Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks.

The Beta-lactamase protein family is vital in countering Beta-lactam antibiotics, a widely used antimicrobial. To enhance our understanding of this family, we adopted a novel approach employing a multiplex network representation of its multiple sequence alignment. Each network layer, derived from the physiochemical properties of amino acids, unveils distinct insights into the intricate interactions among nodes, thereby enabling the identification of key motifs. Nodes with identical property signs tend to aggregate, providing evidence of the presence of consequential functional and evolutionary constraints shaping the Beta-lactamase family. We further investigate the distribution of evolutionary links across various layers. We observe that polarity manifests the highest number of unique links at lower thresholds, followed by hydrophobicity and polarizability, wherein hydrophobicity exerts dominance at higher thresholds. Further, the combinations of polarizability and volume, exhibit multiple simultaneous connections at all thresholds. The combination of hydrophobicity, polarizability, and volume uncovers shared links exclusive to these layers, implying substantial evolutionary impacts that may have functional or structural implications. By assessing the multi-degree of nodes, we unveil the hierarchical influence of properties at each position, identifying crucial properties responsible for the protein's functionality and providing valuable insights into potential targets for modulating enzymatic activity.

Targeting functional motifs of a protein family.

The structural organization of a protein family is investigated by devising a method based on the random matrix theory (RMT), which uses the physiochemical properties of the amino acid with multiple sequence alignment. A graphical method to represent protein sequences using physiochemical properties is devised that gives a fast, easy, and informative way of comparing the evolutionary distances between protein sequences. A correlation matrix associated with each property is calculated, where the noise reduction and information filtering is done using RMT involving an ensemble of Wishart matrices. The analysis of the eigenvalue statistics of the correlation matrix for the ß-lactamase family shows the universal features as observed in the Gaussian orthogonal ensemble (GOE). The property-based approach captures the short- as well as the long-range correlation (approximately following GOE) between the eigenvalues, whereas the previous approach (treating amino acids as characters) gives the usual short-range correlations, while the long-range correlations are the same as that of an uncorrelated series. The distribution of the eigenvector components for the eigenvalues outside the bulk (RMT bound) deviates significantly from RMT observations and contains important information about the system. The information content of each eigenvector of the correlation matrix is quantified by introducing an entropic estimate, which shows that for the ß-lactamase family the smallest eigenvectors (low eigenmodes) are highly localized as well as informative. These small eigenvectors when processed gives clusters involving positions that have well-defined biological and structural importance matching with experiments. The approach is crucial for the recognition of structural motifs as shown in ß-lactamase (and other families) and selectively identifies the important positions for targets to deactivate (activate) the enzymatic actions.

Correlation and network analysis of global financial indices.

Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

Comparison of glucosamine sulfate and a polyherbal supplement for the relief of osteoarthritis of the knee: a randomized controlled trial [ISRCTN25438351].

BACKGROUND: The efficacy and safety of a dietary supplement derived from South American botanicals was compared to glucosamine sulfate in osteoarthritis subjects in a Mumbai-based multi-center, randomized, double-blind study. METHODS: Subjects (n = 95) were screened and randomized to receive glucosamine sulfate (n = 47, 1500 mg/day) or reparagen (n = 48, 1800 mg/day), a polyherbal consisting of 300 mg of vincaria (Uncaria guianensis) and 1500 mg of RNI 249 (Lepidium meyenii) administered orally, twice daily. Primary efficacy variable was response rate based on a 20% improvement in WOMAC pain scores. Additional outcomes were WOMAC scores for pain, stiffness and function, visual analog score (VAS) for pain, with assessments at 1, 2, 4, 6 and 8 weeks. Tolerability, investigator and subject global assessments and rescue medication consumption (paracetamol) were measured together with safety assessments including vital signs and laboratory based assays. RESULTS: Subject randomization was effective: age, gender and disease status distribution was similar in both groups. The response rates (20% reduction in WOMAC pain) were substantial for both glucosamine (89%) and reparagen (94%) and supported by investigator and subject assessments. Using related criteria response rates to reparagen were favorable when compared to glucosamine. Compared to baseline both treatments showed significant benefits in WOMAC and VAS outcomes within one week (P < 0.05), with a similar, progressive improvement over the course of the 8 week treatment protocol (45-62% reduction in WOMAC or VAS scores). Tolerability was excellent, no serious adverse events were noted and safety parameters were unchanged. Rescue medication use was significantly lower in the reparagen group (p < 0.01) at each assessment period. Serum IGF-1 levels were unaltered by treatments. CONCLUSION: Both reparagen and glucosamine sulfate produced substantial improvements in pain, stiffness and function in subjects with osteoarthritis. Response rates were high and the safety profile was excellent, with significantly less rescue medication use with reparagen. Reparagen represents a new natural productive alternative in the management of joint health. TRIAL REGISTRATION: Current Controlled Trials ISRCTN25438351.