Proteome analysis by mass spectrometry

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Deep-ultraviolet laser ablation sampling for proteomic analysis of tissue

Deep-ultraviolet laser ablation with a pulsed 193 nm ArF excimer laser was used to remove localized regions from tissue sections from which proteins were extracted for spatially resolved proteomic analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS). The ability to capture intact proteins by ablation at 193 nm wavelength was verified by matrix-assisted laser desorption ionization (MALDI) of the protein standard bovine serum albumin (BSA), which showed that BSA was ablated and captured without fragmentation. A Bradford assay of the ablated and captured proteins indicated 90% efficiency for transfer of the intact protein at a laser fluence of 3 kJ/m². Rat brain tissue sections mounted on quartz microscope slides and ablated in transmission mode yielded 2 μg protein per mm² as quantified by the Bradford assay. Tissue areas ranging from 0.06 mm² to 1 mm² were ablated and the ejected material was collected for proteomic analysis. Extracted proteins were digested and the resulting peptides were analyzed by LC-MS/MS. The proteins extracted from the ablated areas were identified and the average number of identified proteins ranged from 85 in the 0.06 mm² area to 2400 in the 1 mm² area of a 50 μm thick tissue. In comparison to infrared laser ablation of equivalent sampled areas, both the protein mass and number of proteins identified using DUV laser ablation sampling were approximately four times larger.

Analysis of Antimicrobial Peptide Metabolome of Bacterial Endophyte Isolated From Traditionally Used Medicinal Plant Millettia pachycarpa Benth

Increasing prevalence of antimicrobial resistance (AMR) has posed a major health concern worldwide, and the addition of new antimicrobial agents is diminishing due to overexploitation of plants and microbial resources. Inevitably, alternative sources and new strategies are needed to find novel biomolecules to counter AMR and pandemic circumstances. The association of plants with microorganisms is one basic natural interaction that involves the exchange of biomolecules. Such a symbiotic relationship might affect the respective bio-chemical properties and production of secondary metabolites in the host and microbes. Furthermore, the discovery of taxol and taxane from an endophytic fungus, Taxomyces andreanae from Taxus wallachiana, has stimulated much research on endophytes from medicinal plants. A gram-positive endophytic bacterium, Paenibacillus peoriae IBSD35, was isolated from the stem of Millettia pachycarpa Benth. It is a rod-shaped, motile, gram-positive, and endospore-forming bacteria. It is neutralophilic as per Joint Genome Institute’s (JGI) IMG system analysis. The plant was selected based on its ethnobotany history of traditional uses and highly insecticidal properties. Bioactive molecules were purified from P. peoriae IBSD35 culture broth using 70% ammonium sulfate and column chromatography techniques. The biomolecule was enriched to 151.72-fold and the yield percentage was 0.05. Peoriaerin II, a highly potent and broad-spectrum antimicrobial peptide against Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Candida albicans ATCC 10231 was isolated. LC-MS sequencing revealed that its N-terminal is methionine. It has four negatively charged residues (Asp + Glu) and a total number of two positively charged residues (Arg + Lys). Its molecular weight is 4,685.13 Da. It is linked to an LC-MS/MS inferred biosynthetic gene cluster with accession number A0A2S6P0H9, and blastp has shown it is 82.4% similar to fusaricidin synthetase of Paenibacillus polymyxa SC2. The 3D structure conformation of the BGC and AMP were predicted using SWISS MODEL homology modeling. Therefore, combining both genomic and proteomic results obtained from P. peoriae IBSD35, associated with M. pachycarpa Benth., will substantially increase the understanding of antimicrobial peptides and assist to uncover novel biological agents.

FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics

Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The "real world" sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer . The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%). Graphical abstract The analysis workflow employing FractionOptimizer software.

Methodology and Applications of Disease Biomarker Identification in Human Serum

Biomarkers are biomolecules that serve as indicators of biological and pathological processes, or physiological and pharmacological responses to a drug treatment. Because of the high abundance of albumin and heterogeneity of plasma lipoproteins and glycoproteins, biomarkers are difficult to identify in human serum. Due to the clinical significance the identification of disease biomarkers in serum holds great promise for personalized medicine, especially for disease diagnosis and prognosis. This review summarizes some common and emerging proteomics techniques utilized in the separation of serum samples and identification of disease signatures. The practical application of each protein separation or identification technique is analyzed using specific examples. Biomarkers of cancers of prostate, breast, ovary, and lung in human serum have been reviewed, as well as those of heart disease, arthritis, asthma, and cystic fibrosis. Despite the advancement of technology few biomarkers have been approved by the Food and Drug Administration for disease diagnosis and prognosis due to the complexity of structure and function of protein biomarkers and lack of high sensitivity, specificity, and reproducibility for those putative biomarkers. The combination of different types of technologies and statistical analysis may provide more effective methods to identify and validate new disease biomarkers in blood.

A proteomic study of mesenchymal stem cells from equine umbilical cord

To the best of our knowledge, this is the first study describing the proteome of equine umbilical cord intervascular matrix mesenchymal stem cells (UCIM-MSCs) in a global and functional manner. The aim of this work was to analyze the proteome of previously characterized UCIM-MSCs to determine protein abundance and classify the identified proteins according to Gene Ontology (GO) terms. Protein classification analysis according to biological process, molecular function and cellular component was performed using the PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System, which revealed enrichment for 42 biological processes, 23 molecular functions and 18 cellular components. Protein abundance was estimated according to the emPAI method (Exponential Modified Protein Abundance Index). The two most abundant proteins in the proteome of UCIM-MSCs were the cytoskeletal proteins actin and vimentin, which have important roles in cell stability and motility. Additionally, we identified 14 cell surface antigens. Three of them, CD44, CD90 and CD105, had been previously validated by flow cytometry. In the present study, we also identified important information about the biological properties of UCIM-MSCs such as differentiation potential, low immunogenicity (low MHC-II expression) and chromosomal stability, which reinforces their use for cell therapy. Together with the proteomic findings, this information allowed us to infer the functional relevance of several activities related to primary metabolic processes, protein synthesis, production of vesicle coats, vesicle-mediated transport and antioxidant activity. In addition, the identification of different cell surface markers may help establish an immunophenotypic panel suitable for the characterization of MSCs from equine fetal membranes.

Hybrid Valve Structure for High-Throughput, Low-Volume Liquid-Handling Applications

A hybrid valve that integrates precision microfluidics for fluid routing, high-speed valving for fluid switching, and reagent-jetting devices for metering the fluid dispenses is described. The hybrid valve enables parallel switching between aspiration and dispense modes for multiple sample streams. This unique valve structure addresses many of the concerns with handling microscale volumes, including efficient use of samples, degradation of ink jet valves and speed of operation. A broad range of volumes can be manipulated with excellent reproducibility. The hybrid valve can be configured for a variety of applications. Pick-and-place aspiration and dispensing of unique reagents and rapid dispensing of a common reagent are possible. Together these features lead to higher-speed transfer of smaller volumes of reagent.

A Structures for Lossless Ion Manipulations (SLIM) Module for Collision Induced Dissociation

A collision induced dissociation (CID) structure for lossless ion manipulations (SLIM) module is introduced and coupled to a quadrupole time-of-flight (QTOF) mass spectrometer. The SLIM CID module was mounted after an ion mobility (IM) drift tube to enable IM/CID/MS studies. The efficiency of CID was studied by using the model peptide leucine enkephalin. CID efficiencies (62%) compared favorably with other beam-type CID methods. Additionally, the SLIM CID module was used to fragment a mixture of nine peptides after IM separation. This work also represents the first application of SLIM in the 0.3 to 0.5 Torr pressure regime, an order of magnitude lower in pressure than previously studied. Graphical Abstract ᅟ.

Delineation of Molecular Pathways Involved in Cardiomyopathies Caused by Troponin T Mutations

Familial hypertrophic cardiomyopathy (FHC) is associated with mild to severe cardiac problems and is the leading cause of sudden death in young people and athletes. Although the genetic basis for FHC is well-established, the molecular mechanisms that ultimately lead to cardiac dysfunction are not well understood. To obtain important insights into the molecular mechanism(s) involved in FHC, hearts from two FHC troponin T models (Ile79Asn [I79N] and Arg278Cys [R278C]) were investigated using label-free proteomics and metabolomics. Mutations in troponin T are the third most common cause of FHC, and the I79N mutation is associated with a high risk of sudden cardiac death. Most FHC-causing mutations, including I79N, increase the Ca(2+) sensitivity of the myofilament; however, the R278C mutation does not alter Ca(2+) sensitivity and is associated with a better prognosis than most FHC mutations. Out of more than 1200 identified proteins, 53 and 76 proteins were differentially expressed in I79N and R278C hearts, respectively, when compared with wild-type hearts. Interestingly, more than 400 proteins were differentially expressed when the I79N and R278C hearts were directly compared. The three major pathways affected in I79N hearts relative to R278C and wild-type hearts were the ubiquitin-proteasome system, antioxidant systems, and energy production pathways. Further investigation of the proteasome system using Western blotting and activity assays showed that proteasome dysfunction occurs in I79N hearts. Metabolomic results corroborate the proteomic data and suggest the glycolytic, citric acid, and electron transport chain pathways are important pathways that are altered in I79N hearts relative to R278C or wild-type hearts. Our findings suggest that impaired energy production and protein degradation dysfunction are important mechanisms in FHCs associated with poor prognosis and that cardiac hypertrophy is not likely needed for a switch from fatty acid to glucose metabolism.

Multiplexed Isobaric Tagging Protocols for Quantitative Mass Spectrometry Approaches to Auditory Research

Modern biologists have at their disposal a large array of techniques used to assess the existence and relative or absolute quantity of any molecule of interest in a sample. However, implementing most of these procedures can be a daunting task for the first time, even in a lab with experienced researchers. Just choosing a protocol to follow can take weeks while all of the nuances are examined and it is determined whether a protocol will (a) give the desired results, (b) result in interpretable and unbiased data, and (c) be amenable to the sample of interest. We detail here a robust procedure for labeling proteins in a complex lysate for the ultimate differential quantification of protein abundance following experimental manipulations. Following a successful outcome of the labeling procedure, the sample is submitted for mass spectrometric analysis, resulting in peptide quantification and protein identification. While we will concentrate on cells in culture, we will point out procedures that can be used for labeling lysates generated from tissues, along with any minor modifications required for such samples. We will also outline, but not fully document, other strategies used in our lab to label proteins prior to mass spectrometric analysis, and describe under which conditions each procedure may be desirable. What is not covered in this chapter is anything but the most brief introduction to mass spectrometry (instrumentation, theory, etc.), nor do we attempt to cover much in the way of software used for post hoc analysis. These two topics are dependent upon one's resources, and where applicable, one's collaborators. We strongly encourage the reader to seek out expert advice on topics not covered here.

Characterization of individual mouse cerebrospinal fluid proteomes

Analysis of cerebrospinal fluid (CSF) offers key insight into the status of the CNS. Characterization of murine CSF proteomes can provide a valuable resource for studying CNS injury and disease in animal models. However, the small volume of CSF in mice has thus far limited individual mouse proteome characterization. Through nonterminal CSF extractions in C57Bl/6 mice and high-resolution 2D-LC MS/MS analysis of individual murine samples, we report the most comprehensive proteome characterization of individual murine CSF to date. We identified a total of 566 unique proteins, including 128 proteins from three individual CSF samples that have been previously identified in brain tissue. Our methods and analysis provide a mechanism for individual murine CSF proteome analysis. The data are available in the ProteomeXchange with identifier PXD000248 (http://proteomecentral.proteomexchange.org/dataset/PXD000248).

Four conventional soybean [Glycine max (L.) Merrill] seeds exhibit different protein profiles as revealed by proteomic analysis

Soybeans have several functional properties due to their composition and may exert beneficial health effects that are attributed to proteins and their derivative peptides. The present study aimed to analyze the protein profiles of four new conventional soybean seeds (BRS 257, BRS 258, BRS 267, and Embrapa 48) with the use of proteomic tools. Two-dimensional (2D) and one-dimensional (1D) gel electrophoreses were performed, followed by MALDI-TOF/TOF and ESI-Q-TOF mass spectrometry analyses, respectively. These two different experimental approaches allowed the identification of 117 proteins from 1D gels and 46 differentially expressed protein spots in 2D gels. BRS 267 showed the greatest diversity of identified spots in the 2D gel analyses. In the 1D gels, the major groups were storage (25-40%) and lipid metabolism (11-25%) proteins. The differences in protein composition between cultivars could indicate functional and nutritional differences and could direct the development of new cultivars.

Chip-based nanoelectrospray mass spectrometry for protein characterization

In the last several years, significant progress has been made in the development of microfluidic-based analytical technologies for proteomic and drug discovery applications. Chip-based nanoelectrospray coupled to a mass spectrometer detector is one of the recently developed analytical microscale technologies. This technology offers unique advantages for automated nanoelectrospray including reduced sample consumption, improved detection sensitivity and enhanced data quality for proteomic studies. This review presents an overview and introduction of recent developments in chip devices coupled to electrospray mass spectrometers including the development of the automated nanoelectrospray ionization chip device for protein characterization. Applications using automated chip-based nanoelectrospray ionization technology in proteomic and bioanalytical studies are also extensively reviewed in the fields of high-throughput protein identification, protein post-translational modification studies, top-down proteomics, biomarker screening by pattern recognition, noncovalent protein-ligand binding for drug discovery and lipid analysis. Additionally, future trends in chip-based nanoelectrospray technology are discussed.

Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.

STEPS: a grid search methodology for optimized peptide identification filtering of MS/MS database search results

For bottom-up proteomics, there are wide variety of database-searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid-search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection--referred to as STEPS--utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true-positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.

Finding biomarkers is getting easier

Single biomarkers are rarely accurate. Even suites of biomarkers can give conflicting results. Ideally potent combinations of variables are isolated which accurately identify specific analytes and their level of toxicity. The search for such combinations can be done by reducing the thousands of candidate variables to the small number necessary for treatment classification. When the key variables are recognized by machine learning (ML) the results are quite surprising, given the apparent failure of other searching methods to produce good diagnostics. Proteins seem especially useful for portable field tests of a variety of adverse conditions. This review shows how ML, in particular artificial neural networks, can find potent biomarkers embedded in any type of expression data, mainly proteins in this article. A computer does multiple iterations to produce sets of proteins which systematically identify (to near 100% accuracy) the treatment classes of interest. Whether these proteins are useful in actual diagnoses is tested by presenting the computer model with unknown classes. Finding the biomarkers is getting easier but there still must be confirmation, by multivariable statistics and with field studies.

Circulating biomarkers of protein oxidation for Alzheimer disease: expectations within limits

Alzheimer disease (AD), the most common dementing disorder, is a multifactorial disease with complex etiology. Among different hypotheses proposed for AD one of the most corroborated is the "oxidative stress hypothesis". Although recent studies extensively demonstrated the specific oxidative modification of selected proteins in the brain of AD patients and how their dysfunction possibly correlates with the pathology, there is still an urgent need to extend these findings to peripheral tissue. So far very few studies showed oxidative damage of proteins in peripheral tissues and current findings need to be replicated. Another limit in AD research is represented by the lack of highly specific diagnostic tools for early diagnosis. For a full screening and early diagnosis, biomarkers easily detectable in biological samples, such as blood, are needed. The search of reliable biomarkers for AD in peripheral blood is a great challenge. A few studies described a set of plasma markers that differentiated AD from controls and were shown to be useful in predicting conversion from mild cognitive impairment, which is considered a prodromal stage, to AD. We review the current state of knowledge on peripheral oxidative biomarkers for AD, including proteomics, which might be useful for early diagnosis and prognosis.

Differential proteomic analysis of Rickettsia prowazekii propagated in diverse host backgrounds

The obligate intracellular growth of Rickettsia prowazekii places severe restrictions on the analysis of rickettsial gene expression. With a small genome, predicted to code for 835 proteins, identifying which proteins are differentially expressed in rickettsiae that are isolated from different hosts or that vary in virulence is critical to an understanding of rickettsial pathogenicity. We employed a liquid chromatography (LC)-linear trap quadrupole (LTQ)-Orbitrap mass spectrometer for simultaneous acquisition of quantitative mass spectrometry (MS)-only data and tandem mass spectrometry (MS-MS) sequence data. With the use of a combination of commercially available algorithms and in-house software, quantitative MS-only data and comprehensive peptide coverage generated from MS-MS were integrated, resulting in the assignment of peptide identities with intensity values, allowing for the differential comparison of complex protein samples. With the use of these protocols, it was possible to directly compare protein abundance and analyze changes in the total proteome profile of R. prowazekii grown in different host backgrounds. Total protein extracted from rickettsiae grown in murine, tick, and insect cell lines or hen egg yolk sacs was analyzed. Here, we report the fold changes, including an upregulation of shock-related proteins, in rickettsiae cultivated in tissue culture compared to the level for rickettsiae harvested from hen yolk sacs. The ability to directly compare, in a complex sample, differential rickettsial protein expression provides a snapshot of host-specific proteomic profiles that will help to identify proteins important in intracellular growth and virulence.

Protein-protein interaction networks and subnetworks in the biology of disease

The main goal of systems medicine is to provide predictive models of the patho-physiology of complex diseases as well as define healthy states. The reason is clear--we hope accurate models will ultimately lead to more specific and sensitive markers of disease that will help clinicians better stratify their patient populations and optimize treatment plans. In addition, we expect that these models will define novel targets for combating disease. However, for many complex diseases, particularly at the clinical level, it is becoming increasingly clear that one or a few genomic variations alone (e.g., simple models) cannot adequately explain the multiple phenotypes related to disease states, or the variable risks that attend disease progression. We suggest that models that account for the activities of many interacting proteins will explain a wider range of variability inherent in these phenotypes. These models, which encompass protein interaction networks dysregulated for specific diseases and specific patient sub-populations, will be constructed by integrating protein interaction data with multiple types of other relevant cellular information. Protein interaction databases are thus playing an increasingly important role in systems biology approaches to the study of disease. They present us with a static, but highly functional view of the cellular state, and thus give us a better understanding of not only the normal phenotype, but also the overall disease phenotype at the level of the whole organism when certain interactions become dysregulated.

Massively parallel concentration device for multiplexed immunoassays

A massively parallel nanofluidic concentration device array for multiplexed and high-throughput biomolecule detection is demonstrated. By optimizing the microchannel/nanojunction design and channel conductivity, an array of up to 128 nanofluidic concentration devices were fabricated. Operation of the entire array requires only one inlet and one outlet reservoir, with the application of a single operational voltage bias across them. Concentration efficiencies of the devices were found to be uniform within the array, within 5% error. Alternatively, concentration speed in each channel can be individually tuned by controlling the length of the inlet microchannel and thus controlling the flow rate based on change of the tangential electric field. This allows immuno-binding reactions at different concentration ranges to be performed in parallel. Using multiplexed, successive-concentration enhanced detection in the device, we have shown that the dynamic range and reliability of the immunoassay can be significantly increased.

Labeling elastase digests with TMT: informational gain by identification of poorly detectable peptides with MALDI-TOF/TOF mass spectrometry

The applicability of the less specific protease elastase for the identification of membrane and cytosolic proteins has already been demonstrated. MALDI as ionization technique particularly favors the detection of basic and to a lesser extent of weakly acidic peptides, whereas neutral peptides often remain undetected. Moreover, peptides below 700  Da are routinely excluded. In the following study, the advantage of additional information gained from tandem mass tag zero labeled peptides and the resultant increase in sequence coverage was evaluated. Through derivatization with tandem mass tag reagents, peptide measurement within the standard mass range of the MALDI reflector mode is achievable due to the mass increase. Compared to the unlabeled sample, peptides exhibiting relatively low molecular masses, pI values or higher hydrophobicity could be identified.

Protein identification using nano-HPLC-MS: ESI-MS and MALDI-MS interfaces

Body fluids and body tissues have a myriad of peptides and proteins that, very often, the traditional methodologies of proteomics, such as conventional gel electrophoresis or mass spectrometry, are unable to characterize. We describe two protocols to characterize high molecular weight peptides (>3 kDa) and intact proteins involving on-line trypsin digestion, separation of the digests by nano-HPLC, and analysis by mass spectrometry using two different ionization sources (matrix-assisted laser desorption and electrospray ionization). These protocols have the advantage of promoting protein denaturation in an aqueous-organic solvent, which reduces the derivatization of the sample and facilitates an in-depth analysis for detection and identification of proteins. Additional advantages include the following: (1) integration of these protocols into standard proteomic workflows after the preprocessing of samples and separation; (2) use of high-resolution monolithic columns; and (3) the ability to acquire information from minimal amounts of sample.

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.

An empirical strategy for characterizing bacterial proteomes across species in the absence of genomic sequences

Global protein identification through current proteomics methods typically depends on the availability of sequenced genomes. In spite of increasingly high throughput sequencing technologies, this information is not available for every microorganism and rarely available for entire microbial communities. Nevertheless, the protein-level homology that exists between related bacteria makes it possible to extract biological information from the proteome of an organism or microbial community by using the genomic sequences of a near neighbor organism. Here, we demonstrate a trans-organism search strategy for determining the extent to which near-neighbor genome sequences can be applied to identify proteins in unsequenced environmental isolates. In proof of concept testing, we found that within a CLUSTAL W distance of 0.089, near-neighbor genomes successfully identified a high percentage of proteins within an organism. Application of this strategy to characterize environmental bacterial isolates lacking sequenced genomes, but having 16S rDNA sequence similarity to Shewanella resulted in the identification of 300-500 proteins in each strain. The majority of identified pathways mapped to core processes, as well as to processes unique to the Shewanellae, in particular to the presence of c-type cytochromes. Examples of core functional categories include energy metabolism, protein and nucleotide synthesis and cofactor biosynthesis, allowing classification of bacteria by observation of conserved processes. Additionally, within these core functionalities, we observed proteins involved in the alternative lactate utilization pathway, recently described in Shewanella.

Alteration in protein expression in estrogen receptor alpha-negative human breast cancer tissues indicates a malignant and metastatic phenotype

Ductal carcinoma in situ (DCIS) represents the earliest identifiable breast cancer lesion. Disruption of the myoepithelial cell layer and basement membrane is a prerequisite for DCIS to initiate invasion into the stroma. The majority of epithelial cells overlying a focally-disrupted myoepithelial cell layer are estrogen receptor-alpha negative (ER(-)); however, adjacent cells within the same duct confined by an intact myoepithelial cell layer express high levels of ER. These ER (+) and ER (-) cells were microdissected from the same ducts of breast cancer patients. Differential proteins expressed by ER(+) and ER(-) cells were identified using two-dimensional gel electrophoresis followed by mass spectrometry and Western blot analysis. ER(-) cells express lower levels of superoxide dismutase, RalA binding protein, galectin-1, uridine phosphorylase 2, cellular retinoic acid-binding protein 1, S100 calcium binding protein A11, and nucleoside diphosphate kinase A or non-metastasis protein 23-H1 (nm23-H1). The upregulated protein, Rho GDP-dissociation inhibitor 1 alpha, may induce chemotherapy resistance. The significant findings are that the microdissected ER(-) cells express 12.6 times less cellular retinoic acid-binding protein 1, a protein involved in cellular differentiation, and 4.1 times less nucleoside diphosphate kinase A or nm23-H1, a metastasis suppressor, and express fewer proteins than adjacent ER(+) cells. The collective role of the alterations of protein expression in ER(-) cells may be to promote a more malignant phenotype than adjacent ER(+) cells, including a decreased ability to undergo apoptosis and differentiation, and an increased potential to damage DNA, metastasize, and resist to chemotherapy.

Proteomics of skeletal muscle differentiation, neuromuscular disorders and fiber aging

Skeletal muscle fibers are the most abundant cellular structure in the human body. Altered neuromuscular activity, traumatic injury or genetic abnormalities have profound effects on muscle integrity, tissue mass, fiber type distribution, metabolic integration and contractile function. The recent application of mass spectrometry-based proteomics has decisively advanced our molecular understanding of numerous physiological adaptations in healthy muscle and pathophysiological mechanisms associated with major muscle diseases. Skeletal muscle proteomics promises to play a major role in the establishment of a disease-specific biomarker signature for the major classes of neuromuscular disorders. New muscle markers will be crucial for the development of improved diagnostics, the monitoring of disease progression, evaluation of drug action and the identification of novel therapeutic targets.

Proteomic analysis of colorectal cancer: prefractionation strategies using two-dimensional free-flow electrophoresis

This review deals with the application of a new prefractionation tool, free-flow electrophoresis (FFE), for proteomic analysis of colorectal cancer (CRC). CRC is a leading cause of cancer death in the Western world. Early detection is the single most important factor influencing outcome of CRC patients. If identified while the disease is still localized, CRC is treatable. To improve outcomes for CRC patients there is a pressing need to identify biomarkers for early detection (diagnostic markers), prognosis (prognostic indicators), tumour responses (predictive markers) and disease recurrence (monitoring markers). Despite recent advances in the use of genomic analysis for risk assessment, in the area of biomarker identification genomic methods alone have yet to produce reliable candidate markers for CRC. For this reason, attention is being directed towards proteomics as a complementary analytical tool for biomarker identification. Here we describe a proteomics separation tool, which uses a combination of continuous FFE, a liquid-based isoelectric focusing technique, in the first dimension, followed by rapid reversed-phase HPLC (1–6 min/analysis) in the second dimension. We have optimized imaging software to present the FFE/RP-HPLC data in a virtual 2D gel-like format. The advantage of this liquid based fractionation system over traditional gel-based fractionation systems is the ability to fractionate large quantity protein samples. Unlike 2D gels, the method is applicable to both high-M_r proteins and small peptides, which are difficult to separate, and in the case of peptides, are not retained in standard 2D gels.

Algorithmic and analytical methods in network biology

During the genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining to functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of biological systems, commonly using network models. Such efforts lead to novel insights into the complexity of living systems, through development of sophisticated abstractions, algorithms, and analytical techniques that address a broad range of problems, including the following: (1) inference and reconstruction of complex cellular networks; (2) identification of common and coherent patterns in cellular networks, with a view to understanding the organizing principles and building blocks of cellular signaling, regulation, and metabolism; and (3) characterization of cellular mechanisms that underlie the differences between living systems, in terms of evolutionary diversity, development and differentiation, and complex phenotypes, including human disease. These problems pose significant algorithmic and analytical challenges because of the inherent complexity of the systems being studied; limitations of data in terms of availability, scope, and scale; intractability of resulting computational problems; and limitations of reference models for reliable statistical inference. This article provides a broad overview of existing algorithmic and analytical approaches to these problems, highlights key biological insights provided by these approaches, and outlines emerging opportunities and challenges in computational systems biology.

Proteomic analysis of shear stress-mediated protection from TNF-alpha in endothelial cells

Previous studies have shown that physiological levels of shear stress can protect endothelial cells (ECs) from apoptotic stimuli. Here, we differentiate between acute and chronic protection and demonstrate the use of proteomic technologies to uncover mechanisms associated with chronic protection of ECs. We hypothesized that changes in abundance of proteins associated with the TNF-alpha signaling cascade orchestrate shear stress-mediated protection from TNF-alpha when cells are preconditioned with shear prior to the exposure of apoptotic stimuli. Detection of cleaved caspase 3 through Western blot analysis confirmed chronic shear stress-mediated protection from TNF-alpha. In the presence of the nitric oxide synthase inhibitor, LNMA (N(omega)-monomethyl-l-arginine), chronic protection remained. Treatment with a de novo protein synthesis inhibitor, cycloheximide, eliminated this protective effect. Isotopic-labeling experiments, coupled with LC-MS/MS (liquid chromatography-tandem mass spectrometry) of isolated components of the TNF-alpha pathway revealed that CARD9, a known activator of the NF-kappaB pathway, was increased (60%) in sheared cells versus nonsheared cells. This result was confirmed through Western blot analysis. Our data suggest that de novo formation of proteins is required for protection from TNF-alpha in ECs chronically exposed to shear stress, and that CARD9 is a candidate protein in this response.

Proteomics: challenges, techniques and possibilities to overcome biological sample complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed.

Protein expression profiling

Protein expression profiling is defined in general as identifying the proteins expressed in a particular tissue, under a specified set of conditions and at a particular time, usually compared to expression in reference samples. This information is useful in drug discovery and diagnosis as well as in understanding response mechanisms at the protein level. We may identify all the proteins responding to a particular stimulus and select those whose expression changes most. Or we may isolate significant protein variables and then identify them. These definitive sets of proteins (protein expression signatures; PES) are specific to diseases, toxicants, physical stresses, and to degrees of stress severity. Here we describe a method, based on machine learning, for isolating the sets of proteins, before identifying them by name, which classify accurately the treatment classes in a study. The principle in this chapter is that if proteins associated with known classes of interest can be used to identify unknown classes then the proteins are definitive for diagnosis.The proteins in each class, including controls, are converted to digital data and serve as input to artificial neural network (ANN) models. Multiple two-dimensional electrophoresis (2DE) gel patterns are included in each treatment class. A training subset of digitized individual, not composite, gel images is used to construct an ANN model which is then applied to a test set of images. Successful classification of the unknown (test) data confirms that the variables included in the model are indeed significant in discrimination among the classes. In the study described here the misclassifications were 5% or less using the ANN models. The ANN method seems to be a useful complement to image analysis, described in Chapter "Troubleshooting Image Analysis in 2DE". The reduction in protein variables permits multivariable statistics such as cluster and discriminant analyses.

Multiplexed isobaric tagging protocols for quantitative mass spectrometry approaches to auditory research

Modern biologists have at their disposal a large array of techniques used to assess the existence and relative or absolute quantity of any molecule of interest in a sample. However, implementing most of these procedures can be a daunting task for the first time, even in a lab with experienced researchers. Just choosing a protocol to follow can take weeks while all of the nuances are examined and it is determined whether a protocol will (a) give the desired results, (b) result in interpretable and unbiased data, and (c) be amenable to the sample of interest. We detail here a robust procedure for labeling proteins in a complex lysate for the ultimate differential quantification of protein abundance following experimental manipulations. Following a successful outcome of the labeling procedure, the sample is submitted for mass spectrometric analysis, resulting in peptide quantification and protein identification. While we will concentrate on cells in culture, we will point out procedures that can be used for labeling lysates generated from other tissues, along with any minor modifications required for such samples. We will also outline, but not fully document, other strategies used in our lab to label proteins prior to mass spectrometric analysis, and describe under which conditions each procedure may be desirable. What is not covered in this chapter is anything but the most brief introduction to mass spectrometry (instrumentation, theory, etc.), nor do we attempt to cover much in the way of software used for post hoc analysis. These two topics are dependant upon one's resources, and where applicable, one's collaborators. We strongly encourage the reader to seek out expert advice on topics not covered here.

Genetic approaches towards overcoming water deficit in plants - special emphasis on LEAs

Water deficit arises as a result of low temperature, salinity and dehydration, thereby affecting plant growth adversely and making it imperative for plants to surmount such situations by acclimatizing/adapting at various levels. Water deficit stress results in significant changes in gene expression, mediated by interconnected signal transduction pathways that may be triggered by calcium, and regulated via ABA dependent and/or independent pathways. Hence, adaptation of plants to such stresses involves maintaining cellular homeostasis, detoxification of harmful elements and also growth alterations. Stress in general cause excess production of reactive oxygen species (ROS) and the plants overcome the same by either preventing the accumulation of ROS or by eliminating the ROS formed. Ion homeostasis includes processes such as cellular uptake, sequestration and export in conjunction with long distance transport. Requisite amounts of osmolytes are hence synthesized under stress to maintain turgor along with maintaining the macromolecular structures and also for scavenging ROS. Another noteworthy response is the accumulation of novel proteins, including enzymes involved in the biosynthesis of osmoprotectants, heat-shock proteins (HSPs), late embryogenesis abundant (LEA) proteins, antifreeze proteins, chaperones, detoxification enzymes, transcription factors, kinases and phosphatases. The LEAs belong to a redundant protein family and are highly hydrophilic, boiling-soluble, non-globular and therefore have been defined and classified accordingly. The precise function of LEAs is still unknown, but substantial evidence indicates their involvement in dessication tolerance as the expression of LEAs confers increased resistance to stress in heterologous yeast system and also significantly improves water deficit tolerance in transgenic plants. Genetic manipulation of plants towards conferring abiotic stress tolerance is a daunting task, as the abiotic stress tolerance mechanism is highly complex and various strategies have been exploited to address and evaluate the stress tolerance mechanism, and the molecular responses to water deficit via complex signaling networks. Genomic technologies have recently been useful in integrating the multigenicity of the plant stress responses through, transcriptomics, proteomics and metabolite profilling and their interactions. This review deals with the recent developments on genetic approaches for water stress tolerance in plants, with special emphasis on LEAs.

Digestion of native proteins for proteomics using a thermocycler

Efficient protein digestion is a critical step for successful mass spectrometry analysis. Here we describe simultaneous tryptic digestion and gradual unfolding of native proteins by application of a temperature gradient using a single cycle of 5 min or less in a PCR thermocycler. Chemicals typically used for chromatographic techniques did not affect the digestion efficiency. Tryptic digestion was performed in a small volume (3 microL) with 1.5 microg of trypsin without denaturing agents. This rapid procedure yielded more peptides than conventional methods utilizing chemical denaturation for 18 proteins out of 20. Samples were directly spotted on the MALDI-TOF target plate, without additional purification, thus reducing losses on reversed-phase resins.

On-plate digestion using a commercial microfraction collector for nano-HPLC matrix-assisted laser desorption/ionization tandem time-of-flight protein analysis

A new method for on-plate protein digestion and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry analysis is proposed involving an automated one-step sample separation using nanoflow HPLC followed by nanoliter fraction collection and on-plate digestion with trypsin. This procedure uses a commercial automatic nanoliter fraction collection system for on-line spotting of the eluent onto a MALDI target. After protein digestion, the reaction is stopped by the addition of acidified matrix using the same automated system. Collected spots are subsequently analyzed using a MALDI tandem time-of-flight (TOF/TOF) mass spectrometer for protein sequencing and identification.

The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.

Mass spectrometry-based functional proteomics: from molecular machines to protein networks

The study of protein-protein interactions by mass spectrometry is an increasingly important part of post-genomics strategies to understand protein function. A variety of mass spectrometry-based approaches allow characterization of cellular protein assemblies under near-physiological conditions and subsequent assignment of individual proteins to specific molecular machines, pathways and networks, according to an increasing level of organizational complexity. An appropriate analytical strategy can be individually tailored--from an in-depth analysis of single complexes to a large-scale characterization of entire molecular pathways or even an analysis of the molecular organization of entire expressed proteomes. Here we review different options regarding protein-complex purification strategies, mass spectrometry analysis and bioinformatic methods according to the specific question that is being addressed.

Performance Characteristics of Electron Transfer Dissociation Mass Spectrometry

We performed a large scale study of electron transfer dissociation (ETD) performance, as compared with ion trap collision-activated dissociation (CAD), for peptides ranging from approximately 1000 to 5000 Da (n approximately 4000). These data indicate relatively little overlap in peptide identifications between the two methods ( approximately 12%). ETD outperformed CAD for all charge states greater than 2; however, regardless of precursor charge a linear decrease in percent fragmentation, as a function of increasing precursor m/z, was observed with ETD fragmentation. We postulate that several precursor cation attributes, including peptide length, charge distribution, and total mass, could be relevant players. To examine these parameters unique ETD-identified peptides were sorted by length, and the ratio of amino acid residues per precursor charge (residues/charge) was calculated. We observed excellent correlation between the ratio of residues/charge and percent fragmentation. For peptides of a given residue/charge ratio, there is no correlation between peptide mass and percent fragmentation; instead we conclude that the ratio of residues/charge is the main factor in determining a successful ETD outcome. As charge density decreases so does the probability of non-covalent interactions that can bind a newly formed c/z-type ion pair. Recently we have described a supplemental activation approach (ETcaD) to convert these non-dissociative electron transfer product ions to useful c- and z-type ions. Automated implementation of such methods should remove this apparent precursor m/z ceiling. Finally, we evaluated the role of ion density (both anionic and cationic) and reaction duration for an ETD experiment. These data indicate that the best performance is achieved when the ion trap is filled to its space charge limit with anionic reagents. In this largest scale study of ETD to date, ETD continues to show great promise to propel the field of proteomics and, for small- to medium-sized peptides, is highly complementary to ion trap CAD.

Proteomics-based dissection of stress-responsive pathways in plants

Abiotic stress has an ability to alter the levels of a number of proteins, which may be soluble or structural in nature or which may exist before and after folding in the plant cell. The most crucial function of plant cell is to respond to stress by developing defence mechanisms. This defence is brought about by alteration in the pattern of gene expression. This leads to modulation of certain metabolic and defensive pathways. Owing to gene expression altered under stress, qualitative and quantitative changes in proteins are obvious. These proteins might play a role in signal transduction, antioxidative defence, antifreezing, heat shock, metal binding, antipathogenesis or osmolyte synthesis. A significant part of the literature shows the quantitative and qualitative changes in proteins, mainly employing western analysis, enzymatic kinetics, fraction isolation, one-dimensional SDS-PAGE electrophoresis, etc. Fortunately, recent developments in sensitivity and accuracy for proteome analysis have provided new dimensions to assess the changes in protein types and their expression levels under stress. The novel aim of this review is to do a side-by-side comparison of the proteins that are induced or overexpressed under abiotic stress, examining those from biochemical literature and the ones observed, sequenced and identified using the advanced proteomics and bioinformatic techniques.

Capillary electrophoretic separation and fractionation of hydrophobic peptides onto a pre-structured matrix assisted laser desorption/ ionization target for mass spectrometric analysis

A CE separation of hydrophobic peptides followed by fractionation onto a prestructured MALDI target and off-line MS analysis was performed. An improved and partially automated manufacturing procedure of the previously described MALDI target is presented. This target is structurally coated with silicone and especially developed for hydrophobic peptides and proteins. Here, the target plate was designed specifically for the CE fraction collection. Different solvents were evaluated to meet the requirements of peptide solubility and compatibility to both the CE and MALDI methods and to the fractionation procedure. CE-MALDI-MS analysis of nine highly hydrophobic peptides from cyanogen bromide-digested bacteriorhodopsin is demonstrated.