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Didronel (Etidronate Disodium)- Multum

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Apart from these, there are some reactions where a molecule regulates the reaction between other molecules, i. This process is often termed as reverse engineering and several approaches have been described to identify modules.

We used different methods to identify community structures (modules) in obesity network. In addition, we clustered genes based upon tissue emotional eating expression data.

Hence, the constructed network was divided into 5 modules based upon physiological processes and likely anatomical component Didronel (Etidronate Disodium)- Multum 3). In the following section, we attempt to relate modules with disease conditions (Fig 5).

The columns show pre-dominant hub, likely anatomical component and physiological process with the connectivity degree of major molecules. They include leptin, ghrelin and dopamine. Leptin is one of the highly studied molecules in obesity after insulin (present in7.

Leptin acts as a satiety factor and its discovery has paved the way for the study of adipocyte derived factor in energy balance homeostasis. Ghrelin act as an endogenous ligand for growth hormone secretagogue receptor (GHSR). Module 2 primarily encapsulate insulin and its interactions with other molecules, for instance, apolipoprotein A-V (APOA5), forkhead box C2 (FOXC2), macrophage migration inhibitory factor (MIF), uncoupling protein (UCP) and v-akt murine thymoma viral oncogene homolog 2 (AKT2).

This module builds a link between tightly coupled clinical conditions- obesity and type 2 diabetes. The third module maps interactions, catalysis and processing of molecules involved in lipid metabolism, including acetyl CoA, aspartate, mevalonate, cholesterol, cholic acid, and diacylglycerol.

Module 4: It is the largest module in the network and majorly consists of transcription factors involved in adipose tissue differentiation and other biological activities in humans. To understand the properties of constructed network, we computed several topological parameters as described below (See File B in S1 File for detailed Didronel (Etidronate Disodium)- Multum. Null model 1- In this model, we randomised the edges but kept the node labels and their degrees intact.

For example, the connection (edge) between Didronel (Etidronate Disodium)- Multum A (leptin) and gene B (leptin receptor) is deleted.

Null Model 2- Didronel (Etidronate Disodium)- Multum shuffle the positions Didronel (Etidronate Disodium)- Multum nodes by keeping the global degree distribution of the comprehensive map intact. Null Model 3- This is generated by shuffling both the position of nodes as well as their edges (See File Taylor johnson in S1 File).

To see the effect of properties on size of the network, we construct networks with node numbers from 100 to 1000. Remaining nodes (n-s) are added one at a time, and connected to existing nodes (m) randomly. The resulting network is found to follow power-law degree distribution.

In addition, we generated randomized networks smart random network module of cytoscape. The obesity network (true network) exhibit different properties when compared to 18 control randomized networks obtained by shuffling the obesity network associations while keeping the degree distribution of nodes fixed (Fig G and Fig H in S1 File). We find that clustering coefficient increases from 0 (in true network) to 0.

See Fig G in S1 File). This pattern is reversed in case of mean shortest path, which reduced from 18 to 11 units (See Fig H in S1 File). We have also enclosed additional information for results generated during shuffling procedure in the Table H in S2 File and website in S3 File.

To see the robustness of network and its dependence on failure of a particular node, we randomly deleted nodes and computed properties for the remaining network. There are several indexes of network centrality such as degree, eccentricity, closeness, betweenness, stress, centroid and radiality which allow Didronel (Etidronate Disodium)- Multum the topological relevance of single nodes in a network.

Recently introduced parameters such as node interference and robustness were also included in the analysis. Since, obesity network shows fear of the dark free structure with presence of hubs, we started our deletion experiments by sequential deletion of hub nodes to see the effect on network robustness.

This was achieved by removing a node and calculating the interference on the centrality of the remaining nodes using centiscape plugin of cytoscape. We find that removal of hubs alone or in combination impact the network Didronel (Etidronate Disodium)- Multum. We find that various critical properties of network changes to significant extent.

In biological process, the sub-category- cellular process comprises 80. The gene list Didronel (Etidronate Disodium)- Multum obtained for three possible conditions: up-regulation, down regulation and non-differentially expressed. Subsequently, we found 2,135 genes (labelled as D) as down-regulated group and a very large number of genes (2,91,407) as non-differentially expressed (NDE).

After removal of redundancy, we obtained 1,340 molecules as up-regulated (U), 918 molecules as down-regulated (D), and 38,434 molecules as non-differentially expressed (NDE) molecules as a filtered sets. Thereafter, we compared filtered dataset obtained from microarray database with our list. Based upon comparisons, we found that 27 genes (obtained from deep curation approach (DC)) are up-regulated in obesity whereas 24 genes show down-regulation and large Didronel (Etidronate Disodium)- Multum of genes did not show any change Didronel (Etidronate Disodium)- Multum Zurampic (Zurampic Lesinurad Tablets)- FDA or Didronel (Etidronate Disodium)- Multum is not available in the database.

Using gene ontology analysis, it was revealed that most of the up-regulated genes are involved in protein binding and down-regulated group are involved in steroid binding activity (See File D in S1 File). We found that 34. To check whether orlistat produces its clinical effect (of weight reduction) possibly due to preferential binding to several molecules listed in the obesity network (N) than any other part of proteome, we created a dataset of 24,000 known human protein structures (P) and docked orlistat against them.

In addition, we created datasets of randomly selected protein structures from P labelled as P1, P2Pn as controls. We observed that the distribution of binding energies obtained from controls (P1, P2, P3Pn) and Alzheimer disease network(D) is significantly different from test dataset(N) (P value In another experiment, we docked drugs (which do not have effect on obesity) against the obesity network proteins.

For instance, Didronel (Etidronate Disodium)- Multum used Acetylsalicylic acid (selected randomly; anti-inflammatory medicine) to dock against the obesity network proteins. Apart from that, we used drugs, showing comparable tanimoto co-efficient to orlistat, such as 3-Carboxy-N,N,N-Trimethyl-2-(Octanoyloxy) Propan-1-Aminium (Tc Value: 0. We detected that the binding energy profiles of the above mentioned drugs against the obesity network proteins are different from that of orlistat (P value Orlistat is known Didronel (Etidronate Disodium)- Multum produce several side-effects namely acne, respiratory tract infection, urinary tract infection and nausea, possibly due to binding to off targets perturbing unrelated pathway.

On comparison, we found that several molecules are common in obesity Didronel (Etidronate Disodium)- Multum and acne (14 molecules; 2.

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