Probing the binding affinity of amyloids to reduce toxicity of oligomers in diabetes.

MOTIVATION: Amyloids play a role in the degradation of ß-cells in diabetes patients. In particular, short amyloid oligomers inject themselves into the membranes of these cells and create pores that disrupt the strictly controlled flow of ions through the membranes. This leads to cell death. Getting rid of the short oligomers either by a deconstruction process or by elongating them into longer fibrils will reduce this toxicity and allow the ß-cells to live longer. RESULTS: We develop a computational method to probe the binding affinity of amyloid structures and produce an amylin analog that binds to oligomers and extends their length. The binding and extension lower toxicity and ß-cell death. The amylin analog is designed through a parsimonious selection of mutations and is to be administered with the pramlintide drug, but not to interact with it. The mutations (T9K L12K S28H T30K) produce a stable native structure, strong binding affinity to oligomers, and long fibrils. We present an extended mathematical model for the insulin-glucose relationship and demonstrate how affecting the concentration of oligomers with such analog is strictly coupled with insulin release and ß-cell fitness. AVAILABILITY AND IMPLEMENTATION: SEMBA, the tool to probe the binding affinity of amyloid proteins and generate the binding affinity scoring matrices and R-scores is available at: http://amyloid.cs.mcgill.ca

Complete characterization of the mutation landscape reveals the effect on amylin stability and amyloidogenicity.

Type-II diabetes is believed to be partially aggravated by the emergence of toxic amylin protein deposits in the extracellular space of the pancreas ß-cells. Amylin, the regulatory hormone that is co-secreted with insulin, has been observed to misfold into toxic structures. Pramlintide, an FDA approved injectable amylin analog mutated at positions 25, 28, and 29 was therefore developed to create a more stable, soluble, less-aggregating, and equipotent peptide that is used as an adjunctive therapy for diabetes. However, because Pramlintide is not ideal, researchers have been exploring other amylin analogs as therapeutic replacements. In this work, we assist the finding of optimal analogs by computationally revealing the mutational landscape of amylin. We computed the structure energies of all possible single-point mutations and studied the effect they have on amylin stability and amyloidogenicity. Each of the 37 amylin residues was mutated in silico into the 19 canonical amino acids and an energy function computing the Lennard-Jones, Coulomb and solvation energy was used to analyze changes in stability. The mutation landscape identified amylin's conserved stable regions, residues that can be tweaked to further stabilize structure, regions that are susceptible to mutations, and mutations that are amyloidogenic. We used the single-point mutational landscape data to generate estimations for higher-order multiple-point mutational landscapes and discovered millions of three-point mutations that are more stable and less amyloidogenic than Pramlintide. The landscapes provided an explanation for the effect of the S20G and Q10R mutations on the onset of diabetes of the Chinese and Maori populations, respectively.

Computational assembly of polymorphic amyloid fibrils reveals stable aggregates.

Amyloid proteins aggregate into polymorphic fibrils that damage tissues of the brain, nerves, and heart. Experimental and computational studies have examined the structural basis and the nucleation of short fibrils, but the ability to predict and precisely quantify the stability of larger aggregates has remained elusive. We established a complete classification of fibril shapes and developed a tool called CreateFibril to build such complex, polymorphic, modular structures automatically. We applied stability landscapes, a technique we developed to reveal reliable fibril structural parameters, to assess fibril stability. CreateFibril constructed HET-s, Aß, and amylin fibrils up to 17 nm in length, and utilized a novel dipolar solvent model that captured the effect of dipole-dipole interactions between water and very large molecular systems to assess their aqueous stability. Our results validate experimental data for HET-s and Aß, and suggest novel (to our knowledge) findings for amylin. In particular, we predicted the correct structural parameters (rotation angles, packing distances, hydrogen bond lengths, and helical pitches) for the one and three predominant HET-s protofilaments. We reveal and structurally characterize all known Aß polymorphic fibrils, including structures recently classified as wrapped fibrils. Finally, we elucidate the predominant amylin fibrils and assert that native amylin is more stable than its amyloid form. CreateFibril and a database of all stable polymorphic fibril models we tested, along with their structural energy landscapes, are available at http://amyloid.cs.mcgill.ca.

Leeno: Type 1 diabetes management training environment using smart algorithms.

A growing number of Type-1 Diabetes (T1D) patients globally use insulin pump technologies to monitor and manage their glucose levels. Although recent advances in closed-loop systems promise automated pump control in the near future, most patients worldwide still use open-loop continuous subcutaneous insulin infusion (CSII) devices which require close monitoring and continuous regulation. Apart from specialized diabetes units, hospital physicians and nurses generally lack necessary training to support the growing number of patients on insulin pumps. Most hospital staff and providers worldwide have never seen or operated an insulin pump device. T1D patients at nurseries, schools, in hospital emergency rooms, surgery theatres, and in-patient units all require close monitoring and active management. The lack of knowledge and necessary training to support T1D patients on pumps puts them at life-threatening risks. In this work, we develop a training simulation software for hospitals to educate and train their physicians and nurses on how to effectively operate a T1D pump and reduce hypoglycemia events. The software includes clinically validated T1D virtual patients that users can monitor and adjust their pump settings to improve glycemic outcomes. We develop a Fuzzy-Logic learning algorithm that helps guide users learn how to improve pump parameters for these patients. We recruited and trained 13 nurses on the software and report their improvement in pump administration, basal rates adjustments, and ICR modulation.

Unraveling the stability landscape of mutations in the SARS-CoV-2 receptor-binding domain.

The interaction between the receptor-binding domain (RBD) of the SARS-CoV-2 spike glycoprotein and the ACE2 enzyme is believed to be the entry point of the virus into various cells in the body, including the lungs, heart, liver, and kidneys. The current focus of several therapeutic design efforts explores attempts at affecting the binding potential between the two proteins to limit the activity of the virus and disease progression. In this work, we analyze the stability of the spike protein under all possible single-point mutations in the RBD and computationally explore mutations that can affect the binding with the ACE2 enzyme. We unravel the mutation landscape of the receptor region and assess the toxicity potential of single and multi-point mutations, generating insights for future vaccine efforts on mutations that might further stabilize the spike protein and increase its infectivity. We developed a tool, called SpikeMutator, to construct full atomic protein structures of the mutant spike proteins and shared a database of 3800 single-point mutant structures. We analyzed the recent 65,000 reported spike sequences across the globe and observed the emergence of stable multi-point mutant structures. Using the landscape, we searched through 7.5 million possible 2-point mutation combinations and report that the (R355D K424E) mutation produces one of the strongest spike proteins that therapeutic efforts should investigate for the sake of developing effective vaccines.

Development platform for artificial pancreas algorithms.

BACKGROUND AND AIMS: Assessing algorithms of artificial pancreas systems is critical in developing automated and fault-tolerant solutions that work outside clinical settings. The development and evaluation of algorithms can be facilitated with a platform that conducts virtual clinical trials. We present in this paper a clinically validated cloud-based distributed platform that supports the development and comprehensive testing of single and dual-hormone algorithms for type 1 diabetes mellitus (T1DM). METHODS: The platform is built on principles of object-oriented design and runs user algorithms in real-time virtual clinical trials utilizing a multi-threaded environment enabled by concurrent execution over a cloud infrastructure. The platform architecture isolates user algorithms located on personal machines from proprietary patient data running on the cloud. Users import a plugin into their algorithms (Matlab, Python, or Java) to connect to the platform. Once connected, users interact with a graphical interface to design experimental protocols for their trials. Protocols include trial duration in days, mealtimes and amounts, variability in mealtimes and amounts, carbohydrate counting errors, snacks, and onboard insulin levels. RESULTS: The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. Users can easily debug & modify code, test multiple strategies, and generate detailed clinical performance reports. We validated and integrated into the platform a glucoregulatory system of ordinary differential equations (ODEs) parameterized with clinical data to mimic the inter and intra-day variability of glucose responses of 15 T1DM patients. CONCLUSION: The platform utilizes the validated patient model to conduct virtual clinical trials for the rapid development and testing of closed-loop algorithms for T1DM.

Impact of erroneous meal insulin bolus with dual-hormone artificial pancreas using a simplified bolus strategy - A randomized controlled trial.

Postprandial glucose control remains challenging for patients with type 1 diabetes (T1D). A simplified meal bolus approach with a dual-hormone (insulin and glucagon) closed-loop system (DH-CLS) has been tested; yet, the impact of categorization errors with this strategy is unknown. The objective was to compare, in a randomized controlled inpatient trial, DH-CLS with the simplified meal bolus approach for two different meals properly categorized or overestimated. We tested, in patients with T1D, the simplified strategy with two standardized breakfasts (n = 10 per meal) adequately categorized or overestimated: (1) 75 g and (2) 45 g of carbohydrate. No difference was observed for percentage of time <4.0 mmol/L over a 4-hour post-meal period (primary outcome; median [IQR]: 0[0-0] vs. 0[0-0] for both comparisons, p = 0.47 and 0.31 for the 75 g and 45 g meals, respectively). Despite higher meal insulin boluses with overestimation for both meals (9.2 [8.2-9.6] vs. 8.1 [7.3-9.1] U and 8.4 [7.2-10.4] vs. 4.8 [3.7-5.6] U; p < 0.05), mean glycemia, percentage of time in target range and glucagon infusion did not differ. Additional scenarios were tested in silico with comparable results. These results suggest that the DH-CLS with a simplified meal bolus calculation is probably able to avoid hypoglycemia in the event of meal size misclassification.

Treatment of mild-to-moderate hypoglycemia in patients with type 1 diabetes treated with insulin pump therapy: are current recommendations effective?

AIMS: Mild-to-moderate hypoglycemia (blood glucose < 4.0 mmol/L) is recommended to be treated with 15 g of carbohydrates and to repeat the treatment if hypoglycemia persists after 15 min. This recommendation was established before intensive insulin therapy and based on studies using insulin with different pharmacokinetic profiles from actual insulin analogs showing that 15 g of glucose increases blood glucose by ~ 1.5 mmol/L in 15 min. We aimed to explore the effect of current recommended treatment of mild-to-moderate hypoglycemia in type 1 diabetes (T1D) participants and factors associated with treatment effectiveness. METHODS: This is a secondary analysis from three observational inpatient studies with a standardized and supervised treatment (16 g carbohydrates) of hypoglycemia (< 3.3 mmol/L with symptoms or < 3.0 mmol/L without symptom) in participants (47 adults-10 adolescents) with T1D using continuous subcutaneous insulin infusion ("insulin pumps"; CSII)). RESULTS: Twenty-seven participants presented a total of 48 hypoglycemia episodes treated by a single intake of 16 g of carbohydrates. Time required for normoglycemia recovery was 19.5 ± 12.0 min. The rise in plasma glucose following treatment was 0.85 ± 0.66 mmol/L in 15 min. Eighteen episodes (38%) were resolved (> 4.0 mmol/L) 15-min post-treatment. Glycemia at the time of treatment (< 3.2 mmol/L; p < 0.001) and a higher proportion of total daily insulin from basal doses (p = 0.03) were associated with a slower post-treatment plasma glucose rise. CONCLUSIONS: These results raise the possibility that sixteen grams of carbohydrates could be insufficient to treat a large proportion of hypoglycemia episodes in T1D patients treated with CSII. Factors affecting treatment effectiveness need to be investigated.