In this issue: Proteomics 14/2008

In this issue of Proteomics you will find the following highlighted articles: A spoonful of reality helps the errors go down Physicists predict particles from a theory then go hunting for them. To see if they fit the theory, the scientists make measurements of mass, spin, half‐life, charge, symmetry, etc. Life scientists tend to find particles first, then theorize about what they found. Martinez et al. have developed pattern search/match software that is trained on a set of known data (amino acid sequences) to recognize signal sequences that cause cellular enzymes to modify N‐termini of specified cytosolic proteins. Their accuracy rose dramatically when they expanded the amount of training material and segregated it into modules for the various kingdoms (archaea, fungi, plants, animals, eubacteria). Terminal modifications examined included presence or absence of initial methionine, N‐acetylation, N‐myristoylation, and S‐palmitoylation. The particular modification led to varying degrees of internal accumulation. Martinez, A. et al., Proteomics 2008, 8, 2809–2831. Please don't pet the fish Salmon appear to have no fear of jumping several decimeters out of water on their annual upstream migration but they do seem to have a fear of being lifted out of water for a few seconds once a day. Liu et al. looked at the degree of O‐acetylation of serum N‐glycans of Atlantic salmon over a period of 4 weeks. The salmon N‐glycan pattern was similar to the human. Stress was created by holding the fish out of water for 15 seconds daily. Stressed fish showed a reduced level of mono‐O‐acetylated sialic acids. Di‐O‐acetylated species increased, however, over the 4‐week experimental period. The increase in O‐acetylsialic acid is a potential biomarker for long‐term stress in fish. More work is needed to evaluate the extensibility of these findings. Liu, X. et al., Proteomics 2008, 8, 2849–2857. Cattle and ART and proteomics The ART of cattle raising is not learned in a Parson's School of Bovine Design or Parisian cow ecole, it is “Assisted Reproduction Technologies” and is taught in veterinary programs and schools of agriculture. It includes in vitro fertilization (IVF), somatic cell nuclear transfer (SCNT), and multiple ovulation embryo transfer. Expensive procedures requiring specialized training, they are normally applied only to genetically superior breeding stock. Fetal losses are high — 2× to >10× natural fertilization failure rates. Riding et al. used proteomic technology (2‐D DIGE, MALDI‐TOF‐MS/MS) to look for biomarkers in conceptus fluids. In particular, they sought indicators of fetal‐maternal environment status and fetal health at 45 and 90 days post‐conception for IVF and SCNT. Allantoic fluid samples from 45 days showed elevated levels of cathelicidin antimicrobial protein (CAMP) family members (3 of 4 IVF, up ≤100×; 2 of 4 SCNT, up ≤45×; natural, up ≤6×) and several other proteins (PGLYRP, SERPINB1, COLT1). Riding, G. A. et al., Proteomics 2008, 8, 2967–2982.     

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 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 5/2008

In this issue of Proteomics you will find the following highlighted articles: When is a stain not a stain? When it's dyeing! [Dumb proteomics joke!] This silly riddle is actually relat­ed to a recurrent question in proteomics: when is the best time to apply detection reagents to proteins for quantitative analysis? (a) pre‐electrophoresis labeling with DIGE/Cy‐type of covalent stains, or (b) post‐electrophoresis staining with silver, Sypro Ruby or Deep Purple? Karp et al. explore the question using a bacterial extract as a typical sample, DIGE Cy labels, and Deep Purple. It gets more complex when they have to deal with the “missingness” of spots: just because a spot doesn’t show up doesn’t mean it is not there, there just may not be enough to detect. Progenesis SameSpots software was used to analyze images for missing spots. In the end, DIGE gave better sensitivity as previously reported, and fewer missing spots. Deep Purple was more competitive when analyzed with SameSpots software. Karp, N. A. et al., Proteomics 2008, 8, 948–960. Your own best enemy? If there weren’t one maverick, black sheep, rebel, outlaw, eccentric, or rotten apple in most families, a lot of novels would never have been written. Mammalian immune systems seem to have the same structure – they mostly target enemies of the body but there always seem to be a few maverick antibodies that are targeted at their own body’s antigens. Servettaz et al. take up proteomic tools to identify the targets of the anti‐self antibodies expressed by apparently healthy individuals. Using umbilical cord endothelial cells as a source of antigens, the authors found 884 spots by ­2‐­DE, and 61 ± 25 of those were recognized by serum IgGs. All 12 sera tested recognized 11 antigens derived from 6 proteins. There were 3 cytoskeletal, 2 glycolytic, and 1 disulfide isomerase protein seen. These were confirmed by immunoblotting of 2‐D gels and identification by in‐gel tryptic digestion and MALDI‐TOF MS. Servettaz, A. et al., Proteomics 2008, 8, 1000–1008. Signature in scraps from kidney growth stages You can tell a lot about the quality of a new building, residential or commercial, by what doesn’t go into it. The scraps of lumber, pieces of masonry, lengths and varieties of cables are all revealing. Lee et al. watch the final maturation of the rat urinary tract by proteomic analysis of the debris found in urine over time. Taking special care not to mix adult and neonatal urine, they examined four samples over 2 weeks after birth and one at maturity, >30 d. Using nano‐ESI‐LC‐MS/MS technology, six proteins were found in all samples, 30 were adult specific. Proteins were further characterized by large format 1‐ and 2‐DE, immunoblots, and immunofluorescent analysis of tissue sections. Days 1, 3, and 7 had 37% of proteins in common whereas days 7, 14 and >30 shared only 7.4% of proteins. Levels of fibronectin and location of E‐cadherin expression shifted during maturation. Lee, R. S. et al., Proteomics 2008, 8, 1097–1112.     

In this issue: Proteomics 5/2008

In this issue of Proteomics you will find the following highlighted articles: When is a stain not a stain? When it's dyeing! [Dumb proteomics joke!] This silly riddle is actually relat­ed to a recurrent question in proteomics: when is the best time to apply detection reagents to proteins for quantitative analysis? (a) pre‐electrophoresis labeling with DIGE/Cy‐type of covalent stains, or (b) post‐electrophoresis staining with silver, Sypro Ruby or Deep Purple? Karp et al. explore the question using a bacterial extract as a typical sample, DIGE Cy labels, and Deep Purple. It gets more complex when they have to deal with the “missingness” of spots: just because a spot doesn’t show up doesn’t mean it is not there, there just may not be enough to detect. Progenesis SameSpots software was used to analyze images for missing spots. In the end, DIGE gave better sensitivity as previously reported, and fewer missing spots. Deep Purple was more competitive when analyzed with SameSpots software. Karp, N. A. et al., Proteomics 2008, 8, 948–960. Your own best enemy? If there weren’t one maverick, black sheep, rebel, outlaw, eccentric, or rotten apple in most families, a lot of novels would never have been written. Mammalian immune systems seem to have the same structure – they mostly target enemies of the body but there always seem to be a few maverick antibodies that are targeted at their own body’s antigens. Servettaz et al. take up proteomic tools to identify the targets of the anti‐self antibodies expressed by apparently healthy individuals. Using umbilical cord endothelial cells as a source of antigens, the authors found 884 spots by ­2‐­DE, and 61 ± 25 of those were recognized by serum IgGs. All 12 sera tested recognized 11 antigens derived from 6 proteins. There were 3 cytoskeletal, 2 glycolytic, and 1 disulfide isomerase protein seen. These were confirmed by immunoblotting of 2‐D gels and identification by in‐gel tryptic digestion and MALDI‐TOF MS. Servettaz, A. et al., Proteomics 2008, 8, 1000–1008. Signature in scraps from kidney growth stages You can tell a lot about the quality of a new building, residential or commercial, by what doesn’t go into it. The scraps of lumber, pieces of masonry, lengths and varieties of cables are all revealing. Lee et al. watch the final maturation of the rat urinary tract by proteomic analysis of the debris found in urine over time. Taking special care not to mix adult and neonatal urine, they examined four samples over 2 weeks after birth and one at maturity, >30 d. Using nano‐ESI‐LC‐MS/MS technology, six proteins were found in all samples, 30 were adult specific. Proteins were further characterized by large format 1‐ and 2‐DE, immunoblots, and immunofluorescent analysis of tissue sections. Days 1, 3, and 7 had 37% of proteins in common whereas days 7, 14 and >30 shared only 7.4% of proteins. Levels of fibronectin and location of E‐cadherin expression shifted during maturation. Lee, R. S. et al., Proteomics 2008, 8, 1097–1112.     

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 15/2008

In this issue of Proteomics you will find the following highlighted articles: An old dog refines new tricks Old dogs are reputed to be slow learners but they can be subtle manipulators, able to induce younger dogs and gullible owners to share the food dish in their favor or choose the path they prefer. Two‐dimensional gel electrophoresis has been around for more than 25 years but “new and improved” versions continue to appear. Ericsson et al. scramble the order of several steps to get more information out of the combination of IPG/IEF and “shotgun” peptide analysis. Developed for studying mechanisms of drug resistance in small cell lung cancer, the modified protocol fractionates sonically disrupted cells into microsomes and soluble fractions before tryptic digestion and iTRAQ labeling for later quantitation. Digested samples were fractionated on narrow range immobilized pH gradient strips from which they were eluted for MALDI TOF or LC‐MS/MS analysis. Detection and identification of transmembrane proteins were dramatically improved. Ericsson, H. et al., Proteomics 2008, 8, 3008–3018. An evanescent view of a lectin micro‐array: through a glass faintly Lectins have the ability to distinguish closely‐related carbohydrate moieties attached (or not) to other molecules such as proteins, peptides, lipids, cells, etc. In some respects, they are much like antibodies, just not quite as specific. Using an array of 45 different lectins, the glycan portion of glycoproteins can be identified by its binding profile. Here, Uchiyama et al. report the improvements they have made to the reproducibility and sensitivity of the system. The binding of rhodamine‐labeled probes was detected in an evanescent field fluorescence‐based instrument that was capable of reaching, 10pM levels without having to wash off unbound probe. Depositing lectin spots with a non‐contact type printer, at the right humidity, and blocking with a non‐proteinaceous material greatly improved sensitivity. Uchiyama, N. et al., Proteomics 2008, 8, 3042–3050. The innate defense: multiplex proteomic probing The body's first line of defense against pathogen infection is the innate response. In the case of bacterial infection, it is initiated by the sensing of the universal Gram‐positive cell wall lipopolysaccharide (LPS) component by macrophages primarily (but not solely) through the Toll‐like receptor 4 (TLR4). To understand the regulation of the LPS response, Gu et al. developed a multiplex quantitative proteomic analysis procedure to follow the response of TLR4+ and TLR4? cell lines. The method of choice was amino acid‐coded mass tagging (AACT, also referred to as SILAC). It showed high efficiency of labeling (95%) which eliminated interference with quantitation by the unlabeled fraction. Using triplex labeling of lysine (13C, 15N), the authors confirmed that TLR4? cells did show a response to LPS: 25 proteins were up‐regulated in TLR4+ cells, 5 in TLR4? cells. More than 500 proteins could be quantitated. Gu, S. et al., Proteomics 2008, 8, 3061–3070.     

In this issue: Proteomics 15/2008

In this issue of Proteomics you will find the following highlighted articles: An old dog refines new tricks Old dogs are reputed to be slow learners but they can be subtle manipulators, able to induce younger dogs and gullible owners to share the food dish in their favor or choose the path they prefer. Two‐dimensional gel electrophoresis has been around for more than 25 years but “new and improved” versions continue to appear. Ericsson et al. scramble the order of several steps to get more information out of the combination of IPG/IEF and “shotgun” peptide analysis. Developed for studying mechanisms of drug resistance in small cell lung cancer, the modified protocol fractionates sonically disrupted cells into microsomes and soluble fractions before tryptic digestion and iTRAQ labeling for later quantitation. Digested samples were fractionated on narrow range immobilized pH gradient strips from which they were eluted for MALDI TOF or LC‐MS/MS analysis. Detection and identification of transmembrane proteins were dramatically improved. Ericsson, H. et al., Proteomics 2008, 8, 3008–3018. An evanescent view of a lectin micro‐array: through a glass faintly Lectins have the ability to distinguish closely‐related carbohydrate moieties attached (or not) to other molecules such as proteins, peptides, lipids, cells, etc. In some respects, they are much like antibodies, just not quite as specific. Using an array of 45 different lectins, the glycan portion of glycoproteins can be identified by its binding profile. Here, Uchiyama et al. report the improvements they have made to the reproducibility and sensitivity of the system. The binding of rhodamine‐labeled probes was detected in an evanescent field fluorescence‐based instrument that was capable of reaching, 10pM levels without having to wash off unbound probe. Depositing lectin spots with a non‐contact type printer, at the right humidity, and blocking with a non‐proteinaceous material greatly improved sensitivity. Uchiyama, N. et al., Proteomics 2008, 8, 3042–3050. The innate defense: multiplex proteomic probing The body's first line of defense against pathogen infection is the innate response. In the case of bacterial infection, it is initiated by the sensing of the universal Gram‐positive cell wall lipopolysaccharide (LPS) component by macrophages primarily (but not solely) through the Toll‐like receptor 4 (TLR4). To understand the regulation of the LPS response, Gu et al. developed a multiplex quantitative proteomic analysis procedure to follow the response of TLR4+ and TLR4? cell lines. The method of choice was amino acid‐coded mass tagging (AACT, also referred to as SILAC). It showed high efficiency of labeling (95%) which eliminated interference with quantitation by the unlabeled fraction. Using triplex labeling of lysine (13C, 15N), the authors confirmed that TLR4? cells did show a response to LPS: 25 proteins were up‐regulated in TLR4+ cells, 5 in TLR4? cells. More than 500 proteins could be quantitated. Gu, S. et al., Proteomics 2008, 8, 3061–3070.     

In this issue: 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 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.     

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.     

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.     

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.     

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.     

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.     

Cover Picture: Proteomics 11/2008

In this issue of Proteomics you will find the following highlighted articles: Pancreatic cancer signs autograph on micro antibody array Pancreatic cancer has been one of the nastier members of the “Discovered‐too‐late‐to‐do‐anything‐about‐it” disease club. Its 5‐year survival rate is 3–5 % because of late diagnosis and no effective therapy for advanced disease cases. This paper by Ingvarsson et al. reports their encouraging findings on the use of recombinant antibody microarrays to survey serum for diagnostic and prognostic proteins. In these “proof‐of‐concept” experiments they found a signature of 19 unique scFv antibodies, specific for immunoregulatory proteins, that could distinguish pancreatic cancer from normal and from Helicobacter pylori (an indicator of inflammation, 3 out of 14 overlap). The test panel distinguished long and short survivors (with only one long survivor misclassified). Data was classified using a Support Vector Machine. The classifier was validated by multiple splits of the data and leave‐one‐out tests. Ingvarsson, J. et al., Proteomics 2008, 8, 2211–2219. Of cadmium and zinc: Brothers or not? Cadmium and zinc occupy the same column in the periodic table so you might expect some biological similarities. Not much luck – mercury is also in that column. Zinc, under tight control, is an essential mineral; cadmium is toxic and induces a variety of defensive responses. A highly zinc‐resistant cell line (HZR) has been derived from the human HeLa line. Rousselet et al. have compared the proteomes of HZR and HeLa cultured in Cd and Zn using a variety of proteomic and genomic tools. MALDI‐TOF MS after 2‐DE revealed examples of a co‐chaperone, a heat‐shock organizing protein (Hop), ubiquitin and a number of reactive oxygen species control proteins elevated in HZR. Of special interest was 4‐hydroxyphenyl‐pyruvate dioxygenase (HPPD), catalyst of one of the first breakdown steps of tyrosine. The complex relationships revealed will require a lot more than one paragraph for explanation. Rousselet, E. et al., Proteomics 2008, 8, 2244–2255. Grey box proteomics of salty species In the classic black box experiment you know nothing about the contents of the box. I propose a grey box for experiments directed by homologous knowledge – like these. Pandhal et al. have developed a protocol for proteomic analysis of an unsequenced species by homology. The organism of interest is a halotolerant cyanobacterium, Euhalothece sp. which can grow in NaCl concentrations ranging from 0% to >9% NaCl. The nearest sequenced relative is a Synechocystis sp. By metabolic labeling with ¹⁵N/­¹⁴N, the researchers were able to use MS to match proteins from the two species and also quantitate changes in levels of proteins in response to salt levels. Three labelling experiments ([% NaCl], 0% +3%, 3% +6%, and 3% +9%) yielded 229, 212, and 96 proteins, respectively, by MASCOT search of proteins with two peptides of each isotope. MS BLAST found 32, 30, and 7 more proteins, respectively. Pandhal, J. et al., Proteomics 2008, 8, 2266–2284.     

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.     

In this issue: Proteomics 11/2008

In this issue of Proteomics you will find the following highlighted articles: Pancreatic cancer signs autograph on micro antibody array Pancreatic cancer has been one of the nastier members of the “Discovered‐too‐late‐to‐do‐anything‐about‐it” disease club. Its 5‐year survival rate is 3–5 % because of late diagnosis and no effective therapy for advanced disease cases. This paper by Ingvarsson et al. reports their encouraging findings on the use of recombinant antibody microarrays to survey serum for diagnostic and prognostic proteins. In these “proof‐of‐concept” experiments they found a signature of 19 unique scFv antibodies, specific for immunoregulatory proteins, that could distinguish pancreatic cancer from normal and from Helicobacter pylori (an indicator of inflammation, 3 out of 14 overlap). The test panel distinguished long and short survivors (with only one long survivor misclassified). Data was classified using a Support Vector Machine. The classifier was validated by multiple splits of the data and leave‐one‐out tests. Ingvarsson, J. et al., Proteomics 2008, 8, 2211–2219. Of cadmium and zinc: Brothers or not? Cadmium and zinc occupy the same column in the periodic table so you might expect some biological similarities. Not much luck – mercury is also in that column. Zinc, under tight control, is an essential mineral; cadmium is toxic and induces a variety of defensive responses. A highly zinc‐resistant cell line (HZR) has been derived from the human HeLa line. Rousselet et al. have compared the proteomes of HZR and HeLa cultured in Cd and Zn using a variety of proteomic and genomic tools. MALDI‐TOF MS after 2‐DE revealed examples of a co‐chaperone, a heat‐shock organizing protein (Hop), ubiquitin and a number of reactive oxygen species control proteins elevated in HZR. Of special interest was 4‐hydroxyphenyl‐pyruvate dioxygenase (HPPD), catalyst of one of the first breakdown steps of tyrosine. The complex relationships revealed will require a lot more than one paragraph for explanation. Rousselet, E. et al., Proteomics 2008, 8, 2244–2255. Grey box proteomics of salty species In the classic black box experiment you know nothing about the contents of the box. I propose a grey box for experiments directed by homologous knowledge – like these. Pandhal et al. have developed a protocol for proteomic analysis of an unsequenced species by homology. The organism of interest is a halotolerant cyanobacterium, Euhalothece sp. which can grow in NaCl concentrations ranging from 0% to >9% NaCl. The nearest sequenced relative is a Synechocystis sp. By metabolic labeling with ¹⁵N/­¹⁴N, the researchers were able to use MS to match proteins from the two species and also quantitate changes in levels of proteins in response to salt levels. Three labelling experiments ([% NaCl], 0% +3%, 3% +6%, and 3% +9%) yielded 229, 212, and 96 proteins, respectively, by MASCOT search of proteins with two peptides of each isotope. MS BLAST found 32, 30, and 7 more proteins, respectively. Pandhal, J. et al., Proteomics 2008, 8, 2266–2284.