 seeds, 131 hubs, and 652 bottlenecks). Pathway network analysis has shown the RA-specific disturbances in the functional connectivity between seed genes (RIPK1, ATG7, TLR4, TNFRSF1A, KPNA1, CFLAR, SNW1, FOSB, PARVA, CX3CL1, and TRPC6) and NKPF members (RELA, RELB, NFKB2, and REL). Interestingly, these genes are known for their involvement in inflammation and immune system (signaling by interleukins, cytokine signaling in immune system, NOD-like receptor signaling, MAPK signaling, Toll-like receptor signaling, and TNF signaling) pathways connected to RA. This study, for the first time, reports that SNW1, along with other NK regulatory genes, plays an important role in RA pathogenesis and might act as potential biomarker for RA. Additionally, these genes might play important roles in RA pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative data analysis and network-based methods could accelerate the identification of novel drug targets for RA from high-throughput genomic data.