Air Pollutant Emission Rates for Dry Anaerobic Digestion and Composting of Organic Municipal Solid Waste.

Dry anaerobic digestion (AD) of organic municipal solid waste (MSW) followed by composting of the residual digestate is a waste diversion strategy that generates biogas and soil amendment products. The AD-composting process avoids methane (CH4) emissions from landfilling, but emissions of other greenhouse gases, odorous/toxic species, and reactive compounds can affect net climate and air quality impacts. In situ measurements of key sources at two large-scale industrial facilities in California were conducted to quantify pollutant emission rates across the AD-composting process. These measurements established a strong relationship between flared biogas ammonia (NH3) content and emitted nitrogen oxides (NOx), indicating that fuel NOx formation is significant and dominates over the thermal or prompt NOx pathways when biogas NH3 concentration exceeds ∼200 ppm. Composting is the largest source of CH4, carbon dioxide (CO2), nitrous oxide (N2O), and carbon monoxide (CO) emissions (∼60-70%), and dominate NH3, hydrogen sulfide (H2S), and volatile organic compounds (VOC) emissions (>90%). The high CH4 contribution to CO2-equivalent emissions demonstrates that composting can be an important CH4 source, which could be reduced with improved aeration. Controlling greenhouse gas and toxic/odorous emissions from composting offers the greatest mitigation opportunities for reducing the climate and air quality impacts of the AD-composting process.

Optimization of Secondary Air Injection in a Wood-Burning Cookstove: An Experimental Study.

Nearly 40% of the world's population regularly cooks on inefficient biomass stoves that emit harmful airborne pollutants, such as particulate matter (PM). Secondary air injection can significantly reduce PM mass emissions to mitigate the health and climate impacts associated with biomass cookstoves. However, secondary air injection can also increase the number of ultrafine particles emitted, which may be more harmful to health. This research investigates the effect of secondary air injection on the mass and size distribution of PM emitted during solid biomass combustion. An experimental wood-burning cookstove platform and parametric testing approach are presented to identify and optimize secondary air injection parameters that reduce PM and other harmful pollutants. Size-resolved measurements of PM emissions were collected and analyzed as a function of parametric stove design settings. The results show that PM emissions are highly sensitive to secondary air injection flow rate and velocity. Although increasing turbulent mixing (through increased velocity) can promote more complete combustion, increasing the total flow rate of secondary air may cause localized flame quenching that increases particle emissions. Therefore, biomass cookstoves that implement secondary air injection should be carefully optimized and validated to ensure that PM emission reductions are achieved throughout the particle size range.

Activation of aortic endothelial cells by oxidized phospholipids: a phosphoproteomic analysis.

Previous studies have shown that oxidized products of the phospholipid PAPC (Ox-PAPC) are strong activators of aortic endothelial cells and play an important role in atherosclerosis and other inflammatory diseases. We and others have demonstrated that Ox-PAPC activates specific signaling pathways and regulates a large number of genes. Using a phosphoproteomic approach based on phosphopeptide enrichment and mass spectrometry analysis, we identified candidate changes in Ox-PAPC-induced protein phosphorylation of 228 proteins. Functional annotation of these proteins showed an enrichment of the regulation of cytoskeleton, junctional components, and tyrosine kinases, all of which may contribute to the phenotypic and molecular changes observed in endothelial cells treated with Ox-PAPC. Many changes in protein phosphorylation induced by Ox-PAPC are reported here for the first time and provide new insights into the mechanism of activation by oxidized lipids, including phosphorylation-based signal transduction.

Computational prediction of proteotypic peptides for quantitative proteomics.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

LC-MS solvent composition monitoring and chromatography alignment using mobile phase tracer molecules.

In the field of proteomics, reproducible liquid chromatographic description of analytes is often a key element for the differentiation or identification of proteins or peptides for clinical or biological research projects. However, analyte identification by retention time can be problematic in proteomics where lack of standardization can result in significantly different chromatography for the same analytes analyzed on different machines. Here we present a novel method of monitoring the mobile phase gradient of LC-MS/MS analyses by monitoring the ion current signal intensities of tracer molecules dissolved in the mobile phase solvents. The tracers' ion current signal intensities chronicled gradient fluctuations, did not adversely affect the number or quality of CID-based sequence identifications, and had lower run-to-run variance when compared to retention time.

Global phosphoproteomics reveals crosstalk between Bcr-Abl and negative feedback mechanisms controlling Src signaling.

In subtypes and late stages of leukemias driven by the tyrosine kinase fusion protein Bcr-Abl, signaling by the Src family kinases (SFKs) critically contributes to the leukemic phenotype. We performed global tyrosine phosphoprofiling by quantitative mass spectrometry of Bcr-Abl-transformed cells in which the activities of the SFKs were perturbed to build a detailed context-dependent network of cancer signaling. Perturbation of the SFKs Lyn and Hck with genetics or inhibitors revealed Bcr-Abl downstream phosphorylation events either mediated by or independent of SFKs. We identified multiple negative feedback mechanisms within the network of signaling events affected by Bcr-Abl and SFKs and found that Bcr-Abl attenuated these inhibitory mechanisms. The C-terminal Src kinase (Csk)-binding protein Pag1 (also known as Cbp) and the tyrosine phosphatase Ptpn18 both mediated negative feedback to SFKs. We observed Bcr-Abl-mediated phosphorylation of the phosphatase Shp2 (Ptpn11), and this may contribute to the suppression of these negative feedback mechanisms to promote Bcr-Abl-activated SFK signaling. Csk and a kinase-deficient Csk mutant both produced similar globally repressive signaling consequences, suggesting a critical role for the adaptor protein function of Csk in its inhibition of Bcr-Abl and SFK signaling. The identified Bcr-Abl-activated SFK regulatory mechanisms are candidates for dysregulation during leukemia progression and acquisition of SFK-mediated drug resistance.

A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface.

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI.

Mass spectrometry-based quantitative proteomic profiling.

Quantitative proteomics involves the identification and quantitation of protein components in various biological systems. Stable isotope labelling technology, by both metabolic and chemical methods, has been the most commonly used approach for global proteome-wide profiling. Recently, its capability has been extended from labelled pairs to multiple labels, allowing for the simultaneous quantification of multiplex samples. The ion intensity-based quantitative approach has progressively gained more popularity as mass spectrometry performance has improved significantly. Although some success has been reported, it remains difficult comprehensively to characterise the global proteome, due to its enormous complexity and dynamic range. The use of sub-proteome fractionation techniques permits a simplification of the proteome and provides a practical step towards the ultimate dissection of the entire proteome. Further development of the technology for targeting sub-proteomes on a functional basis - such as selecting proteins with differential expression profiles from mass spectrometric analyses, for further mass spectrometric sequencing in an intelligent manner--is expected in the near future.

Improving mass and liquid chromatography based identification of proteins using bayesian scoring.

We present a method for peptide and protein identification based on LC-MS profiling. The method identified peptides at high-throughput without expending the sequencing time necessary for CID spectra based identification. The measurable peptide properties of mass and liquid chromatographic elution conditions are used to characterize and differentiate peptide features, and these peptide features are matched to a reference database from previously acquired and archived LC-MS/MS experiments to generate sequence assignments. The matches are scored according to the probability of an overlap between the peptide feature and the database peptides resulting in a ranked list of possible peptide sequences for each peptide submitted. This method resulted in 6 times more peptide sequence identifications from a single LC-MS analysis of yeast than from shotgun peptide sequencing using LC-MS/MS.

Abundance ratio-dependent proteomic analysis by mass spectrometry.

The goal of quantitative proteomics is to determine the identity and relative quantity of each protein present in two or more complex protein samples. Here we describe a novel approach to quantitative proteomics. It is based on a highly accurate algorithm for the automated quantification of chromatographically fractionated, isotope-coded affinity-tagged peptides and MALDI quadrupole time-of-flight tandem mass spectrometry for their identification. The method is capable of detecting and selectively identifying those proteins within a complex mixture that show a difference in relative abundance. We demonstrate the effectiveness and the versatility of this approach in the analysis of a standard protein mixture, protein expression profiling in a human prostate cancer cell line model, and identification of the specific components of the multiprotein transcriptional machinery in S. cerevisiae.