Deletion of the Mitochondrial Flavoprotein Apoptosis Inducing Factor (AIF) Induces β-Cell Apoptosis and Impairs β-Cell Mass

Background

Apoptosis is a hallmark of β-cell death in both type 1 and type 2 diabetes mellitus. Understanding how apoptosis contributes to β-cell turnover may lead to strategies to prevent progression of diabetes. A key mediator of apoptosis, mitochondrial function, and cell survival is apoptosis inducing factor (AIF). In the present study, we investigated the role of AIF on β-cell mass and survival using the Harlequin (Hq) mutant mice, which are hypomorphic for AIF.

Methodology/Principal Findings

Immunohistochemical evaluation of pancreata from Hq mutant mice displayed much smaller islets compared to wild-type mice (WT). Analysis of β-cell mass in these mice revealed a greater than 4-fold reduction in β-cell mass together with an 8-fold increase in β-cell apoptosis. Analysis of cell cycle dynamics, using BrdU pulse as a marker for cells in S-phase, did not detect significant differences in the frequency of β-cells in S-phase. In contrast, double staining for phosphorylated Histone H3 and insulin showed a 3-fold increase in β-cells in the G2 phase in Hq mutant mice, but no differences in M-phase compared to WT mice. This suggests that the β-cells from Hq mutant mice are arrested in the G2 phase and are unlikely to complete the cell cycle. β-cells from Hq mutant mice display increased sensitivity to hydrogen peroxide-induced apoptosis, which was confirmed in human islets in which AIF was depleted by siRNA. AIF deficiency had no effect on glucose stimulated insulin secretion, but the impaired effect of hydrogen peroxide on β-cell function was potentiated.

Conclusions/Significance

Our results indicate that AIF is essential for maintaining β-cell mass and for oxidative stress response. A decrease in the oxidative phosphorylation capacity may counteract the development of diabetes, despite its deleterious effects on β-cell survival.     

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability.     

Incidence of Extended-Spectrum β-Lactamases and Characterization of Integrons in Extended-Spectrum β-Lactamase-producing Klebsiella pneumoniae Isolated in Shantou, China

This study is concerned with the level of antibiotic resistance of extended-spectrum β-lactamase （ESBL）-producing Klebsiella pneumoniae, isolated in Shantou, China, and its mechanism. Seventy- four non-repetitive clinical isolates of K. pneumoniae producing ESBLs were isolated over a period of 2 years. Antibiotic susceptibility, carried out by Epsilometer test, showed that most of the isolates were multiresistant. Polymerase chain reaction showed that, among the several types of β-lactamases, SHV was the most prevalent, TEM was the second most prevalent, and CTX-M was the least prevalent. Sixty-nine isolates were positive for integrase gene IntI1, but no IntI2 or IntI3 genes were found. The variable region of class 1 integrons were amplified and further identified by sequencing. Thirteen different gene cassettes and 11 different cassette combinations were detected. Dfr and aadA cassettes were predominant and cassette combinations dfrA12, orfF and aadA2 were most frequently found. No gene cassettes encoding ESBLs were found. Integrons were prevalent and played an important role in multidrug resistance in ESBL-producing K. pneumoniae.     

Transgenic Overexpression of Active Calcineurin in β-Cells Results in Decreased β-Cell Mass and Hyperglycemia

Background

Glucose modulates β-cell mass and function through an initial depolarization and Ca²⁺ influx, which then triggers a number of growth regulating signaling pathways. One of the most important downstream effectors in Ca²⁺ signaling is the calcium/Calmodulin activated serine threonine phosphatase, calcineurin. Recent evidence suggests that calcineurin/NFAT is essential for β-cell proliferation, and that in its absence loss of β-cells results in diabetes. We hypothesized that in contrast, activation of calcineurin might result in expansion of β-cell mass and resistance to diabetes.

Methodology/Principal Findings

To determine the role of activation of calcineurin signaling in the regulation of pancreatic β-cell mass and proliferation, we created mice that expressed a constitutively active form of calcineurin under the insulin gene promoter (caCn^RIP). To our surprise, these mice exhibited glucose intolerance. In vitro studies demonstrated that while the second phase of Insulin secretion is enhanced, the overall insulin secretory response was conserved. Islet morphometric studies demonstrated decreased β-cell mass suggesting that this was a major component responsible for altered Insulin secretion and glucose intolerance in caCn^RIP mice. The reduced β-cell mass was accompanied by decreased proliferation and enhanced apoptosis.

Conclusions

Our studies identify calcineurin as an important factor in controlling glucose homeostasis and indicate that chronic depolarization leading to increased calcineurin activity may contribute, along with other genetic and environmental factors, to β-cell dysfunction and diabetes.     

A GIP Receptor Agonist Exhibits β-Cell Anti-Apoptotic Actions in Rat Models of Diabetes Resulting in Improved β-Cell Function and Glycemic Control

Aims

The gastrointestinal hormone GIP promotes pancreatic islet function and exerts pro-survival actions on cultured β-cells. However, GIP also promotes lipogenesis, thus potentially restricting its therapeutic use. The current studies evaluated the effects of a truncated GIP analog, D-Ala²-GIP_1–30 (D-GIP_1–30), on glucose homeostasis and β-cell mass in rat models of diabetes.

Materials and Methods

The insulinotropic and pro-survival potency of D-GIP_1–30 was evaluated in perfused pancreas preparations and cultured INS-1 β-cells, respectively, and receptor selectivity evaluated using wild type and GIP receptor knockout mice. Effects of D-GIP_1–30 on β-cell function and glucose homeostasis, in vivo, were determined using Lean Zucker rats, obese Vancouver diabetic fatty rats, streptozotocin treated rats, and obese Zucker diabetic fatty rats, with effects on β-cell mass determined in histological studies of pancreatic tissue. Lipogenic effects of D-GIP_1–30 were evaluated on cultured 3T3-L1 adipocytes.

Results

Acutely, D-GIP_1–30 improved glucose tolerance and insulin secretion. Chronic treatment with D-GIP_1–30 reduced levels of islet pro-apoptotic proteins in Vancouver diabetic fatty rats and preserved β-cell mass in streptozotocin treated rats and Zucker diabetic fatty rats, resulting in improved insulin responses and glycemic control in each animal model, with no change in body weight. In in vitro studies, D-GIP_1–30 exhibited equivalent potency to GIP_1–42 on β-cell function and survival, but greatly reduced action on lipoprotein lipase activity in 3T3-L1 adipocytes.

Conclusions

These findings demonstrate that truncated forms of GIP exhibit potent anti-diabetic actions, without pro-obesity effects, and that the C-terminus contributes to the lipogenic actions of GIP.     

Competition between Folding, Native-State Dimerisation and Amyloid Aggregation in β-Lactoglobulin

We show that a series of peptides corresponding to individual β-strands in native β-lactoglobulin readily form amyloid aggregates and that such aggregates are capable of seeding fibril formation by a full-length form of β-lactoglobulin in which the disulfide bonds are reduced. By contrast, preformed fibrils corresponding to only one of the β-strands that we considered, βA, were found to promote fibril formation by a full-length form of β-lactoglobulin in which the disulfide bonds are intact. These results indicate that regions of high intrinsic aggregation propensity do not give rise to aggregation unless at least partial unfolding takes place. Furthermore, we found that the high aggregation propensity of one of the edge strands, βI, promotes dimerisation of the native structure rather than misfolding and aggregation since the structure of βI is stabilised by the presence of a disulfide bond. These findings demonstrate that the interactions that promote folding and native-state oligomerisation can also result in high intrinsic amyloidogenicity. However, we show that the presence of the remainder of the sequence dramatically reduces the net overall aggregation propensity by negative design principles that we suggest are very common in biological systems as a result of evolutionary processes.     

Selection of Aptamers for Amyloid β-Protein,the Causative Agent of Alzheimer's Disease

Alzheimer''s disease (AD) is a progressive, age-dependent, neurodegenerative disorder with an insidious course that renders its presymptomatic diagnosis difficult¹. Definite AD diagnosis is achieved only postmortem, thus establishing presymptomatic, early diagnosis of AD is crucial for developing and administering effective therapies^2,3.Amyloid β-protein (Aβ) is central to AD pathogenesis. Soluble, oligomeric Aβ assemblies are believed to affect neurotoxicity underlying synaptic dysfunction and neuron loss in AD^4,5. Various forms of soluble Aβ assemblies have been described, however, their interrelationships and relevance to AD etiology and pathogenesis are complex and not well understood⁶. Specific molecular recognition tools may unravel the relationships amongst Aβ assemblies and facilitate detection and characterization of these assemblies early in the disease course before symptoms emerge. Molecular recognition commonly relies on antibodies. However, an alternative class of molecular recognition tools, aptamers, offers important advantages relative to antibodies^7,8. Aptamers are oligonucleotides generated by in-vitro selection: systematic evolution of ligands by exponential enrichment (SELEX)^9,10. SELEX is an iterative process that, similar to Darwinian evolution, allows selection, amplification, enrichment, and perpetuation of a property, e.g., avid, specific, ligand binding (aptamers) or catalytic activity (ribozymes and DNAzymes).Despite emergence of aptamers as tools in modern biotechnology and medicine¹¹, they have been underutilized in the amyloid field. Few RNA or ssDNA aptamers have been selected against various forms of prion proteins (PrP)^12-16. An RNA aptamer generated against recombinant bovine PrP was shown to recognize bovine PrP-β¹⁷, a soluble, oligomeric, β-sheet-rich conformational variant of full-length PrP that forms amyloid fibrils¹⁸. Aptamers generated using monomeric and several forms of fibrillar β₂-microglobulin (β₂m) were found to bind fibrils of certain other amyloidogenic proteins besides β₂m fibrils¹⁹. Ylera et al. described RNA aptamers selected against immobilized monomeric Aβ40²⁰. Unexpectedly, these aptamers bound fibrillar Aβ40. Altogether, these data raise several important questions. Why did aptamers selected against monomeric proteins recognize their polymeric forms? Could aptamers against monomeric and/or oligomeric forms of amyloidogenic proteins be obtained? To address these questions, we attempted to select aptamers for covalently-stabilized oligomeric Aβ40²¹ generated using photo-induced cross-linking of unmodified proteins (PICUP)^22,23. Similar to previous findings^17,19,20, these aptamers reacted with fibrils of Aβ and several other amyloidogenic proteins likely recognizing a potentially common amyloid structural aptatope²¹. Here, we present the SELEX methodology used in production of these aptamers²¹.Download video file.^{(175M, mp4)}     

Design and Optimization of Anti-amyloid Domain Antibodies Specific for β-Amyloid and Islet Amyloid Polypeptide

Antibodies with conformational specificity are important for detecting and interfering with polypeptide aggregation linked to several human disorders. We are developing a motif-grafting approach for designing lead antibody candidates specific for amyloid-forming polypeptides such as the Alzheimer peptide (Aβ). This approach involves grafting amyloidogenic peptide segments into the complementarity-determining regions (CDRs) of single-domain (V_H) antibodies. Here we have investigated the impact of polar mutations inserted at the edges of a large hydrophobic Aβ42 peptide segment (Aβ residues 17–42) in CDR3 on the solubility and conformational specificity of the corresponding V_H domains. We find that V_H expression and solubility are strongly enhanced by introducing multiple negatively charged or asparagine residues at the edges of CDR3, whereas other polar mutations are less effective (glutamine and serine) or ineffective (threonine, lysine, and arginine). Moreover, Aβ V_H domains with negatively charged CDR3 mutations show significant preference for recognizing Aβ fibrils relative to Aβ monomers, whereas the same V_H domains with other polar CDR3 mutations recognize both Aβ conformers. We observe similar behavior for a V_H domain grafted with a large hydrophobic peptide from islet amyloid polypeptide (residues 8–37) that contains negatively charged mutations at the edges of CDR3. These findings highlight the sensitivity of antibody binding and solubility to residues at the edges of CDRs, and provide guidelines for designing other grafted antibody fragments with hydrophobic binding loops.     

Dietary Supplementation with tBHQ,an Nrf2 Stabilizer Molecule,Confers Neuroprotection Against Apoptosis in Amyloid β-Injected Rat

Nuclear factor erythroid 2-related factor 2 (Nrf2) coordinates the up-regulation of cytoprotective genes via the antioxidant response element (ARE). There is significant evidence that oxidative stress is a critical event in the pathogenesis of AD. Considering the protective role of Nrf2 against oxidative injury, we studied to determine whether in vivo toxicity of amyloid β (Aβ) can be attenuated by tBHQ, an Nrf2 stabilizer, Using an Aβ injection model. We demonstrated that pre-activation of endogenous Nrf2 by tBHQ attenuated Aβ-induced caspase-3 expression. tBHQ enhanced GSH, decreased MDA level, and inhibited NF-κB. This investigation provides the first documentation of tBHQ’s neuroprotective effect through decrease of Aβ accumulation in rat brain. Our results show the involvement of Hsp-70 in this protective effect. In summary tBHQ treatment for 1 week prior to Aβ injection protected against the oxidative damage, apoptosis and Aβ accumulation in rats.     

Prediction Errors in Learning Drug Response from Gene Expression Data – Influence of Labeling,Sample Size,and Machine Learning Algorithm

Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the prediction error, as does sample size. However, we found that no algorithm was able to consistently outperform the other and there was no significant difference between regression and two- or three class predictors in this experimental setting. These results indicate that response-modeling projects should direct efforts mainly towards sample collection and data quality, rather than method adjustment.