Mitochondrial superoxide anion radicals mediate induction of apoptosis in cardiac myoblasts exposed to chronic hypoxia

Both reactive oxygen species (ROS) and ATP depletion may be significant in hypoxia-induced damage and death, either collectively or independently, with high energy requiring, metabolically active cells being the most susceptible to damage. We investigated the kinetics and effects of ROS production in cardiac myoblasts, H9C2 cells, under 2%, 10% and 21% O2 in the presence or absence of apocynin, rotenone and carbonyl cyanide p-(trifluoromethoxy) phenylhydrazone. H9C2 cells showed significant loss of viability within 30 min of culture at 2% oxygen which was not due to apoptosis, but was associated with an increase in protein oxidation. However, after 4 h, apoptosis induction was observed at 2% oxygen and also to a lesser extent at 10% oxygen; this was dependent on the levels of mitochondrial superoxide anion radicals determined using dihydroethidine. Hypoxia-induced ROS production and cell death could be rescued by the mitochondrial complex I inhibitor, rotenone, despite further depletion of ATP. In conclusion, a change to superoxide anion radical steady state level was not detectable after 30 min but was evident after 4 h of mild or severe hypoxia. Superoxide anion radicals from the mitochondrion and not ATP depletion is the major cause of apoptotic cell death in cardiac myoblasts under chronic, severe hypoxia.

Reactive oxygen and nitrogen species production in cardiomyoblasts during hypoxia and reoxygenation

Hypoxia is a stress condition in which tissues are deprived of an adequate O2 supply; this may trigger cell death with pathological consequences in cardiovascular or neurodegenerative disease. Reperfusion is the restoration of an oxygenated blood supply to hypoxic tissue and can cause more cell injury. The kinetics and consequences of reactive oxygen and nitrogen species (ROS/RNS) production in cardiomyoblasts are poorly understood. The present study describes the systematic characterization of the kinetics of ROS/RNS production and their roles in cell survival and associated protection during hypoxia and hypoxia/reperfusion. H9C2 cells showed a significant loss of viability under 2% O2 for 30min hypoxia and cell death; associated with an increase in protein oxidation. After 4h, apoptosis induction under 2% O2 and 10% O2 was dependent on the production of mitochondrial superoxide (O2-•) and nitric oxide (•NO), partly from nitric oxide synthase (NOS). Both apoptotic and necrotic cell death during 2% O2 for 4h could be rescued by the mitochondrial complex I inhibitor; rotenone and NOS inhibitor; L-NAME. Both L-NAME and the NOX (NADPH oxidase) inhibitor; apocynin reduced apoptosis under 10% O2 for 4h hypoxia. The mitochondrial uncoupler; FCCP significantly reduced cell death via a O2-• dependent mechanism during 2% O2, 30min hypoxia. During hypoxia (2% O2, 4h)/ reperfusion (21% O2, 2h), metabolic activity was significantly reduced with increased production of O2-• and •NO, during hypoxia but, partially restored during reperfusion. O2-• generation during hypoxia/reperfusion was mitochondrial and NOX- dependent, whereas •NO generation depended on both NOS and non-enzymatic sources. Inhibition of NOS worsened metabolic activity during reperfusion, but did not effect this during sustained hypoxia. Nrf2 activation during 2% O2, a sustained hypoxia and reperfusion was O2-•/•NO dependent. Inhibition of NF-?B activation aggravated metabolic activity during 2% O2, 4h hypoxia. In conclusion, mitochondrial O2-•, but, not ATP depletion is the major cause of apoptotic and necrotic cell death in cardiomyoblasts under 2% O2, 4h hypoxia, whereas apoptotic cell death under 10% O2, 4h, is due to NOS-dependent •NO. The management of ROS/RNS rather than ATP is required for improved survival during hypoxia. O2-• production from mitochondria and NOS is cardiotoxic during hypoxia/reperfusion. NF-?B activation during hypoxia and NOS activation during reperfusion is cardiomyoblast protective.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).