Early Aggregation Mechanism of SOD128-38 Based on Force Field Parameter of 5-Cyano-Tryptophan.

The aggregation of superoxide dismutase 1 (SOD1) results in amyloid deposition and is involved in familial amyotrophic lateral sclerosis, a fatal motor neuron disease. There have been extensive studies of its aggregation mechanism. Noncanonical amino acid 5-cyano-tryptophan (5-CN-Trp), which has been incorporated into the amyloid segments of SOD1 as infrared probes to increase the structural sensitivity of IR spectroscopy, is found to accelerate the overall aggregation rate and potentially modulate the aggregation process. Despite these observations, the underlying mechanism remains elusive. Here, we optimized the force field parameters of 5-CN-Trp and then used molecular dynamics simulation along with the Markov state model on the SOD128-38 dimer to explore the kinetics of key intermediates in the presence and absence of 5-CN-Trp. Our findings indicate a significantly increased probability of protein aggregate formation in 5CN-Trp-modified ensembles compared to wildtype. Dimeric ß-sheets of different natures were observed exclusively in the 5CN-Trp-modified peptides, contrasting with wildtype simulations. Free-energy calculations and detailed analyses of the dimer structure revealed augmented interstrand interactions attributed to 5-CN-Trp, which contributed more to peptide affinity than any other residues. These results explored the key events critical for the early nucleation of amyloid-prone proteins and also shed light on the practice of using noncanonical derivatives to study the aggregation mechanism.

In vitro and in vivo immunogenicity assessment of protein aggregate characteristics.

The immunogenicity risk of therapeutic protein aggregates has been extensively investigated over the past decades. While it is established that not all aggregates are equally immunogenic, the specific aggregate characteristics, which are most likely to induce an immune response, remain ambiguous. The aim of this study was to perform comprehensive in vitro and in vivo immunogenicity assessment of human insulin aggregates varying in size, structure and chemical modifications, while keeping other morphological characteristics constant. We found that flexible aggregates with highly altered secondary structure were most immunogenic in all setups, while compact aggregates with native-like structure were found to be immunogenic primarily in vivo. Moreover, sub-visible (1-100 µm) aggregates were found to be more immunogenic than sub-micron (0.1-1 µm) aggregates, while chemical modifications (deamidation, ethylation and covalent dimers) were not found to have any measurable impact on immunogenicity. The findings highlight the importance of utilizing aggregates varying in few characteristics for assessment of immunogenicity risk of specific morphological features and may provide a workflow for reliable particle analysis in biotherapeutics.

Deciphering the Effect of Lysine Acetylation on the Misfolding and Aggregation of Human Tau Fragment 171IPAKTPPAPK180 Using Molecular Dynamic Simulation and the Markov State Model.

The formation of neurofibrillary tangles (NFT) with ß-sheet-rich structure caused by abnormal aggregation of misfolded microtubule-associated protein Tau is a hallmark of tauopathies, including Alzheimer's Disease. It has been reported that acetylation, especially K174 located in the proline-rich region, can largely promote Tau aggregation. So far, the mechanism of the abnormal acetylation of Tau that affects its misfolding and aggregation is still unclear. Therefore, revealing the effect of acetylation on Tau aggregation could help elucidate the pathogenic mechanism of tauopathies. In this study, molecular dynamics simulation combined with multiple computational analytical methods were performed to reveal the effect of K174 acetylation on the spontaneous aggregation of Tau peptide 171IPAKTPPAPK180, and the dimerization mechanism as an early stage of the spontaneous aggregation was further specifically analyzed by Markov state model (MSM) analysis. The results showed that both the actual acetylation and the mutation mimicking the acetylated state at K174 induced the aggregation of the studied Tau fragment; however, the effect of actual acetylation on the aggregation was more pronounced. In addition, acetylated K174 plays a major contributing role in forming and stabilizing the antiparallel ß-sheet dimer by forming several hydrogen bonds and side chain van der Waals interactions with residues I171, P172, A173 and T175 of the corresponding chain. In brief, this study uncovered the underlying mechanism of Tau peptide aggregation in response to the lysine K174 acetylation, which can deepen our understanding on the pathogenesis of tauopathies.

Molecular Dynamics Simulations of Protein Aggregation: Protocols for Simulation Setup and Analysis with Markov State Models and Transition Networks.

Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. The end products of the aggregation process in these diseases are highly structured amyloid fibrils. Though in most cases, small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces that drive protein aggregation is thus required if one aims for the rational design of drugs targeting the formation of amyloid oligomers. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis, we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation.

Towards a generic prototyping approach for therapeutically-relevant peptides and proteins in a cell-free translation system.

Advances in peptide and protein therapeutics increased the need for rapid and cost-effective polypeptide prototyping. While in vitro translation systems are well suited for fast and multiplexed polypeptide prototyping, they suffer from misfolding, aggregation and disulfide-bond scrambling of the translated products. Here we propose that efficient folding of in vitro produced disulfide-rich peptides and proteins can be achieved if performed in an aggregation-free and thermodynamically controlled folding environment. To this end, we modify an E. coli-based in vitro translation system to allow co-translational capture of translated products by affinity matrix. This process reduces protein aggregation and enables productive oxidative folding and recycling of misfolded states under thermodynamic control. In this study we show that the developed approach is likely to be generally applicable for prototyping of a wide variety of disulfide-constrained peptides, macrocyclic peptides with non-native bonds and antibody fragments in amounts sufficient for interaction analysis and biological activity assessment.

Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins.

The presence of protein aggregates in cells is a known feature of many human age-related diseases, such as Huntington's disease. Simulations using fixed parameter values in a model of the dynamic evolution of expanded polyglutaime (PolyQ) proteins in cells have been used to gain a better understanding of the biological system. However, there is considerable uncertainty about the values of some of the parameters governing the system. Currently, appropriate values are chosen by ad hoc attempts to tune the parameters so that the model output matches experimental data. The problem is further complicated by the fact that the data only offer a partial insight into the underlying biological process: the data consist only of the proportions of cell death and of cells with inclusion bodies at a few time points, corrupted by measurement error. Developing inference procedures to estimate the model parameters in this scenario is a significant task. The model probabilities corresponding to the observed proportions cannot be evaluated exactly, and so they are estimated within the inference algorithm by repeatedly simulating realizations from the model. In general such an approach is computationally very expensive, and we therefore construct Gaussian process emulators for the key quantities and reformulate our algorithm around these fast stochastic approximations. We conclude by highlighting appropriate values of the model parameters leading to new insights into the underlying biological processes.

Phase separation vs aggregation behavior for model disordered proteins.

Liquid-liquid phase separation (LLPS) is widely utilized by the cell to organize and regulate various biochemical processes. Although the LLPS of proteins is known to occur in a sequence-dependent manner, it is unclear how sequence properties dictate the nature of the phase transition and thereby influence condensed phase morphology. In this work, we have utilized grand canonical Monte Carlo simulations for a simple coarse-grained model of disordered proteins to systematically investigate how sequence distribution, sticker fraction, and chain length impact the formation of finite-size aggregates, which can preempt macroscopic phase separation for some sequences. We demonstrate that a normalized sequence charge decoration (SCD) parameter establishes a "soft" predictive criterion for distinguishing when a model protein undergoes macroscopic phase separation vs finite aggregation. Additionally, we find that this order parameter is strongly correlated with the critical density for phase separation, highlighting an unambiguous connection between sequence distribution and condensed phase density. Results obtained from an analysis of the order parameter reveal that at sufficiently long chain lengths, the vast majority of sequences are likely to phase separate. Our results suggest that classical LLPS should be the primary phase transition for disordered proteins when short-ranged attractive interactions dominate and suggest a possible reason behind recent findings of widespread phase separation throughout living cells.

Modeling Protein Aggregation Kinetics: The Method of Second Stochasticization.

The nucleation of protein aggregates and their growth are important in determining the structure of the cell's membraneless organelles as well as the pathogenesis of many diseases. The large number of molecular types of such aggregates along with the intrinsically stochastic nature of aggregation challenges our theoretical and computational abilities. Kinetic Monte Carlo simulation using the Gillespie algorithm is a powerful tool for modeling stochastic kinetics, but it is computationally demanding when a large number of diverse species is involved. To explore the mechanisms and statistics of aggregation more efficiently, we introduce a new approach to model stochastic aggregation kinetics which introduces noise into already statistically averaged equations obtained using mathematical moment closure schemes. Stochastic moment equations summarize succinctly the dynamics of the large diversity of species with different molecularity involved in aggregation but still take into account the stochastic fluctuations that accompany not only primary and secondary nucleation but also aggregate elongation, dissociation, and fragmentation. This method of "second stochasticization" works well where the fluctuations are modest in magnitude as is often encountered in vivo where the number of protein copies in some computations can be in the hundreds to thousands. Simulations using second stochasticization reveal a scaling law that correlates the size of the fluctuations in aggregate size and number with the total number of monomers. This scaling law is confirmed using experimental data. We believe second stochasticization schemes will prove valuable for bridging the gap between in vivo cell biology and detailed modeling. (The code is released on https://github.com/MYTLab/stoch-agg.).

Assessment of the role of afucosylated glycoforms on the in vitro antibody-dependent phagocytosis activity of an antibody to Aß aggregates.

The terminal sugars of Fc glycans can influence the Fc-dependent biological activities of monoclonal antibody therapeutics. Afucosylated N-glycans have been shown to significantly alter binding to FcγRIIIa and affect antibody-dependent cell-mediated cytotoxicity (ADCC). Therefore, in order to maintain and ensure safety and efficacy for antibodies whose predominant mechanism of action (MOA) is ADCC, afucosylation is routinely monitored and controlled within appropriate limits. However, it is unclear how the composition and levels of afucosylated N-glycans can modulate the biological activities for a recombinant antibody whose target is not a cell surface receptor, as is the case with ADCC. The impact of different types and varying levels of enriched afucosylated N-glycan species on the in vitro bioactivities is assessed for an antibody whose target is aggregated amyloid beta (Aß). While either the presence of complex biantennary or high mannose afucosylated glycoforms significantly increased FcγRIIIa binding activity compared to fucosylated glycoforms, they did not similarly increase aggregated Aß uptake activity mediated by different effector cells. These experiments suggest that afucosylated N-glycans are not critical for the in vitro phagocytic activity of a recombinant antibody whose target is aggregated Aß and uses Fc effector function as part of its MOA.

Aggregation and coacervation with Monte Carlo simulations.

Protein aggregation into oligomeric species has been linked to a number of neurodegenerative diseases. The ability to assemble into ordered fibril-like forms under certain conditions is now regarded as a very common property of polypeptide chains. A different form of assembly is known for some proteins in which protein molecules sequester into liquid-like droplets. Understanding the biophysical mechanisms behind these phenomena is of great relevance not only for deeper insights into the associated disorders but also for any protein-based therapeutics. Here we review a few examples of the use of Markov chain Monte Carlo simulations in the study of these two forms of protein assembly.

Mechanistic Assessment of Functionalized Mesoporous Silica-Mediated Insulin Fibrillation.

Insulin, which is a small protein hormone consisting of 51 amino acids, rapidly fibrillates under stressogenic conditions. This biotechnological/medical problematic reaction quickly accelerates in the presence of some particles, while there are several other particles that slow down the kinetic process. To address the unexplored demand of the particles that modulate protein fibrillation, we have synthesized two amino-based particles and a chitosan-coated mesoporous silica particle (MS-NH2, MS-3NH2, and MS-chitosan) to investigate insulin fibrillation. While these particles were fairly similar in size, they are differ in their net positive charge and surface hydrophobicity. To monitor the exact role of the hydrophobic interaction between the protein and MS-chitosan during the fibrillation, we have also co- and preincubated insulin with cholesterol and the particles under stressogenic conditions. The results indicate that MS-NH2 and MS-3NH2, due to their high positive charges and lack of surface hydrophobicity, repel the positively charged unfolded insulins at pH 2.0. Moreover, MS-chitosan with 25% surface hydrophobicity stacks partially unfolded insulins to its surface and induces some α-helix to ß-sheet structural transitions to the protein. Consequently, both amino- and chitosan-based particles slow down the kinetics of the fibrillation. We also showed that cholesterol can structurally participate in insulin fibril architecture as a hydrophobic bridge, and extraction of this molecule from the preformed fibrils may disrupt the fibril structure.

A Streamlined Bioanalytical Approach to Select a Compatible Primary Container System Early in Drug Development: A Toolbox for the Biopharmaceutical Industry.

To ensure drug efficacy and patient safety, the importance of interaction between primary container and a biological drug product must not be ignored. The United States Food and Drug Administration guidance on development and manufacturing of combination products (e.g., the biologic and the primary container) encourages careful selection and stability testing of the drug and its performance in the marketed primary container. With various options available for primary container type (vials, prefilled syringes, and cartridges), material (e.g., glass or plastic), and lubricants/coatings, we designed a platform consisting of several bioanalytical methods that can simplify selection of a compatible primary container. We tested the stability of a commercially available monoclonal antibody (mAb) in 3 syringe types under 3 conditions: cold storage, high temperature, and agitation induced stress, respectively. Under each condition, dynamic fluid imaging was sensitive enough to differentiate mAb stability as measured by aggregate formation in different syringe systems, followed by size exclusion-high performance liquid chromatography and sodium dodecyl sulfate-polyacrylamide gel electrophoresis but only at high temperature. With this platform, we identified a primary container that provided higher mAb stability under cold storage as well as stress conditions. We recommend that such an approach should be applied early in drug development stage to identify a superior primary container system to maintain drug stability and quality.

A Multicompany Assessment of Submicron Particle Levels by NTA and RMM in a Wide Range of Late-Phase Clinical and Commercial Biotechnology-Derived Protein Products.

One of the major product quality challenges for injectable biologics is controlling the amount of protein aggregates and particles present in the final drug product. This article focuses on particles in the submicron range (<2 µm). A cross-industry collaboration was undertaken to address some of the analytical gaps in measuring submicron particles (SMPs), developing best practices, and surveying the concentration of these particles present in 52 unique clinical and commercial protein therapeutics covering 62 dosage forms. Measured particle concentrations spanned a range of 4 orders of magnitude for nanoparticle tracking analysis and 3 orders of magnitude for resonant mass measurement. The particle concentrations determined by the 2 techniques differed significantly for both control and actual product. In addition, results suggest that these techniques exhibit higher variability compared to well-established subvisible particle characterization techniques (e.g., flow-imaging or light obscuration). Therefore, in their current states, nanoparticle tracking analysis and resonant mass measurement-based techniques can be used during product and process characterization, contributing information on the nature and propensity for formation of submicron particles and what is normal for the product, but may not be suitable for release or quality control testing. Evaluating the level of SMPs to which humans have been routinely exposed during the administration of several commercial and late-phase clinical products adds critical knowledge to our understanding of SMP levels that may be considered acceptable from a safety point of view. This article also discusses dependence of submicron particle size and concentration on the dosage form attributes such as physical state, primary packaging, dose strength, etc. To the best of our knowledge, this is the largest study ever conducted to characterize SMPs in late-phase and commercial products.

VITPOR AI, A Coagulation Factor XIIa Inhibitor from Porphyra yezoensis: In Vivo Mode of Action and Assessment of Platelet Function Analysis.

BACKGROUND: Thrombosis represents as the prime contributor to the burden of diseases, worldwide. Conventional anticoagulants for thrombosis therapy have a common bleeding side effect. Bioactive peptides are studied to be an effective alternative for currently available therapeutic drugs. OBJECTIVE: In this study, VITPOR AI peptide, a previously reported coagulation FXIIa inhibitor from Nori (Porphyra yezoensis), was assessed for its inhibitory activity against FXIIa and its in vivo mode of action. METHODS: In vivo efficacy as well as the antithrombotic property of the peptide was evaluated in mice model by ex vivo activated Partial Thromboplastin Time assay, tail transection model and whole blood clotting time. The enzyme kinetics was studied using chromogenic substrate assay. RESULTS: The kinetic behaviour of VITPOR AI showed that the peptide is a competitive inhibitor of FXIIa. Peptide showed significant inhibition of platelet adhesion and aggregation. VITPOR AI exhibited significant antithrombotic activity. Furthermore, ex vivo activated Partial Thromboplastin Time assay revealed that VITPOR AI exhibited potent anticoagulant activity in vivo. Tail bleeding assay revealed that the peptide did not prolong bleeding time in mice even at a higher dose of 5 mg/kg. Cytotoxicity studies of the peptide against human blood leukocytes indicated the safety of the peptide. CONCLUSION: VITPOR AI could be prospected as a potent anticoagulant with Factor XIIa inhibition, antiplatelet aggregation and antithrombotic activity. It was also studied to have no bleeding side effect.

Disclosing the Mechanism of Spontaneous Aggregation and Template-Induced Misfolding of the Key Hexapeptide (PHF6) of Tau Protein Based on Molecular Dynamics Simulation.

The microtubule-associated protein tau is critical for the development and maintenance of the nervous system. Tau dysfunction is associated with a variety of neurodegenerative diseases called tauopathies, which are characterized by neurofibrillary tangles formed by abnormally aggregated tau protein. Studying the aggregation mechanism of tau protein is of great significance for elucidating the etiology of tauopathies. The hexapeptide 306VQIVYK311 (PHF6) of R3 has been shown to play a vital role in promoting tau aggregation. In this study, long-term all-atom molecular dynamics simulations in explicit solvent were performed to investigate the mechanisms of spontaneous aggregation and template-induced misfolding of PHF6, and the dimerization at the early stage of nucleation was further specifically analyzed by the Markov state model (MSM). Our results show that PHF6 can spontaneously aggregate to form multimers enriched with ß-sheet structure and the ß-sheets in multimers prefer to exist in a parallel way. It is observed that PHF6 monomer can be induced to form a ß-sheet structure on either side of the template but in a different way. In detail, the ß-sheet structure is easier to form on the left side but does not extend well, but on the right side, the monomer can form the extended ß-sheet structure. Furthermore, MSM analysis shows that the formation of dimer mainly occurs in three steps. First, the separated monomers collide with each other at random orientations, and then a dimer with short ß-sheet structure at the N-terminal forms; finally, ß-sheets elongate to form an extended parallel ß-sheet dimer. During these processes, multiple intermediate states are identified and multiple paths can form a parallel ß-sheet dimer from the disordered coil structure. Moreover, the residues I308, V309, and Y310 play an essential role in the dimerization. In a word, our results uncover the aggregation and misfolding mechanism of PHF6 from the atomic level, which can provide useful theoretical guidance for rational design of effective therapeutic drugs against tauopathies.

Probing the Basis of α-Synuclein Aggregation by Comparing Simulations to Single-Molecule Experiments.

Intrinsically disordered proteins often play an important role in protein aggregation. However, it is challenging to determine the structures and interactions that drive the early stages of aggregation because they are transient and obscured in a heterogeneous mixture of disordered states. Even computational methods are limited because the lack of ordered structure makes it difficult to ensure that the relevant conformations are sampled. We address these challenges by integrating atomistic simulations with high-resolution single-molecule measurements reported previously, using the measurements to help discern which parts of the disordered ensemble of structures in the simulations are most probable while using the simulations to identify residues and interactions that are important for oligomer stability. This approach was applied to α-synuclein, an intrinsically disordered protein that aggregates in the context of Parkinson's disease. We simulated single-molecule pulling experiments on dimers, the minimal oligomer, and compared them to force spectroscopy measurements. Force-extension curves were simulated starting from a set of 66 structures with substantial structured content selected from the ensemble of dimer structures generated at zero force via Monte Carlo simulations. The pattern of contour length changes as the structures unfolded through intermediate states was compared to the results from optical trapping measurements on the same dimer to discern likely structures occurring in the measurements. Simulated pulling curves were generally consistent with experimental data but with a larger number of transient intermediates. We identified an ensemble of ß-rich dimer structures consistent with the experimental data from which dimer interfaces could be deduced. These results suggest specific druggable targets in the structural motifs of α-synuclein that may help prevent the earliest steps of oligomerization.

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation.

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.

The Early Phase of ß2m Aggregation: An Integrative Computational Study Framed on the D76N Mutant and the ΔN6 Variant.

Human ß2-microglobulin (b2m) protein is classically associated with dialysis-related amyloidosis (DRA). Recently, the single point mutant D76N was identified as the causative agent of a hereditary systemic amyloidosis affecting visceral organs. To get insight into the early stage of the ß2m aggregation mechanism, we used molecular simulations to perform an in depth comparative analysis of the dimerization phase of the D76N mutant and the ΔN6 variant, a cleaved form lacking the first six N-terminal residues, which is a major component of ex vivo amyloid plaques from DRA patients. We also provide first glimpses into the tetramerization phase of D76N at physiological pH. Results from extensive protein-protein docking simulations predict an essential role of the C- and N-terminal regions (both variants), as well as of the BC-loop (ΔN6 variant), DE-loop (both variants) and EF-loop (D76N mutant) in dimerization. The terminal regions are more relevant under acidic conditions while the BC-, DE- and EF-loops gain importance at physiological pH. Our results recapitulate experimental evidence according to which Tyr10 (A-strand), Phe30 and His31 (BC-loop), Trp60 and Phe62 (DE-loop) and Arg97 (C-terminus) act as dimerization hot-spots, and further predict the occurrence of novel residues with the ability to nucleate dimerization, namely Lys-75 (EF-loop) and Trp-95 (C-terminus). We propose that D76N tetramerization is mainly driven by the self-association of dimers via the N-terminus and DE-loop, and identify Arg3 (N-terminus), Tyr10, Phe56 (D-strand) and Trp60 as potential tetramerization hot-spots.

Properties of collagen extracted from Amur sturgeon Acipenser schrenckii and assessment of collagen fibrils in vitro.

The objective of this study was to assess the nature of the collagens from the Amur sturgeon to determine its possibility as a potential collagen source for biomedical applications. From a sturgeon (1.22 kg), 6.0 g (dry wt) of skin collagen (SC), 4.1 g of swim bladder collagen (SBC), and 0.4 g of notochord collagen (NC) were obtained. SC and SBC were characterized as type I, and NC as type II collagen. Denaturation temperatures of SC, SBC, and NC were calculated as 28.5, 30.5, and 33.5 °C, respectively. Gene expression of the type I procollagen α2 chain of Amur sturgeon (ascol1a2) was specifically higher than ascol1a1 expression in the swim bladder, suggesting a unique composition of α chains in this organ. SC and SBC had better abilities of fibril formation with unique higher-order structures compared with porcine type I collagen. The maximum transition temperature (Tm) of reassembled fibrils formed in a buffer solution containing NaCl at 0 and 140 mM was 34.4 °C and 38.9 °C in SC, and 40.1 °C and 40.7 °C in SBC, respectively. These characteristic features suggested that sturgeon collagens could be used in the biomedical industries in future applications.

IAPP toxicity activates HIF1α/PFKFB3 signaling delaying ß-cell loss at the expense of ß-cell function.

The islet in type 2 diabetes (T2D) is characterized by amyloid deposits derived from islet amyloid polypeptide (IAPP), a protein co-expressed with insulin by ß-cells. In common with amyloidogenic proteins implicated in neurodegeneration, human IAPP (hIAPP) forms membrane permeant toxic oligomers implicated in misfolded protein stress. Here, we establish that hIAPP misfolded protein stress activates HIF1α/PFKFB3 signaling, this increases glycolysis disengaged from oxidative phosphorylation with mitochondrial fragmentation and perinuclear clustering, considered a protective posture against increased cytosolic Ca2+ characteristic of toxic oligomer stress. In contrast to tissues with the capacity to regenerate, ß-cells in adult humans are minimally replicative, and therefore fail to execute the second pro-regenerative phase of the HIF1α/PFKFB3 injury pathway. Instead, ß-cells in T2D remain trapped in the pro-survival first phase of the HIF1α injury repair response with metabolism and the mitochondrial network adapted to slow the rate of cell attrition at the expense of ß-cell function.