Amyloidogenicity of naturally occurring full-length animal IAPP variants

Misfolded protein oligomers: mechanisms of formation, cytotoxic effects, and pharmacological approaches against protein misfolding diseases

The conversion of native peptides and proteins into amyloid aggregates is a hallmark of over 50 human disorders, including Alzheimer's and Parkinson's diseases. Increasing evidence implicates misfolded protein oligomers produced during the amyloid formation process as the primary cytotoxic agents in many of these devastating conditions. In this review, we analyze the processes by which oligomers are formed, their structures, physicochemical properties, population dynamics, and the mechanisms of their cytotoxicity. We then focus on drug discovery strategies that target the formation of oligomers and their ability to disrupt cell physiology and trigger degenerative processes.

Analysis of Sheep and Goat IAPP Provides Insight into IAPP Amyloidogenicity and Cytotoxicity

Human islet amyloid polypeptide (hIAPP) plays a role in glucose regulation but forms pancreatic amyloid deposits in type 2 diabetes, and that process contributes to β-cell dysfunction. Not all species develop diabetes, and not all secrete an IAPP that is amyloidogenic in vitro under normal conditions, a perfect correlation currently exists between both. Studies of IAPPs from such organisms can provide clues about the high amyloidogenicity of hIAPP and can inform the design of soluble analogues of hIAPP. Sheep and goat IAPP are among the most divergent from hIAPP, with 13 and 11 substitutions, respectively, including an unusual Tyr to His substitution at the C-terminus. The properties of sheep and goat IAPP were examined in solution and in the presence of anionic vesicles, resulting in no observed amyloid formation, even at increased concentrations. Furthermore, both peptides are considerably less toxic to cultured β-cells than hIAPP. The effect of the Y37H replacements was studied in the context of hIAPP, as was a Y37R substitution. Buffer- and salt-dependent effects were observed. There was little impact on the time to form amyloid in phosphate-buffered saline; however, a significant deceleration was observed in Tris buffer, and amyloid formation was slower in the absence of added salt. The Y37H substitution had little impact on toxicity, while the Y37R replacement led to a 30% decrease in toxicity compared with that of hIAPP. The implications for the amyloidogenicity of hIAPP and the design of soluble analogues of the human peptide are discussed.

Differential Effects of Aromatic Residues on Amyloid Formation and Cytotoxicity of Human IAPP

Islet amyloid polypeptide (IAPP) is a 37-residue polypeptide hormone secreted by the pancreatic β-cells. IAPP plays a role in glycemic regulation, but in the pre-type-2 diabetic state, it aggregates to form an islet amyloid. The process of islet amyloid formation contributes to β-cell dysfunction and disease progression. The features of the IAPP sequence that modulate amyloid formation are still not understood. Human IAPP contains three aromatic residues, F15, F23, and Y37. F15 and Y37 are highly conserved, while F23 is more commonly a Leu or Ile in other species. The role of the aromatic residues in modulating the time course of amyloid formation and the cytotoxicity was examined using aromatic to Leu mutations. All three single and double mutants and the triple mutant were studied. F23 plays a dominant role in both amyloid formation and toxicity. An F15L mutant accelerated amyloid formation, a Y37L mutant had little effect, while an F23L replacement slowed amyloid formation by a factor of 2.6. Double mutants, which contained an F23L replacement, had a larger effect than those that did not, and there are non-additive effects between pairs of aromatic residues. F23 also plays a key role in toxicity. Single or multiple mutants that contain the F23L replacement were noticeably less toxic than the wild-type or mutants which did not include the F23L substitution. In contrast, the F15L mutant was more toxic than the wild-type one. The implications for IAPP amyloid formation and for the design of non-aggregating analogues of IAPP are discussed.

Recent Advances in the Discovery of Therapeutics to Curtail Islet Amyloid Polypeptide Aggregation for Type 2 Diabetes Treatment

In humans with type 2 diabetes, at least 70% of patients exhibit islet amyloid plaques formed by misfolding islet amyloid polypeptides (IAPP). The oligomeric conformation and accumulation of the IAPP plaques lead to a panoply of cytotoxic effects on the islet β-cells. Currently, no marketed therapies for the prevention or elimination of these amyloid deposits exist, and therefore significant efforts are required to address this gap. To date, most of the experimental treatments are limited to only in vitro stages of testing. In general, the proposed therapeutics use various targeting strategies, such as binding to the N-terminal region of islet amyloid polypeptide on residues 1-19 or the hydrophobic region of IAPP. Other strategies include targeting the peptide self-assembly through π-stacking. These methods are realized by using several different families of compounds, four of which are highlighted in this review: naturally occurring products, small molecules, organometallic compounds, and nanoparticles. Each of these categories holds immense potential to optimize and develop inhibitor(s) of pancreatic amyloidosis in the near future.

Antidiabetic-activity sulfated polysaccharide from Chaetomorpha linum: Characteristics of its structure and effects on oxidative stress and mitochondrial function

A water-soluble polysaccharide from the green alga Chaetomorpha linum, designated CHS2, was obtained by water extraction, preparative anion-exchange and size-exclusion chromatography. Results of chemical and spectroscopic analyses showed that CHS2 was a sulfated rhamnogalactoarabinan, and its backbone was mainly constituted by 4-linked and 3,4-linked β-l-arabinopyranose with sulfate groups at C-2/C-3 of 4-linked β-l-arabinopyranose. The branching contained 4-linked, 6-linked β-d-galactopyranose and terminal rhamnose residues. Based on the inhibition of human islet amyloid polypeptide (hIAPP) aggregation and morphology change of hIAPP aggregates in in vitro tests, it was proved that CHS2 effectively inhibited the hIAPP aggregation and possessed strong antidiabetic activity. CHS2 was nearly no toxicity in NIT-1 cells and could attenuate hIAPP-induced cytotoxicity. CHS2 may significantly reduce the generation of intracellular reactive oxygen species and hIAPP aggregation-induced oxidative stress in NIT-1 cells. CHS2 was co-localized with mitochondria, and largely protected mitochondria function from hIAPP aggregation-induced damage through stabilizing mitochondrial membrane potential and enhancing the mitochondrial complex I, II or III activity and ATP level. The data demonstrated that CHS2 could have potential prospect to become an antidiabetic drug for type 2 diabetes mellitus treatment.

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.

Linking hIAPP misfolding and aggregation with type 2 diabetes mellitus: a structural perspective

There are over 40 identified human disorders that involve certain proteins folding incorrectly, accumulating in the body causing damage to cells and organs and causing disease. Type 2 Diabetes Mellitus (T2DM) is one of these protein misfolding disorders (PMDs) and involves human islet amyloid polypeptide (hIAPP) misfolding and accumulating in parts of the body, primarily in the pancreas, causing damage to islet cells and affecting glucose regulation. In this review, we have summarised our current understanding of what causes hIAPP to misfold, what conformations are found in different parts of the body with a particular focus on what is known about the structure of hIAPP and how this links to T2DM. Understanding the molecular basis behind these misfolding events is essential for understanding the role of hIAPP to develop better therapeutics since type 2 diabetes currently affects over 4.9 million people in the United Kingdom alone and is predicted to increase as our population ages.

Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets

Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

Nonhuman IAPP Variants Inhibit Human IAPP Aggregation

AIM

To identify naturally occurring variants of IAPP capable of inhibiting the aggregation of human IAPP and protecting living cells from the toxic effects of human IAPP.

BACKGROUND

The loss of insulin-producing β-cells and the overall progression of type 2 diabetes appears to be linked to the formation of toxic human IAPP (hIAPP, Islet Amyloid Polypeptide, amylin) amyloid in the pancreas. Inhibiting the initial aggregation of hIAPP has the potential to slow, if not stop entirely, the loss of β-cells and halt the progression of the disease.

OBJECTIVE

To identify and characterize naturally occurring variants of IAPP capable of inhibiting human IAPP aggregation.

METHODS

Synthetic human IAPP was incubated with synthetic IAPP variants identified from natural sources under conditions known to promote amyloid-based aggregation. To identify IAPP variants capable of inhibiting human IAPP aggregation, Thioflavin T-binding fluorescence, atomic force microscopy, and cell-rescue assays were performed.

RESULTS

While most IAPP variants showed little to no ability to inhibit human IAPP aggregation, several variants showed some ability to inhibit aggregation, with two variants showing substantial inhibitory potential.

CONCLUSION

Several naturally occurring IAPP variants capable of inhibiting human IAPP aggregation were identified and characterized.

The Role of Glycation on the Aggregation Properties of IAPP

Epidemiological evidence shows an increased risk for developing Alzheimer's disease in people affected by diabetes, a pathology associated with increased hyperglycemia. A potential factor that could explain this link could be the role that sugars may play in both diseases under the form of glycation. Contrary to glycosylation, glycation is an enzyme-free reaction that leads to formation of toxic advanced glycation end-products (AGEs). In diabetes, the islet amyloid polypeptide (IAPP or amylin) is found to be heavily glycated and to form toxic amyloid-like aggregates, similar to those observed for the Aβ peptides, often also heavily glycated, observed in Alzheimer patients. Here, we studied the effects of glycation on the structure and aggregation properties of IAPP with several biophysical techniques ranging from fluorescence to circular dichroism, mass spectrometry and atomic force microscopy. We demonstrate that glycation occurs exclusively on the N-terminal lysine leaving the only arginine (Arg11) unmodified. At variance with recent studies, we show that the dynamical interplay between glycation and aggregation affects the structure of the peptide, slows down the aggregation process and influences the aggregate morphology.