The decomposition of peroxynitrite does not yield nitroxyl anion and singlet oxygen

In a recent article [Khan, A. U., Kovacic, D., Kolbanovsky, A., Desai, M., Frenkel, K. & Geacintov, N. E. (2000) Proc. Natl. Acad. Sci. USA 97, 2984–2989], the authors claimed that ONOO−, after protonation to ONOOH, decomposes into 1HNO and 1O2 according to a spin-conserved unimolecular mechanism. This claim was based partially on their observation that nitrosylhemoglobin is formed via the reaction of peroxynitrite with methemoglobin at neutral pH. However, thermochemical considerations show that the yields of 1O2 and 1HNO are about 23 orders of magnitude lower than those of ⋅NO2 and ⋅OH, which are formed via the homolysis of ONOOH. We also show that methemoglobin does not form with peroxynitrite any spectrally detectable product, but with contaminations of nitrite and H2O2 present in the peroxynitrite sample. Thus, there is no need to modify the present view of the mechanism of ONOOH decomposition, according to which initial homolysis into a radical pair, [ONO⋅ ⋅OH]cage, is followed by the diffusion of about 30% of the radicals out of the cage, while the rest recombines to nitric acid in the solvent cage.

Singular value decomposition for genome-wide expression data processing and modeling

We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized “eigengenes” × “eigenarrays” space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Mode matches and their locations in the hydrophobic free energy sequences of peptide ligands and their receptor eigenfunctions

Patterns in sequences of amino acid hydrophobic free energies predict secondary structures in proteins. In protein folding, matches in hydrophobic free energy statistical wavelengths appear to contribute to selective aggregation of secondary structures in “hydrophobic zippers.” In a similar setting, the use of Fourier analysis to characterize the dominant statistical wavelengths of peptide ligands’ and receptor proteins’ hydrophobic modes to predict such matches has been limited by the aliasing and end effects of short peptide lengths, as well as the broad-band, mode multiplicity of many of their frequency (power) spectra. In addition, the sequence locations of the matching modes are lost in this transformation. We make new use of three techniques to address these difficulties: (i) eigenfunction construction from the linear decomposition of the lagged covariance matrices of the ligands and receptors as hydrophobic free energy sequences; (ii) maximum entropy, complex poles power spectra, which select the dominant modes of the hydrophobic free energy sequences or their eigenfunctions; and (iii) discrete, best bases, trigonometric wavelet transformations, which confirm the dominant spectral frequencies of the eigenfunctions and locate them as (absolute valued) moduli in the peptide or receptor sequence. The leading eigenfunction of the covariance matrix of a transmembrane receptor sequence locates the same transmembrane segments seen in n-block-averaged hydropathy plots while leaving the remaining hydrophobic modes unsmoothed and available for further analyses as secondary eigenfunctions. In these receptor eigenfunctions, we find a set of statistical wavelength matches between peptide ligands and their G-protein and tyrosine kinase coupled receptors, ranging across examples from 13.10 amino acids in acid fibroblast growth factor to 2.18 residues in corticotropin releasing factor. We find that the wavelet-located receptor modes in the extracellular loops are compatible with studies of receptor chimeric exchanges and point mutations. A nonbinding corticotropin-releasing factor receptor mutant is shown to have lost the signatory mode common to the normal receptor and its ligand. Hydrophobic free energy eigenfunctions and their transformations offer new quantitative physical homologies in database searches for peptide-receptor matches.

Rates of decomposition of ribose and other sugars: implications for chemical evolution.

The existence of the RNA world, in which RNA acted as a catalyst as well as an informational macromolecule, assumes a large prebiotic source of ribose or the existence of pre-RNA molecules with backbones different from ribose-phosphate. The generally accepted prebiotic synthesis of ribose, the formose reaction, yields numerous sugars without any selectivity. Even if there were a selective synthesis of ribose, there is still the problem of stability. Sugars are known to be unstable in strong acid or base, but there are few data for neutral solutions. Therefore, we have measured the rate of decomposition of ribose between pH 4 and pH 8 from 40 degrees C to 120 degrees C. The ribose half-lives are very short (73 min at pH 7.0 and 100 degrees C and 44 years at pH 7.0 and 0 degrees C). The other aldopentoses and aldohexoses have half-lives within an order of magnitude of these values, as do 2-deoxyribose, ribose 5-phosphate, and ribose 2,4-bisphosphate. These results suggest that the backbone of the first genetic material could not have contained ribose or other sugars because of their instability.