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.     

Cover Picture: Proteomics 5/2009

In this issue of Proteomics you will find the following highlighted articles: Heart (pump) broken? Hearts are pumps within pumps within channels and pumps. Calcium is pumped, potassium, sodium, amino acids, and electrons are all pumped, channeled or driven until, finally, blood is pumped. Failure of one or more pumps leads to a heart attack. This report from Zlatkovic et al. looks at the sub‐proteome associated with hypertensive failure of the K⁺ATP channel and associated cardiomyopathy that develops in KIR6.2 knock‐out mice. Out of >900 reproducible 2‐DE spots, 81 displayed significant over‐ or under‐expression, a number of which validated previously proposed interactions with the Kir6.2 channel. Two‐thirds were down‐regulations, including creatine kinase, adenylate kinase, and lactate dehydrogenase. A total of 114 proteins were ontologically mapped into the K⁺ATP‐dependent sub‐proteome and a role in hypertensive heart failure. Interaction mapping found >240 nodes and >1200 interactions/edges. A good foundation for future work. Zlatkovic, J. et al., Proteomics 2009, 9, 1314‐1325. The deeper you dig, the more you find A classical biochemist interested in protein‐protein interactions purifies his protein away from other proteins, seeking the highest “‐fold purification”. A proteomicist, on the other hand, looks for “consistent contamination” – i.e. association – of the protein of interest with other proteins. This requires high resolution separations and high accuracy concentration determinations. You can only work with species with concentrations above the detection limit (DL) for the detection method. 2‐DE MS has a DL of approximately 10^?8 M, LC‐MS/MS is ～10^?10 M and saturating Cy5 dye method is ～10^?13 M. Archakov et al. report on an atomic force microscope technique that can yield a DL of 10^?16 M when the target is irreversibly fixed to the bait to avoid the losses due to dissociation kinetics. At that level, over 1 000 000 different proteins can be seen in human plasma. How many biomarkers do you want? Math warning: more equations than figures. Archakov, A. et al., Proteomics 2009, 9, 1326‐1343. Unexplored territory: a catfish pathogen's proteome As genomic and proteomic tools become more powerful and cheaper per base or peptide, we can expect to see more papers like this one by Dumpala et al., focused on an organism of modest economic value. Each paper will, however, contribute a new niche with alternative adaptations for survival. In this case, we are introduced to Edwardsiella ictaluri, a Gram negative pathogen of farm‐raised channel catfish. Enteric septicemia of catfish is the most frequent disease of the commercially farmed catfish and appears in acute and chronic forms. For the work reported here, the bacteria were grown in culture, washed, lysed and separated by 2‐DE TOF/TOF or 2‐D LC‐MS/MS for peptide identification. The combined methods identified 788 unique proteins, including 73 ribosomal proteins, several protein synthesis factors, tRNA synthases and a number of other proteins that could be assigned by orthology to Escherichia coli or Edwardsiella tarda. Dumpala, P. R. et al., Proteomics 2009, 9, 1353‐1363.     

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.     

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.     

Cover Picture: 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.