Advances in shotgun proteomics and the analysis of membrane proteomes

The emergence of shotgun proteomics has facilitated the numerous biological discoveries made by proteomic studies. However, comprehensive proteomic analysis remains challenging and shotgun proteomics is a continually changing field. This review details the recent developments in shotgun proteomics and describes emerging technologies that will influence shotgun proteomics going forward. In addition, proteomic studies of integral membrane proteins remain challenging due to the hydrophobic nature in integral membrane proteins and their general low abundance levels. However, there have been many strategies developed for enriching, isolating and separating membrane proteins for proteomic analysis that have moved this field forward. In summary, while shotgun proteomics is a widely used and mature technology, the continued pace of improvements in mass spectrometry and proteomic technology and methods indicate that future studies will have an even greater impact on biological discovery.     

Comparison of a Salmonella typhimurium proteome defined by shotgun proteomics directly on an LTQ-FT and by proteome pre-fractionation on an LCQ-DUO.

Shotgun proteomics is rapidly becoming one of the most efficient and popular tools to examine protein expression in cells. Numerous laboratories now have a wide array of low- and high-performance mass spectrometry instrumentation necessary to complete proteome-wide projects. Often these laboratories have time and financial constraints that prohibit all projects from being conducted on high-performance state-of-the-art mass spectrometers. Here, we compare shotgun proteomic results using a direct 'lyse, digest and analyse' approach on a high-performance mass spectrometer (i.e. the LTQ-FT) with the results from a much lower-performance instrument (i.e. the LCQ-DUO) where, for the latter, various traditional protein pre-fractionation steps and gas-phase fractionation were used to increase the proteome coverage. Our results demonstrate that shotgun proteomic analyses conducted on the lower-performance LCQ-DUO mass spectrometer could adequately characterize a PhoP constitutive strain of Salmonella typhimurium if proteome pre-fractionation steps and gas-phase fractionation were included.     

Characterization of planktonic and biofilm cells from two filamentous cyanobacteria using a shotgun proteomic approach

Abstract

Cyanobacteria promote marine biofouling with significant impacts. A qualitative proteomic analysis, by LC-MS/MS, of planktonic and biofilm cells from two cyanobacteria was performed. Biofilms were formed on glass and perspex at two relevant hydrodynamic conditions for marine environments (average shear rates of 4?s^?1 and 40?s^?1). For both strains and surfaces, biofilm development was higher at 4?s^?1. Biofilm development of Nodosilinea sp. LEGE 06145 was substantially higher than Nodosilinea sp. LEGE 06119, but no significant differences were found between surfaces. Overall, 377 and 301 different proteins were identified for Nodosilinea sp. LEGE 06145 and Nodosilinea sp. LEGE 06119. Differences in protein composition were more noticeable in biofilms formed under different hydrodynamic conditions than in those formed on different surfaces. Ribosomal and photosynthetic proteins were identified in most conditions. The characterization performed gives new insights into how shear rate and surface affect the planktonic to biofilm transition, from a structural and proteomics perspective.     

Quantitative proteomic analyses reveal the dynamics of protein and amino acid accumulation during soybean seed development

Using high throughput tandem mass tag (TMT) based tagging technique, we identified 4172 proteins in three developmental stages: early, mid, and late seed filling. We mapped the identified proteins to metabolic pathways associated with seed filling. The elevated abundance of several kinases was observed from the early to mid-stages of seed filling, indicating that protein phosphorylation was a significant event during this period. The early to late seed filling stages were characterized by an increased abundance of proteins associated with the cell wall, oil, and vacuolar-related processes. Among the seed storage proteins, 7S (β-subunit) and 11S (Gy3, Gy4, Gy5) steadily increased in abundance during early to late stages of seed filling, whereas 2S albumin exhibited a decrease in abundance during the same period. An increased abundance of proteases, senescence-associated proteins, and oil synthesis proteins was observed from the mid to late seed filling stages. The mid to late stages of seed filling was also characterized by a lower abundance of transferases, transporters, Kunitz family trypsin, and protease inhibitors. Two enzymes associated with methionine synthesis exhibited lower abundance from early to late stages. This study unveiled several essential enzymes/proteins related to amino acid and protein synthesis and their accumulation during seed development. All data can be accessed through this link: https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=38784ecbd0854bb3801afc0d89056f84 . (Accession MSV000087577)     

Identification of four proteins from the small subunit of the mammalian mitochondrial ribosome using a proteomics approach

Proteins in the small subunit of the mammalian mitochondrial ribosome were separated by two-dimensional polyacrylamide gel electrophoresis. Four individual proteins were subjected to in-gel Endoprotease Lys-C digestion. The sequences of selected proteolytic peptides were obtained by electrospray tandem mass spectrometry. Peptide sequences obtained from in-gel digestion of individual spots were used to screen human, mouse, and rat expressed sequence tag databases, and complete consensus cDNAs for these species were deduced in silico. The corresponding protein sequences were characterized by comparison to known ribosomal proteins in protein databases. Four different classes of mammalian mitochondrial small subunit ribosomal proteins were identified. Only two of these proteins have significant sequence similarities to ribosomal proteins from prokaryotes. These proteins are homologs to Escherichia coli S9 and S5 proteins. The presence of these newly identified mitochondrial ribosomal proteins are also investigated in the Drosophila melanogaster, Caenorhabditis elegans, and in the genomes of several fungi.     

温度对大豆(Glycine max)种子发育过程中蛋白质、脂肪和淀粉积累过程的影响

目前人们仍不清楚温度是如何影响发育中的大豆（Glycine max L．）种子蛋白质和脂肪积累过程以及基因型不同的大豆是否对温度具有相同的反应。研究拟通过对3个基因型大豆在不同温度处理下,种子发育过程中的蛋白质和脂肪的积累模式研究,以了解温度对种子组分的调节机理。3个基因型大豆品种（Evans,PI132．217,和Proto）种子盆栽在温度为27／20％（中温）的生长箱中生长到开花。在开花后第10天,将其中的一个生长箱的温度调节到35／27℃（高温）;另一个调到20／12℃（低温）。生长在高温和中温条件下的大豆,在开花的第21天开始收集豆荚,每3d取1次样。生长在低温条件下的大豆,在开花的第25天开始收集豆荚,每5d取1次样。结果表明,3个基因型大豆种子均在高温下生长快,成熟早,在中温下生长速率最大,低温下生长速率低但种子生长期延长。当种子获得60％-70％总干重时种子脂肪含量达到最大（中温）,高温使其提前出现,低温则被推后。在低温下,种子中蛋白质和脂肪两者积累模式相同,但蛋白质积累速率低。在高温和中温条件下,种子蛋白质和脂肪的积累模式不同。在种子获得60％～70％的总干重之前,蛋白质和脂肪积累模式相同,但在种子获得60％～70％的总干重之后,蛋白质积累呈上升趋势,而脂肪积累停止或下降。同时在种子发育的晚期伴随着蛋白质含量增加,淀粉和蔗糖含量快速下降。虽然3个基因型大豆种子的蛋白质和脂肪积累模式均明显受温度影响,但在不同温度条件下和不同生长阶段中高蛋白质品种Proto和PI132．217（蛋白质稳定型）蛋白质含量总是高于低蛋白质品种Evans,而且两者差异显著。这一研究表明温度不能改变品种在蛋白质和脂肪合成上的遗传特性。遗传育种在提高大豆种子蛋白质含量上仍起决定作用,但是合理的播种时期在提高大豆种子蛋白质和脂肪含量上也是不可忽视的问题。     

Synthesis and degradation of the major allergens in developing and germinating soybean seed

Gly m Bd 28K,Gly m Bd 30K and Gly m Bd 60K are the major soybean(Glycine max(L.)Merr.)allergens limiting the consumption of a good protein source for sensitive individuals.However,little is known about their temporal-spatial expression during seed development and upon germination.The present data shows that soy allergens accumulated in both the embryonic axes and cotyledon,but expression patterns differed depending on the specific allergen.Allergens accumulated sooner and to a greater level in cotyledons than in embryonic axes.Gly m Bd 28 began at 14 d after flowering,7 to 14 d earlier than Gly m Bd 30K and Gly m Bd 60K.Comparatively,their degradation was faster and more profound in embryonic axes than in cotyledons.Gly m Bd 60K began to decline at 36 h after imbibition and remained detectable up to 108 h in cotyledons.In contrast,the Glym Bd 60K protein was reduced at 24 h,and eventually disappeared at 96 h.In cotyledons Gly m Bd 28K first declined at 24 h,then increased from 36 h to 48 h,followed by its large reduction at 72 h after seed germination.These findings provide useful information on soy allergen biosynthesis and will help move forward towards developing a hypoallergenic soybean for safer food.     

Protein sorting and expression of a unique soybean cotyledon protein,GmSBP, destined for the protein storage vacuole

The initial biochemical characterization of the soybean sucrose-binding protein, GmSBP, within our lab and others produced several incongruous characteristics that required a re-characterization of GmSBP via sequence homology, cell biology, immunolocalization, and semi-quantitative analysis. The GmSBP proteins share amino acid sequence homology as well as putative structural homology with globulin-like seed storage proteins. A comparison to the major soybean seed storage proteins, glycinin and -conglycinin established several storage protein-like characteristics for GmSBP. All three proteins were present in a prevacuolar compartment and protein storage vacuole. All three proteins increased in expression during seed development and are remobilized during germination. Quantitatively, the relative concentrations of GmSBP, -conglycinin (/ subunits), and glycinin (acidic subunits) indicated that GmSBP contributes 19-fold less to the stored nitrogen. The quantitative differences between GmSBP and glycinin may be attributed to the unconserved order and spacing of cis-acting regulatory elements present within the promoter regions. Ultimately, GmSBP is transported to the mature protein storage vacuole. The biological function of GmSBP within the protein storage vacuole remains uncertain, but its localization is a remnant of its evolutionary link to a globulin-like or vicilin-like ancestor that gave rise to the 7S family of storage proteins.     

Tissue proteomics of the low‐molecular weight proteome using an integrated cLC‐ESI‐QTOFMS approach

Analysis of the protein/peptide composition of tissue has provided meaningful insights into tissue biology and even disease mechanisms. However, little has been published regarding top down methods to investigate lower molecular weight (MW) (500–5000 Da) species in tissue. Here, we evaluate a tissue proteomics approach involving tissue homogenization followed by depletion of large proteins and then cLC‐MS (where c stands for capillary) analysis to interrogate the low MW/low abundance tissue proteome. In the development of this method, sheep heart, lung, liver, kidney, and spleen were surveyed to test our ability to observe tissue differences. After categorical tissue differences were demonstrated, a detailed study of this method's reproducibility was undertaken to determine whether or not it is suitable for analyzing more subtle differences in the abundance of small proteins and peptides. Our results suggest that this method should be useful in exploring the low MW proteome of tissues.     

Towards cracking the epigenetic code using a combination of high-throughput epigenomics and quantitative mass spectrometry-based proteomics

High-throughput genomic sequencing and quantitative mass spectrometry (MS)-based proteomics technology have recently emerged as powerful tools, increasing our understanding of chromatin structure and function. Both of these approaches require substantial investments and expertise in terms of instrumentation, experimental methodology, bioinformatics, and data interpretation and are, therefore, usually applied independently from each other by dedicated research groups. However, when applied reiteratively in the context of epigenetics research these approaches are strongly synergistic in nature.     

Metabolically engineered soybean seed with enhanced threonine levels: biochemical characterization and seed‐specific expression of lysine‐insensitive variants of aspartate kinases from the enteric bacterium Xenorhabdus bovienii

Threonine (Thr) is one of a few limiting essential amino acids (EAAs) in the animal feed industry, and its level in feed rations can impact production of important meat sources, such as swine and poultry. Threonine as well as EAAs lysine (Lys) and methionine (Met) are all synthesized via the aspartate family pathway. Here, we report a successful strategy to produce high free threonine soybean seed via identification of a feedback‐resistant aspartate kinase (AK) enzyme that can be over‐expressed in developing soybean seed. Towards this goal, we have purified and biochemically characterized AK from the enteric bacterium Xenorhabdus bovienii (Xb). Site‐directed mutagenesis of XbAK identified two key regulatory residues Glu‐257 and Thr‐359 involved in lysine inhibition. Three feedback‐resistant alleles, XbAK_T359I, XbAK_E257K and XbAK_E257K/T359I, have been generated. This study is the first to kinetically characterize the XbAK enzyme and provide biochemical and transgenic evidence that Glu‐257 near the catalytic site is a critical residue for the allosteric regulation of AK. Furthermore, seed‐specific expression of the feedback‐resistant XbAK_T359I or XbAK_E257K allele results in increases of free Thr levels of up to 100‐fold in R₁ soybean seed when compared to wild‐type. Expression of feedback‐sensitive wild‐type AK did not substantially impact seed Thr content. In addition to high Thr, transgenic seed also showed substantial increases in other major free amino acid (FAA) levels, resulting in an up to 3.5‐fold increase in the total FAA content. The transgenic seed was normal in appearance and germinated well under greenhouse conditions.     

Sowing seasons and drying methods during post-harvest influence the seed vigour of soybean (Glycine max (L.) Merr.)

Experiments were conducted to study the influence of sowing seasons and drying methods on the seed vigour of two spring soybean (Glycine max (L.) Merr.) cultivars. Two cultivars, ‘Huachun18’ and ‘Huachun 14’, were sown in three seasons viz., spring, summer and autumn and the harvested seeds were dried using three different methods. The results showed that soybean sown in spring had a higher number of branches per plant, pods per branch and seed weight, and consequently resulted in higher seed yields than that of soybean sown in autumn or summer seasons. Seeds sown in the autumn season had the lowest values of electrical conductivity during seed imbibitions, higher peroxidase (POD) activity in germinated seedlings and lower contamination by the seed-borne fungi on the MS medium, which indirectly improved the seed vigour, which was followed by summer sown seeds. Seeds sown during the spring season resulted in poor seed vigour. In addition, the effect of drying methods on the seed vigour was also clarified. Seeds that hung for four days before threshing and then air-dried had the poorest seed vigour which was determined by germination, electrical conductivity, POD activity and seed borne fungal growth. There was no difference in seed vigour between other methods, i.e. seeds threshed directly at harvest and then air-dried on a bamboo sifter or concrete floor. These results indicated that autumn sowing soybean and the drying method in which seeds were threshed directly at harvest and then air-dried on a bamboo sifter resulted in higher seed vigour.     

Viral proteomics: global evaluation of viruses and their interaction with the host

Viruses constantly adapt to and modulate the host environment during replication and propagation. Both DNA and RNA viruses encode multifunctional proteins that interact with and modify host cell proteins. While viral genomes were the first complete sequences known, the corresponding proteomes are only now elucidated, with some surprising results. Even more daunting is the task to globally monitor the impact of viral infection on the proteome of the host cell and many technical hurdles must still be overcome in order to facilitate robust and reproducible measurements. Further complicating the picture is the dynamic nature of proteins, including post-translational modifications, enzymatic cleavage and activation or destruction by proteolytic events. Nevertheless, several promising studies have been published using high-throughput methods directly measuring protein abundance. Particularly, quantitative or semiquantitative mass spectrometry-based analysis of viral and cellular proteomes are now being used to characterize viruses and their host interaction. In addition, the full set of interactions between viral and host proteins, the interactome, is beginning to emerge, with often unexpected interactions that need to be carefully validated. In this review, we will discuss two major areas of viral proteomics: first, virion proteomics (such as the protein characterization of viral particles) and second, proteoviromics, including the viral protein interactomics and the quantitative analysis of host cell proteome during viral infection.     

Three‐dimensional genetic networks among seed oil‐related traits,metabolites and genes reveal the genetic foundations of oil synthesis in soybean

Although the biochemical and genetic basis of lipid metabolism is clear in Arabidopsis, there is limited information concerning the relevant genes in Glycine max (soybean). To address this issue, we constructed three‐dimensional genetic networks using six seed oil‐related traits, 52 lipid metabolism‐related metabolites and 54 294 SNPs in 286 soybean accessions in total. As a result, 284 and 279 candidate genes were found to be significantly associated with seed oil‐related traits and metabolites by phenotypic and metabolic genome‐wide association studies and multi‐omics analyses, respectively. Using minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) analyses, six seed oil‐related traits were found to be significantly related to 31 metabolites. Among the above candidate genes, 36 genes were found to be associated with oil synthesis (27 genes), amino acid synthesis (four genes) and the tricarboxylic acid (TCA) cycle (five genes), and four genes (GmFATB1a, GmPDAT, GmPLDα1 and GmDAGAT1) are already known to be related to oil synthesis. Using this information, 133 three‐dimensional genetic networks were constructed, 24 of which are known, e.g. pyruvate–GmPDAT–GmFATA2–oil content. Using these networks, GmPDAT, GmAGT and GmACP4 reveal the genetic relationships between pyruvate and the three major nutrients, and GmPDAT, GmZF351 and GmPgs1 reveal the genetic relationships between amino acids and seed oil content. In addition, GmCds1, along with average temperature in July and the rainfall from June to September, influence seed oil content across years. This study provides a new approach for the construction of three‐dimensional genetic networks and reveals new information for soybean seed oil improvement and the identification of gene function.     

Analysis of transglutaminase protein substrates by functional proteomics

Transglutaminases are calcium-dependent enzymes that catalyze a post-translational modification of proteins through the formation of epsilon -(gamma-glutamyl)lysine bonds. Although specific roles for transglutaminases have been described, recent findings have provided evidence that dysregulation of transglutaminases may contribute to many pathological processes including celiac disease and neurodegenerative diseases. A crucial step in the elucidation of biological and pathological roles of transglutaminases requires the identification of protein substrates. A strategy based on a functional proteomic analysis was set up using two well-characterized biotinylated transglutaminase substrates as affinity probes: 5-(biotinamido)pentylamine and the synthetic biotinylated peptide TVQQEL, the amino- and acyl-donor probes, respectively. A pool of known tissue type transglutaminase protein substrates was selected in order to test the procedure. Results obtained in this paper indicate that the whole strategy can be successfully applied in order to identify transglutaminases protein substrates as well as the amino acid site sensitive toward enzyme activity.     

High-throughput peptide mass fingerprinting of soybean seed proteins: automated workflow and utility of UniGene expressed sequence tag databases for protein identification

Identification of anonymous proteins from two-dimensional (2-D) gels by peptide mass fingerprinting is one area of proteomics that can greatly benefit from a simple, automated workflow to minimize sample contamination and facilitate high-throughput sample processing. In this investigation we outline a workflow employing robotic automation at each step subsequent to 2-D gel electrophoresis. As proof-of-concept, 96 protein spots from a 2-D gel were analyzed using this approach. Whole protein (1 mg) from mature, dry soybean (Glycine max [L.] Merr.) cv. Jefferson seed was resolved by high resolution 2-D gel electrophoresis. Approximately 150 proteins were observed after staining with Coomassie Blue. The rather low number of detected proteins was due to the fact that the dynamic range of protein expression was greater than 100-fold. The most abundant proteins were seed storage proteins which in total represented over 60% of soybean seed protein. Using peptide mass fingerprinting 44 protein spots were identified. Identification of soybean proteins was greatly aided by the use of annotated, contiguous Expressed Sequence Tag (EST) databases which are available for public access (UniGene, ftp.ncbi.nih.gov/repository/UniGene/). Searches were orders of magnitude faster when compared to searches of unannotated EST databases and resulted in a higher frequency of valid, high-scoring matches. Some abundant, non seed storage proteins identified in this investigation include an isoelectric series of sucrose binding proteins, alcohol dehydrogenase and seed maturation proteins. This survey of anonymous seed proteins will serve as the basis for future comparative analysis of seed-filling in soybean as well as comparisons with other soybean varieties.     

Multi-dimensional mass spectrometry-based shotgun lipidomics and the altered lipids at the mild cognitive impairment stage of Alzheimer's disease

Multi-dimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) is a well-developed technology for global lipid analysis, which identifies and quantifies individual lipid molecular species directly from lipid extracts of biological samples. By using this technology, we have revealed three marked changes of lipids in brain samples of subjects with mild cognitive impairment of Alzheimer's disease including sulfatides, ceramides, and plasmalogens. Further studies using MDMS-SL lead us to the identification of the potential biochemical mechanisms responsible for the altered lipids at the disease state, which are thoroughly discussed in this minireview. Specifically, in studies to identify the causes responsible for sulfatide depletion at the mild cognitive impairment stage of Alzheimer's disease, we have found that apolipoprotein E is associated with sulfatide transport and mediates sulfatide homeostasis in the nervous system through lipoprotein metabolism pathways and that alterations in apolipoprotein E-mediated sulfatide trafficking can lead to sulfatide depletion in the brain. Collectively, the results obtained from lipidomic analyses of brain samples provide important insights into the biochemical mechanisms underlying the pathogenesis of Alzheimer's disease.     

Identification and quantification of peptides and proteins secreted from prostate epithelial cells by unbiased liquid chromatography tandem mass spectrometry using goodness of fit and analysis of variance

The proteins secreted by prostate cancer cells (PC3(AR)6) were separated by strong anion exchange chromatography, digested with trypsin and analyzed by unbiased liquid chromatography tandem mass spectrometry with an ion trap. The spectra were matched to peptides within proteins using a goodness of fit algorithm that showed a low false positive rate. The parent ions for MS/MS were randomly and independently sampled from a log-normal population and therefore could be analyzed by ANOVA. Normal distribution analysis confirmed that the parent and fragment ion intensity distributions were sampled over 99.9% of their range that was above the background noise. Arranging the ion intensity data with the identified peptide and protein sequences in structured query language (SQL) permitted the quantification of ion intensity across treatments, proteins and peptides. The intensity of 101,905 fragment ions from 1421 peptide precursors of 583 peptides from 233 proteins separated over 11 sample treatments were computed together in one ANOVA model using the statistical analysis system (SAS) prior to Tukey-Kramer honestly significant difference (HSD) testing. Thus complex mixtures of proteins were identified and quantified with a high degree of confidence using an ion trap without isotopic labels, multivariate analysis or comparing chromatographic retention times.     

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.