Future directions in obesity pharmacotherapy

There is a growing unmet need for more effective treatment of obesity and its complications. While current anti-obesity medications are effective and offer real clinical benefits over diet and lifestyle interventions, they cannot meet the levels of efficacy and reduction of hard endpoint outcomes seen with bariatric surgery. As knowledge on the control of body weight unravels, the complexity of this physiology opens the opportunity to new druggable targets. Currently, gut peptide analogues such as semaglutide, a glucagon like peptide-1 (GLP-1) receptor agonist, and the dual agonist GLP-1 and gastric inhibitory polypeptide (GIP) tirzepatide are the furthest advanced in clinical development and seem likely to meet current regulatory requirements within the next year or so. However, current regulatory requirements are out of step with the efficacy of new compounds and concepts relating to obesity and its complications. Many other drugs in early development will target different pathways of energy balance, raising the possibility of drug combinations to maximise efficacy as for other chronic disease such as hypertension and diabetes. This will allow more complex and personalised guidelines to evolve.

Evolutionary Adaptation and Amyloid Formation: Does the Reduced Amyloidogenicity and Cytotoxicity of Ursine Amylin Contribute to the Metabolic Adaption of Bears and Polar Bears?

Much of our knowledge of diabetes is derived from studies of rodent models. An alternative approach explores evolutionary solutions to physiological stress by studying organisms that face challenging metabolic environments. Polar bears eat an enormously lipid-rich diet without deleterious metabolic consequences. In contrast, transgenic rodents expressing the human neuropancreatic polypeptide hormone amylin develop hyperglycemia and extensive pancreatic islet amyloid when fed a high fat diet. The process of islet amyloid formation by human amylin contributes to β-cell dysfunction and loss of β-cell mass in type-2 diabetes. We show that ursine amylin is considerably less amyloidogenic and less toxic to β-cells than human amylin, consistent with the hypothesis that part of the adaptation of bears to metabolic challenges might include protection from islet amyloidosis-induced β-cell toxicity. Ursine and human amylin differ at four locations: H18R, S20G, F23L, and S29P. These are interesting from a biophysical perspective since the S20G mutation accelerates amyloid formation but the H18R slows it. An H18RS20G double mutant of human amylin behaves similarly to the H18R mutant, indicating that the substitution at position 18 dominates the S20G replacement. These data suggest one possible mechanism underpinning the protection of bears against metabolic challenges and provide insight into the design of soluble analogs of human amylin.

Thioredoxin-interacting protein promotes islet amyloid polypeptide expression through miR-124a and FoxA2

Thioredoxin-interacting protein (TXNIP) is up-regulated by glucose and diabetes and plays a critical role in glucotoxicity, inflammation, and beta-cell apoptosis, whereas we have found that TXNIP deficiency protects against diabetes. Interestingly, human islet amyloid polypeptide (IAPP) is also induced by glucose, aggregates into insoluble amyloid fibrils found in islets of most individuals with type 2 diabetes and promotes inflammation and beta-cell cytotoxicity. However, so far no connection between TXNIP and IAPP signaling had been reported. Using TXNIP gain and loss of function experiments, INS-1 beta-cells and beta-cell-specific Txnip knock-out mice, we now found that TXNIP regulates IAPP expression. Promoter analyses and chromatin-immunoprecipitation assays further demonstrated that TXNIP increases IAPP expression at the transcriptional level, and we discovered that TXNIP-induced FoxA2 (forkhead box A2) transcription factor expression was conferring this effect by promoting FoxA2 enrichment at the proximal FoxA2 site in the IAPP promoter. Moreover, we found that TXNIP down-regulates miR-124a expression, a microRNA known to directly target FoxA2. Indeed, miR-124a overexpression led to decreased FoxA2 expression and IAPP promoter occupancy and to a significant reduction in IAPP mRNA and protein expression and also effectively inhibited TXNIP-induced IAPP expression. Thus, our studies have identified a novel TXNIP/miR-124a/FoxA2/IAPP signaling cascade linking the critical beta-cell signaling pathways of TXNIP and IAPP and thereby provide new mechanistic insight into an important aspect of transcriptional regulation and beta-cell biology.

Computational approach for protein structure prediction

Objectives

To predict the structure of protein, which dictates the function it performs, a newly designed algorithm is developed which blends the concept of self-organization and the genetic algorithm.

Methods

Among many other approaches, genetic algorithm is found to be a promising cooperative computational method to solve protein structure prediction in a reasonable time. To automate the right choice of parameter values the influence of self-organization is adopted to design a new genetic operator to optimize the process of prediction. Torsion angles, the local structural parameters which define the backbone of protein are considered to encode the chromosome that enhances the quality of the confirmation. Newly designed self-configured genetic operators are used to develop self-organizing genetic algorithm to facilitate the accurate structure prediction.

Results

Peptides are used to gauge the validity of the proposed algorithm. As a result, the structure predicted shows clear improvements in the root mean square deviation on overlapping the native indicates the overall performance of the algorithm. In addition, the Ramachandran plot results implies that the conformations of phi-psi angles in the predicted structure are better as compared to native and also free from steric hindrances.

Conclusions

The proposed algorithm is promising which contributes to the prediction of a native-like structure by eliminating the time constraint and effort demand. In addition, the energy of the predicted structure is minimized to a greater extent, which proves the stability of protein.