The results indicate that oxidation of CO to CO 2 supplies electrons for reduction of CO 2 to a methyl group by steps and enzymes of the pathway for CO 2 reduction determined for other methane-producing species. However, proteomic and quantitative RT-PCR results suggest that reduction of the methyl group to methane involves novel methyltransferases and a coenzyme F 420H2:heterodisulfide oxidoreductase system that generates a proton gradient for ATP synthesis not previously described for pathways reducing CO 2 to methane. Biochemical assays support a role for the oxidoreductase, and transcriptional mapping identified an unusual operon structure encoding the oxidoreductase. The proteomic results further indicate that acetate is synthesized from the methyl group and CO by a reversal of initial steps in the pathway for conversion of acetate to methane that yields ATP by substrate level phosphorylation. The results indicate that M. acetivorans utilizes a pathway distinct from all known CO 2 reduction pathways for methane formation that reflects an adaptation to the marine environment. Finally, the pathway supports the basis for a recently proposed primitive CO-dependent energy-conservation cycle that drove and directed the early evolution of life on Earth.anaerobic ͉ Archaea ͉ carbon monoxide C arbon monoxide (CO), an atmospheric pollutant that binds tightly to hemoglobin, is held below toxic levels in part by both aerobic and anaerobic microbes (1). The microbial metabolism of CO is an important component of the global carbon cycle (1, 2), and CO is believed to have been present in the atmosphere of early Earth that fueled the evolution of primitive metabolisms (3-7). Investigations of aerobic species from the Bacteria domain have contributed important insights into microbial CO oxidation (8, 9), as have investigations of anaerobes from the Bacteria domain that conserve energy by coupling CO oxidation to H 2 evolution (10-12). Further understanding has been derived from studies of CO-using anaerobes from the Bacteria domain that conserve energy by oxidizing CO and reducing CO 2 to acetate (13,14) or reducing sulfate to sulfide (15). Far less is known for pathways of the few CO-using species in the Archaea domain that have been described. Methanothermobacter thermautotrophicus, Methanosarcina barkeri, and Methanosarcina acetivorans obtain energy for growth by converting CO to methane (16)(17)(18)(19)(20). Although methane formation from CO first was reported in 1947 (21), a comprehensive understanding of the overall pathway for any species has not been reported. It is postulated that M. barkeri oxidizes CO to H 2 , and the H 2 is reoxidized to provide electrons for reducing CO 2 to methane (16). It is postulated further that H 2 production is essential for ATP synthesis during growth on CO (16,22,23). M. acetivorans was isolated from marine sediments where giant kelp is decomposed to methane (24). The flotation bladders of kelp contain CO that is a presumed substrate for M. acetivorans in nature. M. acetivorans produ...

A new denoising and peak picking algorithm (MEND, matched filtration with experimental noise determination) for analysis of LC-MS data is described. The algorithm minimizes both random and chemical noise in order to determine MS peaks corresponding to sample components. Noise characteristics in the data set are experimentally determined and used for efficient denoising. MEND is shown to enable low-intensity peaks to be detected, thus providing additional useful information for sample analysis. The process of denoising, performed in the chromatographic time domain, does not distort peak shapes in the m/z domain, allowing accurate determination of MS peak centroids, including low-intensity peaks. MEND has been applied to denoising of LC-MALDI-TOF-MS and LC-ESI-TOF-MS data for tryptic digests of protein mixtures. MEND is shown to suppress chemical and random noise and baseline fluctuations, as well as filter out false peaks originating from the matrix (MALDI) or mobile phase (ESI). In addition, MEND is shown to be effective for protein expression analysis by allowing selection of a large number of differentially expressed ICAT pairs, due to increased signal-to-noise ratio and mass accuracy.

Quantitative proteomics analysis of cortical samples of ten Alzheimer's disease (AD) brains versus ten normally aged brains was performed by following the accurate mass and time tag (AMT) approach with the high resolution LTQ Orbitrap mass spectrometer. More than 1400 proteins were identified and quantitated. A conservative approach of selecting only the consensus results of four normalization methods was suggested and used. A total of 197 proteins were shown to be significantly differentially abundant (p-values<0.05, corrected for multiplicity of testing) in AD versus control brain samples. Thirty seven of these proteins were reported as differentially abundant or modified in AD in the previous proteomics and transcriptomics publications. The rest to the best of our knowledge are new. Mapping of the discovered proteins with bioinformatic tools revealed significant enrichment with differentially abundant proteins of pathways and processes known to be important in AD, including signal transduction, regulation of protein phosphorylation, immune response, cytoskeleton organization, lipid metabolism, energy production, and cell death.

A new algorithm is described for label-free quantitation of relative protein abundances across multiple complex proteomic samples. Q-MEND is based on the denoising and peak picking algorithm, MEND, previously developed in our laboratory. Q-MEND takes advantage of the high resolution and mass accuracy of the hybrid LTQFT MS mass spectrometer (or other high resolution mass spectrometers, such as a Q-TOF MS). The strategy, termed "cross-assignment", is introduced to increase substantially the number of quantitated proteins. In this approach, all MS/MS identifications for the set of analyzed samples are combined into a master ID list, and then each LC/ MS run is searched for the features that can be assigned to a specific identification from that master list. The reliability of quantitation is enhanced by quantitating separately all peptide charge states, along with a scoring procedure to filter out less reliable peptide abundance measurements. The effectiveness of Q-MEND is illustrated in the relative quantitative analysis of E.coli samples spiked with known amounts of non-E.coli protein digests. A mean quantitation accuracy of 7% and mean precision of 15% is demonstrated. Q-MEND can perform relative quantitation of a set of LC/MS datasets without manual intervention and can generate files compatible with the Guidelines for Proteomic Data Publication.