Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.

Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets.

Despite immense interest in the proteome as a source of biomarkers in cancer, mass spectrometry has yet to yield a clinically useful protein biomarker for tumor classification. To explore the potential of a particular class of mass spectrometry-based quantitation approaches, label-free alignment of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data sets, for the identification of biomarkers for acute leukemias, we asked whether a label-free alignment algorithm could distinguish known classes of leukemias on the basis of their proteomes. This approach to quantitation involves (1) computational alignment of MS1 peptide peaks across large numbers of samples; (2) measurement of the relative abundance of peptides across samples by integrating the area under the curve of the MS1 peaks; and (3) assignment of peptide IDs to those quantified peptide peaks on the basis of the corresponding MS2 spectra. We extracted proteins from blasts derived from four patients with acute myeloid leukemia (AML, acute leukemia of myeloid lineage) and five patients with acute lymphoid leukemia (ALL, acute leukemia of lymphoid lineage). Mobilized CD34+ cells purified from peripheral blood of six healthy donors and mononuclear cells (MNC) from the peripheral blood of two healthy donors were used as healthy controls. Proteins were analyzed by LC-MS/MS and quantified with a label-free alignment-based algorithm developed in our laboratory. Unsupervised hierarchical clustering of blinded samples separated the samples according to their known biological characteristics, with each sample group forming a discrete cluster. The four proteins best able to distinguish CD34+, AML, and ALL were all either known biomarkers or proteins whose biological functions are consistent with their ability to distinguish these classes. We conclude that alignment-based label-free quantitation of LC-MS/MS data sets can, at least in some cases, robustly distinguish known classes of leukemias, thus opening the possibility that large scale studies using such algorithms can lead to the identification of clinically useful biomarkers.

Electrokinetic properties of hydroxyapatite under flotation conditions.

The effect of calcite supernatant, calcium, and carbonate ions on the hydroxyapatite (HA) zeta potential without and in the presence of sodium oleate (1x10(-4) mol L(-1)) was examined within the pH range from 4 to 12. The interpretation of results was based on the HA surface and oleate solution chemistry, and on some floatability tests. HA, with different positive and negative surface sites formed depending on its solubility and pH, had a negative zeta potential over the whole pH range. This mineral is not naturally floatable (flotation recovery, 5%9.3. The HA surface is less negatively charged in calcite supernatant than in water from pH 6.6 to 9.2 due to the adsorption on HA negative surface active centers ([triple bond]HPO(4)(-) and [triple bond]PO(4)(2-)) of the Ca(2+), and CaOH(+) ions (present in the calcite supernatant), producing more surface sites [triple bond]HPO(4)Ca(+), [triple bond]PO(4)Ca, [triple bond]HPO(4)CaOH, and [triple bond]PO(4)(-) CaOH, and new centers [triple bond]HPO(4)CaHCO(3) and [triple bond]PO(4)(-) CaHCO(3). In the presence of 1x10(-3) mol L(-1) CaCl(2), the HA sample has positive zeta potential, the same as calcite from the same deposit, up to IEP at pH 11.25. Carbonate ions (1x10(-3) mol L(-1) Na(2)CO(3)) do not affect the HA zeta potential. However, a possible process can be the ion-exchange reaction between bicarbonate (or carbonate) and some anion from the surface sites formed on HA. The obtained values of the HA zeta potential with the collector (1x10(-4) mol L(-1) Na-oleate) added into hydroxyapatite/calcite supernatant suspensions corroborate the weak chemisorption of Ol(-) and H(Ol)(2)(-). The likely processes in this system also are the ion-exchange reactions on [triple bond]HPO(4)CaOH and [triple bond]PO(4)(-) CaOH, [triple bond]HPO(4)CaHCO(3) and [triple bond]PO(4)(-) CaHCO(3) between oleate ion and surface hydroxyl and bicarbonate ions, surface and bulk precipitations of calcium oleate, Ca(Ol)(2), and the surface and bulk precipitations of Ca[H(Ol)(2)(-)](2) over the pH range from 7 to 9. Calcite supernatant does not influence natural floatability of the mineral. However, calcite supernatant depresses the hydroxyapatite flotation in the presence of 1x10(-4) mol L(-1) Na-oleate (pH 9, R approximately 50%), a likely result of the weak chemisorption due to the steric effect of heterogeneous HA surface formed in calcite supernatant, Ca(Ol)(2) and Ca[H(Ol)(2)(-)](2) surface and bulk precipitations.

Comparison of a label-free quantitative proteomic method based on peptide ion current area to the isotope coded affinity tag method.

Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.

Genetic basis of proteome variation in yeast.

Proper regulation of protein levels is essential for health, and abnormal levels of proteins are hallmarks of many diseases. A number of studies have recently shown that messenger RNA levels vary among individuals of a species and that genetic linkage analysis can be used to identify quantitative trait loci that influence these levels. By contrast, little is known about the genetic basis of variation in protein levels in genetically diverse populations, in large part because techniques for large-scale measurements of protein abundance lag far behind those for measuring transcript abundance. Here we describe a label-free, mass spectrometry-based approach to measuring protein levels in total unfractionated cellular proteins, and we apply this approach to elucidate the genetic basis of variation in protein abundance in a cross between two diverse strains of yeast. Loci that influenced protein abundance differed from those that influenced transcript levels, emphasizing the importance of direct analysis of the proteome.

MglA regulates Francisella tularensis subsp. novicida (Francisella novicida) response to starvation and oxidative stress.

MglA is a transcriptional regulator of genes that contribute to the virulence of Francisella tularensis, a highly infectious pathogen and the causative agent of tularemia. This study used a label-free shotgun proteomics method to determine the F. tularensis subsp. novicida (F. novicida) proteins that are regulated by MglA. The differences in relative protein amounts between wild-type F. novicida and the mglA mutant were derived directly from the average peptide precursor ion intensity values measured with the mass spectrometer by using a suite of mathematical algorithms. Among the proteins whose relative amounts changed in an F. novicida mglA mutant were homologs of oxidative and general stress response proteins. The F. novicida mglA mutant exhibited decreased survival during stationary-phase growth and increased susceptibility to killing by superoxide generated by the redox-cycling agent paraquat. The F. novicida mglA mutant also showed increased survival upon exposure to hydrogen peroxide, likely due to increased amounts of the catalase KatG. Our results suggested that MglA coordinates the stress response of F. tularensis and is likely essential for bacterial survival in harsh environments.

PRISM, a generic large scale proteomic investigation strategy for mammals.

We have developed a systematic analytical approach, termed PRISM (Proteomic Investigation Strategy for Mammals), that permits routine, large scale protein expression profiling of mammalian cells and tissues. PRISM combines subcellular fractionation, multidimensional liquid chromatography-tandem mass spectrometry-based protein shotgun sequencing, and two newly developed computer algorithms, STATQUEST and GOClust, as a means to rapidly identify, annotate, and categorize thousands of expressed mammalian proteins. The application of PRISM to adult mouse lung and liver resulted in the high confidence identification of over 2,100 unique proteins including more than 100 integral membrane proteins, 400 nuclear proteins, and 500 uncharacterized proteins, the largest proteome study carried out to date on this important model organism. Automated clustering of the identified proteins into Gene Ontology annotation groups allowed for streamlined analysis of the large data set, revealing interesting and physiologically relevant patterns of tissue and organelle specificity. PRISM therefore offers an effective platform for in-depth investigation of complex mammalian proteomes.

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry.

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.