Cover Picture: Proteomics 6/2009

In this issue of Proteomics you will find the following highlighted articles: Keeping up with the lung cancers You're in good company if you smoke and develop lung cancer. The World Health Organization estimates 1.2 million new cases occur every year. On the other hand, 1.1 million people die from it every year‐bummer. One reason for the high death rate is the frequent development of resistance to several of the most commonly used drugs simultaneously. Multiple drug resistance (MDR) is the major cause of chemotherapeutic failure. Keenan et al. explored the proteomic changes associated with MDR failure (adriamycin) in a cultured lung cancer cell line (DLKP) and several subtypes. Adriamycin normally kills by blocking replication at DNA gyrase and by generating reactive oxygen species that lead to apoptosis. Proteomes were examined by 2‐D DIGE. Approximately 80 proteins displayed quantitative shifts, 32 showed a correlation with resistance, 24 being linked positively to resistance, 6 correlated negatively. Some known targets did not appear on the 2‐D maps consistently. Keenan, J. et al., Proteomics 2009, 9, 1556‐1566. An image of spit Spitting images have been around for a long time. The phrase is possibly human‐kind's first recognition of genetically transmitted traits. Proteomic analysis of saliva has only developed recently. The question raised by Walz et al. here is “What is the possible contribution of saliva to the high level of infection by Helicobacter pylori?” H. pylori is known to have extracellular adhesins that bind to a number of salivary proteins. A convenient way to detect targets of adhesins was found to be incubating 1‐D and 2‐D PAGE Western blots with an overlay of whole H. pylori. Targets detected included mucins, sialic acid‐containing glycoproteins, fucose‐containing blood group antigens and each pair of salivary glands had a different binding pattern. Walz, A. et al., Proteomics 2009, 9, 1582‐1592. Mix'em up, folks Conventional analytical chemical identifications frequently yield a characteristic spectrum of peaks for particular compounds on particular instruments. Just look up the observed spectrum in the “library” of standard spectra for identification. It is not so simple for proteins. Because of the size of a potential proteomic peptide library and the diversity of instruments used, most often the observed spectrum is compared to a theoretical spectrum for a peptide of interest. Ahrné et al. combine the two for improved performance. First they run the spectrum of interest through an exhaustive proteome search program (Phenyx), then through a sensitive library search (SpectraST) of the highest scoring sequences in the previous Phenyx search plus a number of controls. In the first (relatively simple) test, Phenyx matched 362 spectra, SpectraST made 639 matches at the same error detection level. In a more complex test, Phenyx generated >1000 hits, SpectraST 1304 hits. Ahrné, E. et al., Proteomics 2009, 9, 1731‐1736.     

A simple workflow to increase MS2 identification rate by subsequent spectral library search

Searching a spectral library for the identification of protein MS/MS data has proven to be a fast and accurate method, while yielding a high identification rate. We investigated the potential to increase peptide discovery rate, with little increase in computational time, by constructing a workflow based on a sequence search with Phenyx followed by a library search with SpectraST. Searching a consensus library compiled from the search results of the prior Phenyx search increased the number of confidently matched spectra by up to 156%. Additionally matched spectra by SpectraST included noisy spectra, spectra representing missed cleaved peptides as well as spectra from post‐translationally modified peptides.     

Cover Picture: Proteomics 7/2009

In this issue of Proteomics you will find the following highlighted articles: Computing clusters and complexes At first glance, the structure of a cell looks like a semi‐random collection of proteins, lipids and nucleic acids. With the development of high‐throughput tools and bioinformatic procedures, we can begin to see some order in the chaos, including relationships that regulate cell functions (the interactome). Carbonell et al. looked at hubs, hot spots, interfaces, modules, complexes, binding site disorder, affinity and alanine scanning in developing a model for the energetics and specificity of protein‐protein interactions. They observed self‐segregation of binding sites by affinity, i.e. specific‐specific and promiscuous‐ promiscuous interactions between hubs are much higher than random association. Examples of low and high affinity energetics are discussed for cytochrome b, cdc42 GTPase, ubiquitin, and calmodulin‐dependent kinase. Calculated values were selectively validated for a reality check. Carbonell, P. et al., Proteomics 2009, 9, 1744‐1753. Pursuing the Plasmodium plague: understanding malaria through homology Plasmodium falciparum is a difficult organism to work with because of its complex life cycle: ring, trophozoite and schizont phases. From recent genome sequencing work, proteins/open reading frames can be selected by homology to look at possible elements of the plasmodium interactome. Wuchty et al. took on the challenge. Information was derived from reliable interaction experiments with S. cerevisiae, D. melanogaster, C. elegans, and E. coli. Homologies were determined by BlastP (all‐vs.‐all). Shared GO annotations were found which added to further understanding of the sparsely annotated parasite. Other parameters examined included Cluster Participation Coefficient, Kernel Density Function, K‐Clique Clustering, and (drum roll please) the Rich‐Club Coefficient. Using the InParanoid yeast database, they found over 1800 interactions among almost 700 yeast proteins. Pooling the four organisms gave 5000 interactions among 1900 proteins. There should be some interesting targets in there . . . Wuchty, S.et al., Proteomics 2009, 9, 1841‐1849 Race to the finish‐aging nerve vs. aging muscle Our image of a “senior citizen” often has a wobbling gait and sagging face. These are both in part the result of muscle atrophy. A good surgeon and $150 000 will get you the Joan Rivers look that should hold you into your 90's. But what about your legs? Tough luck for now. Capitanio et al., however, are looking at the relationship between muscle and nerve breakdown with age using proteomic tools. Studying the gastrocnemius muscle and the sciatic nerve of young (8 month) and older (22 month) rats, the authors found a number of coordinate morphological and metabolic changes in the deterioration of nerves and their linked muscles. Light and electron microscopy, 2‐D DIGE, ESI‐MS/MS MALDI‐TOF, Western immunoblots and immunocytochemistry were all brought to bear on the question. The results were a much clearer understanding of the mechanics of muscle aging. Capitanio, D. et al., Proteomics 2009, 9, 2004‐2020.     

Cover Picture: Proteomics 1/2009

In this issue of Proteomics you will find the following highlighted articles: How many tries before you get it right? British Prime Minister Benjamin Disraeli is reputed to have stated that “There are three types of lies: lies, damned lies and statistics.” As those immersed in bioinformatics have recognized, though they may be slippery characters, statistics are the only way some information can be extracted from an experimental structure. One of the recurring problems is the question of how many samples need to be tested to get a reasonable, reliable result. This is particularly important when samples are difficult to get, require arduous preparation, or yield only small amounts. These experiments are generally multidimensional. In this article Cairns et al., examine the number of mass spectrometry samples that are required for a quantitative answer in a biomarker search. They evaluate MALDI‐TOF and SELDI‐TOF data for sources and amounts of variability on a pilot scale (biological and technical particularly) which allows them to calculate the number of samples required for a valid full‐scale screen. Cairns, D. A. et al., Proteomics 2009, 9, 74‐86. Double‐barreled proteomic run on embryonic stem cell membranes Embryonic stem cells (ESC) appear to be as close to the fountain of youth as most of us can reasonably expect to get in this lifetime. How close they come to being a “silver bullet” for cancer and other diseases is yet to be determined. Intoh et al., have taken a major step forward in improving our understanding of ESC control and maintenance. They applied 2‐D DIGE and trypsin digestion + iTRAQ labeling to identify membrane and membrane‐associated proteins in mouse ESCs that had or had not been exposed to leukemia inhibitory factor, a factor which maintains pluripotency in ESCs. Some 338 membrane and membrane‐associated proteins, up‐ or down‐regulated, were identified and assigned to functional groups. Intoh, A. et al., Proteomics 2009, 9, 126‐137. H, M, L You see these three letters on a variety of simple controllers: pump speed, temperature, under‐desk foot warmers, etc. Now you can hope to see them soon on bottles in a cell mass isotope labeling kit. Schwanhäusser et al., describe here a protocol for following levels of protein expression in array volumes and numbers with array simplicity. They pulse label samples with Heavy, Medium, or Light amino acids. Pulse‐labeling has been used for determining protein turnover rates for eons but with a quantitation problem for translation: did the ratio change because the numerator changed or because the denominator changed? The answer comes from labeling the untreated control with the M amino acid, then mixing M+H or M+L samples before fractionating by SDS‐PAGE and high‐resolution LC‐MS/MS. It worked for cell fractions (HeLa) as well as whole cells (yeast). Schwanhäusser, B. et al., Proteomics 2009, 9, 205‐209.     

Refining spectral library searching

Spectral library searching has many advantages over sequence database searching, yet it has not been widely adopted. One possible reason for this is that users are unsure exactly how to interpret the similarity scores (e.g., “dot products” are not probability‐based scores). Methods to create decoys have been proposed, but, as developers caution, may produce proxies that are not equivalent to reversed sequences. In this issue, Shao et al. (Proteomics 2013, 13, 3273–3283) report advances in spectral library searching where the focus is not on improving the performance of their search engine, SpectraST, but is instead on improving the statistical meaningfulness of its discriminant score and removing the need for decoys. The results in their paper indicate that by “standardizing” the input and library spectra, sensitivity is not lost but is, surprisingly, gained. Their tests also show that false discovery rate (FDR) estimates, derived from their new score, track better with “ground truth” than decoy searching. It is possible that their work strikes a good balance between the theory of library searching and its application. And as such, they hope to have removed a major entrance barrier for some researchers previously unwilling to try library searching.     

In this issue: Proteomics 8/2008

In this issue of Proteomics you will find the following highlighted articles: Have a heart (mitochondrial) proteome Is a rose always a rose? How clean is clean? Is a proteome always a proteome? Such deep questions to ponder. Zhang et al. don't just ponder, they attack the last two questions. Taking meticulous care to prepare clean mouse cardiac mitochondria, they identify almost a thousand proteins from the functionally and morphologically validated organelle. Half of the proteins had not been previously identified. Functional clusters include the expected and the “under‐appreciated” – proteolysis, protein folding, apoptosis and redox signaling. A close association with rough ER could not be disrupted without damage to the outer mitochondrial membrane. Immunocytological localization of many of the proteins revealed roles in other sites as well, including ER, cytoplasm, and Golgi. Comparative analysis of published mitochondrial proteomes from different tissues suggests that the proteomes are functionally adapted to their particular milieu. A mitochondrion (heart) is not a mitochondrion (liver). Zhang, J. et al., Proteomics 2008, 8, 1564–1575. Ibuprofen: split personality complicates proteome analyses Ibuprofen is one of those two‐fisted drugs that comes in an S form and an R form. The S form of this nonsteroidal anti‐inflammatory drug (NSAID) is the only active one, in this case. Normally sold over the counter for general aches and pains in the US, statistical analysis of its regular users has found it associated with a reduced incidence of Alzheimer's disease. Following up on this lead, Zhang et al. performed proteomic analysis of the effect of the R and S forms and their mixture on neuroblastoma cells. From three replicates, 167 proteins were identified as being quantitatively shifted. A total of 13 were unique. Functionally, they included representatives from metabolic enzymes (5), signaling (6), and cytoskeleton (2). Of interest for the Alzheimer's association was the reduced levels of reactive oxygen species (ROS), probably linked to levels of peroxiredoxins 2 and 6 in ibuprofen S‐treated cells. Zhang, J. et al., Proteomics 2008, 8, 1595–1607. Not your usual marine bacterium Rhodopirellula baltica is a member of the Planctomycetes phylum. These bacteria exhibit a proteinaceous cell wall, budding cell division, and intracellular compartments. From genome sequencing, it has >7300 ORFs. Analyzing the soluble proteins over the range of pH 3–10 by 2‐D PAGE, using narrow range pH gradient gels, nHPLC‐MS, and 1‐D SDS‐PAGE, Hieu et al. added 709 proteins to the proteins identified previously to bring the total identified to 1267, 17% of the predicted total ORFs. Gel‐free analysis (multiple dimension LC‐MS) yielded 145 proteins not seen in gel‐based methods. Both 1‐D and gel‐free methods were used for identification of cell wall and ribosomal proteins. Ninety three proteins were identified in the cell wall proteome and 13 extracellular proteins. No support was found for the hypothesis that R. baltica fed on sinking dead “marine snow” organisms by secreting proteases. Hieu, C. X. et al., Proteomics 2008, 8, 1608–1623.     

In this issue: Proteomics 7/2009

In this issue of Proteomics you will find the following highlighted articles: Computing clusters and complexes At first glance, the structure of a cell looks like a semi‐random collection of proteins, lipids and nucleic acids. With the development of high‐throughput tools and bioinformatic procedures, we can begin to see some order in the chaos, including relationships that regulate cell functions (the interactome). Carbonell et al. looked at hubs, hot spots, interfaces, modules, complexes, binding site disorder, affinity and alanine scanning in developing a model for the energetics and specificity of protein‐protein interactions. They observed self‐segregation of binding sites by affinity, i.e. specific‐specific and promiscuous‐ promiscuous interactions between hubs are much higher than random association. Examples of low and high affinity energetics are discussed for cytochrome b, cdc42 GTPase, ubiquitin, and calmodulin‐dependent kinase. Calculated values were selectively validated for a reality check. Carbonell, P. et al., Proteomics 2009, 9, 1744‐1753. Pursuing the Plasmodium plague: understanding malaria through homology Plasmodium falciparum is a difficult organism to work with because of its complex life cycle: ring, trophozoite and schizont phases. From recent genome sequencing work, proteins/open reading frames can be selected by homology to look at possible elements of the plasmodium interactome. Wuchty et al. took on the challenge. Information was derived from reliable interaction experiments with S. cerevisiae, D. melanogaster, C. elegans, and E. coli. Homologies were determined by BlastP (all‐vs.‐all). Shared GO annotations were found which added to further understanding of the sparsely annotated parasite. Other parameters examined included Cluster Participation Coefficient, Kernel Density Function, K‐Clique Clustering, and (drum roll please) the Rich‐Club Coefficient. Using the InParanoid yeast database, they found over 1800 interactions among almost 700 yeast proteins. Pooling the four organisms gave 5000 interactions among 1900 proteins. There should be some interesting targets in there . . . Wuchty, S.et al., Proteomics 2009, 9, 1841‐1849 Race to the finish‐aging nerve vs. aging muscle Our image of a “senior citizen” often has a wobbling gait and sagging face. These are both in part the result of muscle atrophy. A good surgeon and $150 000 will get you the Joan Rivers look that should hold you into your 90's. But what about your legs? Tough luck for now. Capitanio et al., however, are looking at the relationship between muscle and nerve breakdown with age using proteomic tools. Studying the gastrocnemius muscle and the sciatic nerve of young (8 month) and older (22 month) rats, the authors found a number of coordinate morphological and metabolic changes in the deterioration of nerves and their linked muscles. Light and electron microscopy, 2‐D DIGE, ESI‐MS/MS MALDI‐TOF, Western immunoblots and immunocytochemistry were all brought to bear on the question. The results were a much clearer understanding of the mechanics of muscle aging. Capitanio, D. et al., Proteomics 2009, 9, 2004‐2020.     

Cover Picture: Proteomics 8/2008

In this issue of Proteomics you will find the following highlighted articles: Have a heart (mitochondrial) proteome Is a rose always a rose? How clean is clean? Is a proteome always a proteome? Such deep questions to ponder. Zhang et al. don't just ponder, they attack the last two questions. Taking meticulous care to prepare clean mouse cardiac mitochondria, they identify almost a thousand proteins from the functionally and morphologically validated organelle. Half of the proteins had not been previously identified. Functional clusters include the expected and the “under‐appreciated” – proteolysis, protein folding, apoptosis and redox signaling. A close association with rough ER could not be disrupted without damage to the outer mitochondrial membrane. Immunocytological localization of many of the proteins revealed roles in other sites as well, including ER, cytoplasm, and Golgi. Comparative analysis of published mitochondrial proteomes from different tissues suggests that the proteomes are functionally adapted to their particular milieu. A mitochondrion (heart) is not a mitochondrion (liver). Zhang, J. et al., Proteomics 2008, 8, 1564–1575. Ibuprofen: split personality complicates proteome analyses Ibuprofen is one of those two‐fisted drugs that comes in an S form and an R form. The S form of this nonsteroidal anti‐inflammatory drug (NSAID) is the only active one, in this case. Normally sold over the counter for general aches and pains in the US, statistical analysis of its regular users has found it associated with a reduced incidence of Alzheimer's disease. Following up on this lead, Zhang et al. performed proteomic analysis of the effect of the R and S forms and their mixture on neuroblastoma cells. From three replicates, 167 proteins were identified as being quantitatively shifted. A total of 13 were unique. Functionally, they included representatives from metabolic enzymes (5), signaling (6), and cytoskeleton (2). Of interest for the Alzheimer's association was the reduced levels of reactive oxygen species (ROS), probably linked to levels of peroxiredoxins 2 and 6 in ibuprofen S‐treated cells. Zhang, J. et al., Proteomics 2008, 8, 1595–1607. Not your usual marine bacterium Rhodopirellula baltica is a member of the Planctomycetes phylum. These bacteria exhibit a proteinaceous cell wall, budding cell division, and intracellular compartments. From genome sequencing, it has >7300 ORFs. Analyzing the soluble proteins over the range of pH 3–10 by 2‐D PAGE, using narrow range pH gradient gels, nHPLC‐MS, and 1‐D SDS‐PAGE, Hieu et al. added 709 proteins to the proteins identified previously to bring the total identified to 1267, 17% of the predicted total ORFs. Gel‐free analysis (multiple dimension LC‐MS) yielded 145 proteins not seen in gel‐based methods. Both 1‐D and gel‐free methods were used for identification of cell wall and ribosomal proteins. Ninety three proteins were identified in the cell wall proteome and 13 extracellular proteins. No support was found for the hypothesis that R. baltica fed on sinking dead “marine snow” organisms by secreting proteases. Hieu, C. X. et al., Proteomics 2008, 8, 1608–1623.     

Cover Picture: Proteomics 13/2008

In this issue of Proteomics you will find the following highlighted articles: Mini pig kidney pie? A lot of antigens to chew on Miniature pigs have been of interest as potential organ xeno‐transplant donors for a number of years but mostly without success. A galactosyl transferase gene knock‐out heart lasted for 6 months, but then succumbed to vascular rejection, indicating previously unrecognized antigens. Kim, et al. applied current glycome analysis techniques to mini‐pig kidney surface antigens. They found an abundance of new ones–over 100 N‐glycans total, some sialylated, some neutral, some never reported before. The structures of many were determined and relatively quantitated. What was sauce for the kidney was not necessarily sauce for the heart. The information gathered and the questions raised will keep transplanters chewing for a long time. Y.‐G. Kim et al., Proteomics 2008, 8, 2596–2610. PACE‐ing along with the DUKX that are really hamsters Turning a marching band or moving it through a bottleneck requires different speeds at different points across the ranks. So does maximal production of biologically produced pharmaceuticals. Here Meleady, et al. use 2‐D DIGE technology to look at the required proteins and the levels of expression required for optimal production of human bone morphogenetic protein 2 (rhBMP‐2) in Chinese hamster ovary‐derived cell lines (CHO DUKX and engineered derivatives). Maturation of BMP‐2 requires the action of PACE (paired basic amino acid cleaving enzyme) and PACE levels are improved by co‐transfection with a soluble PACE gene. With high levels of PACE activity, yields of BMP‐2 improved 4‐fold. PACEsol enhances production of a variety of other proteins as well. Comparison of DUKX‐BMP‐2 cells expressing vs. not expressing PACEsol showed ～180 differentially expressed proteins, 60 identified, that were assigned to a number of functional categories. P. Meleady et al., Proteomics 2008, 8, 2611–2624. Ever deeper into cheesy secretome Kluyveromyces lactis, a budding yeast related to Saccharomyces cerevisiae, is of genetic and industrial interest. Its name comes from its ability to convert sweet milk to sour by fermentation of lactose to lactic acid, not quite the same as glucose to ethanol, but useful nonetheless. Industrially, it has been engineered to produce a vegetarian rennet for cheese‐making as well as other secreted protein products. Swaim, et al. compared the proteins in spent fermentation broth of the industrial expression strain K. lactis GG799 to the predicted secretion products based on genome sequence information and to predicted secretions from Candida albicans and S. cerevisiae. Using multidimensional LC‐MS/MS to analyze tryptic digests, they found 81 secreted products out of 178 predicted. Twenty‐six of those did not exhibit an N‐terminal secretion signal, suggesting that there are alternative pathways to the cell surface. An intracellular nano‐Swiss, perhaps? C. L. Swaim et al., Proteomics 2008, 8, 2714–2723.     

In this issue: Proteomics 8/2009

In this issue of Proteomics you will find the following highlighted articles: Are you sick or are you just getting old: Does it matter? We all joke about the hazards of aging: various systems that break down, some you didn't even know you had. But then there's the alternative of not aging. Hmmm. Now what if aging were a disease? If so, it is worse than any cold I've had. Zürbig et al. have found that the proteome of the aging kidney has many markers in common with chronic kidney disease. The degree of match among small peptide markers ranged from 4% to 22% for IgA nephropathy to diabetic nephropathy, respectively. From these data they developed an age estimating scale that revealed some individuals had kidneys apparently “older” than their bodies. If these findings hold up, they could offer new approaches to diagnosis and therapy of chronic kidney diseases. Zürbig, P. et al., Proteomics 2009, 9, 2108‐2117. Sharing your niche with an unrelated species Anyone who's ever lived with a roommate knows the pain of dividing up the refrigerator space and the cleaning duties as well as the rent. Is it based on number of people, the size of bedrooms, or size of biceps? Many “free‐living” bacteria share their living space with other species in stable consortia to which each member contributes. Bobadilla Fazzini et al. use proteomic and other tools to examine the changes resulting from shifts in limiting carbon sources. Their system is a continuous culture of 9:1 Pseudomonas reinekei (MT1): Achromobacter xylosoxidans (MT3), cultured from a contaminated stream and able to grow on 4‐chlorosalicylate, an intermediate in the degradation of toxic furans and dioxins. MT1 OprF, the outer membrane protein and homolog of E. coli OmpA, is a “slow porin” that contributes to toxin resistance. After a shift in carbon sources, MT1 OprF was up‐regulated ～11‐fold in mixed culture vs. pure culture. Bobadilla Fazzini, R. A. et al., Proteomics 2009, 9, 2273‐2285. Heart to heart: Biomarkers for MACE Mace is a spice, not an herb. It is a badge of office and a weapon (albeit now of a defensive sort). It is also an acronym for a Major Adverse Cardiac Event, otherwise known as a big heart attack, something you want to know is coming and to prevent. So what to do? Biomarkers to the rescue. Currently the FDA has approved one prospective test: the CardioMPO? ELISA test for myeloperoxidase. The MPO marker is >60% accurate in predicting a MACE over 30 days and 6 months. Zhou et al. propose an alternative statistical method for evaluating a panel of mass spectrometry markers. An improved preprocessing procedure utilizes low‐level signal processing and spectrum cleanup routines followed by partial least squares logistic regression and support vector machine classifier to select the markers. The prediction is done by an improved genetic algorithm with local optimization. Using seven markers yields >75% accuracy. Zhou, X. et al., Proteomics 2009, 9, 2286‐2294.     

When 2D is not enough,go for an extra dimension

The use of an extra SDS separation in a different buffer system provide a technique for deconvoluting 2D gel spots made of several proteins (Colignon et al. Proteomics, 2013, 13, 2077–2082). This technique keeps the quantitative analysis of the protein amounts and combines it with a strongly improved identification process by mass spectrometry, removing identification ambiguities in most cases. In some favorable cases, posttranslational variants can be separated by this procedure. This versatile and easy to use technique is anticipated to be a very valuable addition to the toolbox used in 2D gel‐based proteomics.     

Cover Picture: Proteomics 8/2009

In this issue of Proteomics you will find the following highlighted articles: Are you sick or are you just getting old: Does it matter? We all joke about the hazards of aging: various systems that break down, some you didn't even know you had. But then there's the alternative of not aging. Hmmm. Now what if aging were a disease? If so, it is worse than any cold I've had. Zürbig et al. have found that the proteome of the aging kidney has many markers in common with chronic kidney disease. The degree of match among small peptide markers ranged from 4% to 22% for IgA nephropathy to diabetic nephropathy, respectively. From these data they developed an age estimating scale that revealed some individuals had kidneys apparently “older” than their bodies. If these findings hold up, they could offer new approaches to diagnosis and therapy of chronic kidney diseases. Zürbig, P. et al., Proteomics 2009, 9, 2108‐2117. Sharing your niche with an unrelated species Anyone who's ever lived with a roommate knows the pain of dividing up the refrigerator space and the cleaning duties as well as the rent. Is it based on number of people, the size of bedrooms, or size of biceps? Many “free‐living” bacteria share their living space with other species in stable consortia to which each member contributes. Bobadilla Fazzini et al. use proteomic and other tools to examine the changes resulting from shifts in limiting carbon sources. Their system is a continuous culture of 9:1 Pseudomonas reinekei (MT1): Achromobacter xylosoxidans (MT3), cultured from a contaminated stream and able to grow on 4‐chlorosalicylate, an intermediate in the degradation of toxic furans and dioxins. MT1 OprF, the outer membrane protein and homolog of E. coli OmpA, is a “slow porin” that contributes to toxin resistance. After a shift in carbon sources, MT1 OprF was up‐regulated ～11‐fold in mixed culture vs. pure culture. Bobadilla Fazzini, R. A. et al., Proteomics 2009, 9, 2273‐2285. Heart to heart: Biomarkers for MACE Mace is a spice, not an herb. It is a badge of office and a weapon (albeit now of a defensive sort). It is also an acronym for a Major Adverse Cardiac Event, otherwise known as a big heart attack, something you want to know is coming and to prevent. So what to do? Biomarkers to the rescue. Currently the FDA has approved one prospective test: the CardioMPO? ELISA test for myeloperoxidase. The MPO marker is >60% accurate in predicting a MACE over 30 days and 6 months. Zhou et al. propose an alternative statistical method for evaluating a panel of mass spectrometry markers. An improved preprocessing procedure utilizes low‐level signal processing and spectrum cleanup routines followed by partial least squares logistic regression and support vector machine classifier to select the markers. The prediction is done by an improved genetic algorithm with local optimization. Using seven markers yields >75% accuracy. Zhou, X. et al., Proteomics 2009, 9, 2286‐2294.     

In this issue: Proteomics 13/2008

In this issue of Proteomics you will find the following highlighted articles: Mini pig kidney pie? A lot of antigens to chew on Miniature pigs have been of interest as potential organ xeno‐transplant donors for a number of years but mostly without success. A galactosyl transferase gene knock‐out heart lasted for 6 months, but then succumbed to vascular rejection, indicating previously unrecognized antigens. Kim, et al. applied current glycome analysis techniques to mini‐pig kidney surface antigens. They found an abundance of new ones–over 100 N‐glycans total, some sialylated, some neutral, some never reported before. The structures of many were determined and relatively quantitated. What was sauce for the kidney was not necessarily sauce for the heart. The information gathered and the questions raised will keep transplanters chewing for a long time. Y.‐G. Kim et al., Proteomics 2008, 8, 2596–2610. PACE‐ing along with the DUKX that are really hamsters Turning a marching band or moving it through a bottleneck requires different speeds at different points across the ranks. So does maximal production of biologically produced pharmaceuticals. Here Meleady, et al. use 2‐D DIGE technology to look at the required proteins and the levels of expression required for optimal production of human bone morphogenetic protein 2 (rhBMP‐2) in Chinese hamster ovary‐derived cell lines (CHO DUKX and engineered derivatives). Maturation of BMP‐2 requires the action of PACE (paired basic amino acid cleaving enzyme) and PACE levels are improved by co‐transfection with a soluble PACE gene. With high levels of PACE activity, yields of BMP‐2 improved 4‐fold. PACEsol enhances production of a variety of other proteins as well. Comparison of DUKX‐BMP‐2 cells expressing vs. not expressing PACEsol showed ～180 differentially expressed proteins, 60 identified, that were assigned to a number of functional categories. P. Meleady et al., Proteomics 2008, 8, 2611–2624. Ever deeper into cheesy secretome Kluyveromyces lactis, a budding yeast related to Saccharomyces cerevisiae, is of genetic and industrial interest. Its name comes from its ability to convert sweet milk to sour by fermentation of lactose to lactic acid, not quite the same as glucose to ethanol, but useful nonetheless. Industrially, it has been engineered to produce a vegetarian rennet for cheese‐making as well as other secreted protein products. Swaim, et al. compared the proteins in spent fermentation broth of the industrial expression strain K. lactis GG799 to the predicted secretion products based on genome sequence information and to predicted secretions from Candida albicans and S. cerevisiae. Using multidimensional LC‐MS/MS to analyze tryptic digests, they found 81 secreted products out of 178 predicted. Twenty‐six of those did not exhibit an N‐terminal secretion signal, suggesting that there are alternative pathways to the cell surface. An intracellular nano‐Swiss, perhaps? C. L. Swaim et al., Proteomics 2008, 8, 2714–2723.     

In this issue: Proteomics 9/2009

In this issue of Proteomics you will find the following highlighted articles: Rafting on the pond It seems that any river with a drop of more than 20‐30 cm/km is a candidate for a commercially viable rafting business. Biochemical rafters are pickier. They need a detergent‐resistant lipid raft where they can set up their signaling system. Kim et al. examined the changes in the raft molecules involved in insulin stimulated pre‐adipocyte to adipocyte differentiation (adipogenesis). A substantial number of adipocyte raft‐specific proteins were identified by immunoblots and confirmed by 2‐DE MS. A protein of particular interest was gC1qR, specific for mature adipocyte rafts, which also binds complement C1q and a number of other extracellular proteins (vitronectin, fibrinogen, hyaluronic acids . . .). Down‐regulation of gC1qR by siRNA was paralleled by reduction of insulin signaling through gC1qR, through the insulin receptor, and prevented adipogenesis. The rafts also were home to a variety of mitochondrial proteins during adipogenesis. Kim, K.‐B. et al., Proteomics 2009, 9, 2373‐2382. E. coli chaperone SurA is recognized SurA was a sad protein. It was sad because it couldn't get promoted without proof that it had done a good job on its current assignment. But what was that assignment? Being a good little protein, it did its best to never make a mistake and its good was very good, making thousands of perfect cycles. Still, no‐one noticed. Then one day, Vertommen et al. decided to give SurA a rest (actually its clone rested). After creating the deletion clone, they fired up the proteome machines to see what had changed. The lab was quiet as the proteomers collected their results. They sat down with the data and looked and talked, studied and talked. They finally came to a conclusion: SurA was indeed a chaperone and was responsible for transport of eight important bbarrel proteins across the periplasmic space to the outer membrane! And now a publication! Vertommen, D.. et al., Proteomics 2009, 9, 2432‐2443. Aphid saliva: solvent, glue, caulk, . . . Children learn quickly that if they don't wash their faces properly, a mother's wet thumb will finish the job. If hair won't stay where it belongs, you can always use saliva. Spots on your glasses or your computer monitor? Aphids and mosquitoes extend the uses even further. Carolan et al. report on the active components of saliva of the pea aphid (Acrythosiphon pisum), an agricultural pest that attacks legumes. The researchers used mass spectrometry, RNAi, and various types of electrophoresis to identify the nine proteins secreted in pea aphid saliva. From the complete genome sequence, four proteins could be identified by homology: a metalloprotease [M2], a zinc [M1] protease, both probably cleaving plant defensive peptides, a glucose oxidoreductase that probably detoxifies phytochemicals, and a relative of regucalsin, which might suppress Ca+2 mediated defense. Three of the proteins could not be matched to any known proteins. Carolan, J. C. et al., Proteomics 2009, 9, 2457‐2467.     

In this issue: Proteomics 1/2008

In this issue of Proteomics you will find the following highlighted articles: Arachnophilia: A Charlotte working on the web In the children’s book Charlotte’s Web, a spider communicates with a pig by weaving messages into her web. In this Technical Brief, Mayer’s spider is the intermediate, a program taking queries about the protein world and weaving relevant information from the www’s libraries and databases into spreadsheets. PIC (Protein Information Crawler) can link directly to a number of databases including BLAST, SMART, PROSITE, and CDD. Selected data is deposited in an Excel spreadsheet or HTML table for sorting and browsing. The system is customizable to anyone with minimal programming skills in LabView G, an easy‐to‐learn graphical language. Using PIC reduced the initial data search for a system of ～1000 neural proteins from 8 wks to 2 days. The software is free. Mayer, U., Proteomics 2008, 8, 42–44. Hard heart, soft heart: analyzing tropomyosin links to types of cardiomyopathy I don’t know if the type of a heart patient’s cardiomyopathy has been diagnosed by behavioral observations but Warren et al. examined the behavior of tropomyosin on improved 2‐D PAGE and 2‐D DIGE separations. First dimension separations were run on 18‐cm long narrow range (pH 4.5 to pH 5.5) IPG strips. Second dimension gels were 16 cm wide, 1 mm thick, and 8 cm long. Ends of the IPG strips were trimmed off to fit the vertical gel. The equilibrated strip was put in place without agarose on top of stacking and resolving gels that included 10% glycerol and, in the stacking gel, 15% N,N’‐diallyltartardiamide to ensure efficient transfer of the protein from the first‐ to the second‐dimension gel. With these changes they were able to distinguish wild type tropomyosin from an E54K mutant and phosphorylated from unphosphorylated tropomyosin, potentially key prognostic clues. Warren, C. M. et al., Proteomics 2008, 8, 100–105. Moo‐ving into ART: Cows lead the way Cow ART is not the product of a bovine Moonet or Moodigliani, it is “Assist­ed Reproductive Technology.” Not simply artificial insemination, ART includes somatic cell nuclear transfer and other advanced techniques which are critical to creating breeding herds with “elite” genetics. But the success rate is not what was expected or required for effective use. Riding et al. apply proteome analysis techniques to establish a foundation for pregnancy progress biomarkers. Ruminants have two fluid‐filled sacs, amniotic and allantoic, that are critical to fetal development. After developing an improved sample prep procedure, the 5–50 kDa fraction of the allantoic proteome was analyzed. Some 139 proteins were identified and ontologically classified into nine functional groups. Too little amniotic fluid was recovered for thorough analysis but the two fluids were clearly distinguishable at 45 days post‐conception. Riding, G. et al., Proteomics 2008, 8, 160–177.     

Cover Picture: Proteomics 9/2009

In this issue of Proteomics you will find the following highlighted articles: Rafting on the pond It seems that any river with a drop of more than 20‐30 cm/km is a candidate for a commercially viable rafting business. Biochemical rafters are pickier. They need a detergent‐resistant lipid raft where they can set up their signaling system. Kim et al. examined the changes in the raft molecules involved in insulin stimulated pre‐adipocyte to adipocyte differentiation (adipogenesis). A substantial number of adipocyte raft‐specific proteins were identified by immunoblots and confirmed by 2‐DE MS. A protein of particular interest was gC1qR, specific for mature adipocyte rafts, which also binds complement C1q and a number of other extracellular proteins (vitronectin, fibrinogen, hyaluronic acids . . .). Down‐regulation of gC1qR by siRNA was paralleled by reduction of insulin signaling through gC1qR, through the insulin receptor, and prevented adipogenesis. The rafts also were home to a variety of mitochondrial proteins during adipogenesis. Kim, K.‐B. et al., Proteomics 2009, 9, 2373‐2382. E. coli chaperone SurA is recognized SurA was a sad protein. It was sad because it couldn't get promoted without proof that it had done a good job on its current assignment. But what was that assignment? Being a good little protein, it did its best to never make a mistake and its good was very good, making thousands of perfect cycles. Still, no‐one noticed. Then one day, Vertommen et al. decided to give SurA a rest (actually its clone rested). After creating the deletion clone, they fired up the proteome machines to see what had changed. The lab was quiet as the proteomers collected their results. They sat down with the data and looked and talked, studied and talked. They finally came to a conclusion: SurA was indeed a chaperone and was responsible for transport of eight important bbarrel proteins across the periplasmic space to the outer membrane! And now a publication! Vertommen, D.. et al., Proteomics 2009, 9, 2432‐2443. Aphid saliva: solvent, glue, caulk, . . . Children learn quickly that if they don't wash their faces properly, a mother's wet thumb will finish the job. If hair won't stay where it belongs, you can always use saliva. Spots on your glasses or your computer monitor? Aphids and mosquitoes extend the uses even further. Carolan et al. report on the active components of saliva of the pea aphid (Acrythosiphon pisum), an agricultural pest that attacks legumes. The researchers used mass spectrometry, RNAi, and various types of electrophoresis to identify the nine proteins secreted in pea aphid saliva. From the complete genome sequence, four proteins could be identified by homology: a metalloprotease [M2], a zinc [M1] protease, both probably cleaving plant defensive peptides, a glucose oxidoreductase that probably detoxifies phytochemicals, and a relative of regucalsin, which might suppress Ca+2 mediated defense. Three of the proteins could not be matched to any known proteins. Carolan, J. C. et al., Proteomics 2009, 9, 2457‐2467.