Advances in proteomics data analysis and display using an accurate mass and time tag approach

Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation

Background

Biomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance) among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF) and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) for CSF biomarker discovery by (1) identifying reparable sources of technical variability, (2) assessing subject variance and residual technical variability for numerous CSF proteins, and (3) testing its ability to segregate samples on the basis of desired biomarker characteristics.

Methods/Results

Fourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i) incomplete MAF and keratins; ii) globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii) oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5%) whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and individual samples.

Conclusions

Quantitative label-free LC-MS/MS can measure scores of CSF proteins with low technical variability and can segregate samples according to desired criteria. Thus, this technique shows potential for biomarker discovery for neurological diseases.

Disparate proteome responses of pathogenic and nonpathogenic aspergilli to human serum measured by activity-based protein profiling (ABPP)

Aspergillus fumigatus is the primary pathogen causing the devastating pulmonary disease Invasive Aspergillosis in immunocompromised individuals. There is high genomic synteny between A. fumigatus and closely related rarely pathogenic Neosartorya fischeri and Aspergillus clavatus genomes. We applied activity-based protein profiling to compare unique or overexpressed activity-based probe-reactive proteins of all three fungi over time in minimal media growth and in response to human serum. We found 360 probe-reactive proteins exclusive to A. fumigatus, including known virulence associated proteins, and 13 proteins associated with stress response exclusive to A. fumigatus culture in serum. Though the fungi are highly orthologous, A. fumigatus has a significantly greater number of ABP-reactive proteins across varied biological process. Only 50% of expected orthologs of measured A. fumigatus reactive proteins were observed in N. fischeri and A. clavatus. Activity-based protein profiling identified a number of processes that were induced by human serum in A. fumigatus relative to N. fischeri and A. clavatus. These included actin organization and assembly, transport, and fatty acid, cell membrane, and cell wall synthesis. Additionally, signaling proteins regulating vegetative growth, conidiation, and cell wall integrity, required for appropriate cellular response to external stimuli, had higher activity-based probe-protein reaction over time in A. fumigatus and N. fisheri, but not in A. clavatus. Together, we show that measured proteins and physiological processes identified solely or significantly over-represented in A. fumigatus reveal a unique adaptive response to human protein not found in closely related, but rarely pathogenic aspergilli. These unique activity-based probe-protein responses to culture condition may reveal how A. fumigatus initiates pulmonary invasion leading to Invasive Aspergillosis.

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.

Current Omics Technologies in Biomarker Discovery

Biomarkers are playing an increasingly important role in drug discovery and development and can be applied for many purposes, including disease mechanism study, diagnosis, prognosis, staging, and treatment selection. Advances in high-throughput “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, significantly accelerate the pace of biomarker discovery. Comprehensive molecular profiling using these “omics” technology has become a field of intensive research aiming at identifying biomarkers relevant for improved diagnostics and therapeutics. Although each “omics” technology plays important roles in biomarker research, different “omics” platforms have different strengths and limitations. This chapter aims to give an overview of these “omics” technologies and their current application in the biomarker discovery.

MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis

Background

MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and chromatographic elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. For example, clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs.

Results

MultiAlign was applied to two large proteomics datasets obtained from liquid chromatography-mass spectrometry analyses of environmental samples. Peptides in the datasets for a microbial community that had a known metagenome were identified by matching mass and elution time features to those in an established reference peptide database. Results compared favorably with those obtained using existing tools such as VIPER, but with the added benefit of being able to trace clusters of peptides across conditions to existing tandem mass spectra. MultiAlign was further applied to detect clusters across experimental samples derived from a reactor biomass community for which no metagenome was available. Several clusters were culled for further analysis to explore changes in the community structure. Lastly, MultiAlign was applied to liquid chromatography-mass spectrometry-based datasets obtained from a previously published study of wild type and mitochondrial fatty acid oxidation enzyme knockdown mutants of human hepatocarcinoma to demonstrate its utility for analyzing metabolomics datasets.

Conclusion

MultiAlign is an efficient software package for finding similar analytes across multiple liquid chromatography-mass spectrometry feature maps, as demonstrated here for both proteomics and metabolomics experiments. The software is particularly useful for proteomic studies where little or no genomic context is known, such as with environmental proteomics.

Release of severe acute respiratory syndrome coronavirus nuclear import block enhances host transcription in human lung cells

The severe acute respiratory syndrome coronavirus accessory protein ORF6 antagonizes interferon signaling by blocking karyopherin-mediated nuclear import processes. Viral nuclear import antagonists, expressed by several highly pathogenic RNA viruses, likely mediate pleiotropic effects on host gene expression, presumably interfering with transcription factors, cytokines, hormones, and/or signaling cascades that occur in response to infection. By bioinformatic and systems biology approaches, we evaluated the impact of nuclear import antagonism on host expression networks by using human lung epithelial cells infected with either wild-type virus or a mutant that does not express ORF6 protein. Microarray analysis revealed significant changes in differential gene expression, with approximately twice as many upregulated genes in the mutant virus samples by 48 h postinfection, despite identical viral titers. Our data demonstrated that ORF6 protein expression attenuates the activity of numerous karyopherin-dependent host transcription factors (VDR, CREB1, SMAD4, p53, EpasI, and Oct3/4) that are critical for establishing antiviral responses and regulating key host responses during virus infection. Results were confirmed by proteomic and chromatin immunoprecipitation assay analyses and in parallel microarray studies using infected primary human airway epithelial cell cultures. The data strongly support the hypothesis that viral antagonists of nuclear import actively manipulate host responses in specific hierarchical patterns, contributing to the viral pathogenic potential in vivo. Importantly, these studies and modeling approaches not only provide templates for evaluating virus antagonism of nuclear import processes but also can reveal candidate cellular genes and pathways that may significantly influence disease outcomes following severe acute respiratory syndrome coronavirus infection in vivo.

Serum proteomics reveals systemic dysregulation of innate immunity in type 1 diabetes

Proteomics analysis identifies human serum proteins involved with innate immune responses, complement activation, and blood coagulation that are diagnostic for type 1 diabetes.

Using global liquid chromatography-mass spectrometry (LC-MS)–based proteomics analyses, we identified 24 serum proteins that were significantly variant between those with type 1 diabetes (T1D) and healthy controls. Functionally, these proteins represent innate immune responses, the activation cascade of complement, inflammatory responses, and blood coagulation. Targeted verification analyses were performed on 52 surrogate peptides representing these proteins, with serum samples from an antibody standardization program cohort of 100 healthy control and 50 type 1 diabetic subjects. 16 peptides were verified as having very good discriminating power, with areas under the receiver operating characteristic curve ≥0.8. Further validation with blinded serum samples from an independent cohort (10 healthy control and 10 type 1 diabetics) demonstrated that peptides from platelet basic protein and C1 inhibitor achieved both 100% sensitivity and 100% specificity for classification of samples. The disease specificity of these proteins was assessed using sera from 50 age-matched type 2 diabetic individuals, and a subset of proteins, C1 inhibitor in particular, were exceptionally good discriminators between these two forms of diabetes. The panel of biomarkers distinguishing those with T1D from healthy controls and those with type 2 diabetes suggests that dysregulated innate immune responses may be associated with the development of this disorder.

A multi-omic systems approach to elucidating Yersinia virulence mechanisms

The underlying mechanisms that lead to dramatic differences between closely related pathogens are not always readily apparent. For example, the genomes of Yersinia pestis (YP) the causative agent of plague with a high mortality rate and Yersinia pseudotuberculosis (YPT) an enteric pathogen with a modest mortality rate are highly similar with some species specific differences; however the molecular causes of their distinct clinical outcomes remain poorly understood. In this study, a temporal multi-omic analysis of YP and YPT at physiologically relevant temperatures was performed to gain insights into how an acute and highly lethal bacterial pathogen, YP, differs from its less virulent progenitor, YPT. This analysis revealed higher gene and protein expression levels of conserved major virulence factors in YP relative to YPT, including the Yop virulon and the pH6 antigen. This suggests that adaptation in the regulatory architecture, in addition to the presence of unique genetic material, may contribute to the increased pathogenecity of YP relative to YPT. Additionally, global transcriptome and proteome responses of YP and YPT revealed conserved post-transcriptional control of metabolism and the translational machinery including the modulation of glutamate levels in Yersiniae. Finally, the omics data was coupled with a computational network analysis, allowing an efficient prediction of novel Yersinia virulence factors based on gene and protein expression patterns.

Detection and identification of heme c-modified peptides by histidine affinity chromatography, high-performance liquid chromatography-mass spectrometry, and database searching

Multiheme c-type cytochromes (proteins with covalently attached heme c moieties) play important roles in extracellular metal respiration in dissimilatory metal-reducing bacteria. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) characterization of c-type cytochromes is hindered by the presence of multiple heme groups, since the heme c modified peptides are typically not observed or, if observed, not identified. Using a recently reported histidine affinity chromatography (HAC) procedure, we enriched heme c tryptic peptides from purified bovine heart cytochrome c, two bacterial decaheme cytochromes, and subjected these samples to LC-MS/MS analysis. Enriched bovine cytochrome c samples yielded 3- to 6-fold more confident peptide-spectrum matches to heme c containing peptides than unenriched digests. In unenriched digests of the decaheme cytochrome MtoA from Sideroxydans lithotrophicus ES-1, heme c peptides for 4 of the 10 expected sites were observed by LC-MS/MS; following HAC fractionation, peptides covering 9 out of 10 sites were obtained. Heme c peptide spiked into E. coli lysates at mass ratios as low as 1×10(-4) was detected with good signal-to-noise after HAC and LC-MS/MS analysis. In addition to HAC, we have developed a proteomics database search strategy that takes into account the unique physicochemical properties of heme c peptides. The results suggest that accounting for the double thioether link between heme c and peptide, and the use of the labile heme fragment as a reporter ion, can improve database searching results. The combination of affinity chromatography and heme-specific informatics yielded increases in the number of peptide-spectrum matches of 20-100-fold for bovine cytochrome c.

Identification of soybean proteins from a single cell type: the root hair

Root hairs (RH) are a terminally differentiated single cell type, mainly involved in water and nutrient uptake from the soil. The soybean RH cell represents an excellent model for the study of single cell systems biology. In this study, we identified 5702 proteins, with at least two peptides, from soybean RH using an accurate mass and time tag approach, establishing a comprehensive proteome reference map of this single cell type. We also showed that trypsin is the most appropriate enzyme for soybean proteomic studies by performing an in silico digestion of the soybean proteome using different proteases. Although the majority of proteins identified in this study are involved in basal metabolism, the function of others are more related to RH formation/function and include proteins involved in nutrient uptake (transporters) or vesicular trafficking (cytoskeleton and ras-associated binding proteins). Interestingly, some of these proteins appear to be specifically detected in RH and constitute promising candidates for further studies to elucidate unique features of this single-cell model.

Activity-based protein profiling reveals mitochondrial oxidative enzyme impairment and restoration in diet-induced obese mice

High-fat diet (HFD) induced obesity and concomitant development of insulin resistance (IR) and type 2 diabetes mellitus have been linked to mitochondrial dysfunction. However, it is not clear whether mitochondrial dysfunction is a direct effect of a HFD, or if mitochondrial function is reduced with increased HFD duration. We hypothesized that the function of mitochondrial oxidative and lipid metabolism functions in skeletal muscle mitochondria for HFD mice are similar, or elevated, relative to standard diet (SD) mice; thereby, IR is neither cause nor consequence of mitochondrial dysfunction. We applied a chemical probe approach to identify functionally reactive ATPases and nucleotide-binding proteins in mitochondria isolated from skeletal muscle of C57Bl/6J mice fed HFD or SD chow for 2-, 8-, or 16-weeks; feeding time points known to induce IR. A total of 293 probe-labeled proteins were identified by mass spectrometry-based proteomics, of which 54 differed in abundance between HFD and SD mice. We found proteins associated with the TCA cycle, oxidative phosphorylation (OXPHOS), and lipid metabolism were altered in function when comparing SD to HFD fed mice at 2-weeks, however by 16-weeks HFD mice had TCA cycle, β-oxidation, and respiratory chain function at levels similar to or higher than SD mice.

Multiplexed activity-based protein profiling of the human pathogen Aspergillus fumigatus reveals large functional changes upon exposure to human serum

Environmental adaptability is critical for survival of the fungal human pathogen Aspergillus fumigatus in the immunocompromised host lung. We hypothesized that exposure of the fungal pathogen to human serum would lead to significant alterations to the organism's physiology, including metabolic activity and stress response. Shifts in functional pathway and corresponding enzyme reactivity of A. fumigatus upon exposure to the human host may represent much needed prognostic indicators of fungal infection. To address this, we employed a multiplexed activity-based protein profiling (ABPP) approach coupled to quantitative mass spectrometry-based proteomics to measure broad enzyme reactivity of the fungus cultured with and without human serum. ABPP showed a shift from aerobic respiration to ethanol fermentation and utilization over time in the presence of human serum, which was not observed in serum-free culture. Our approach provides direct insight into this pathogen's ability to survive, adapt, and proliferate. Additionally, our multiplexed ABPP approach captured a broad swath of enzyme reactivity and functional pathways and provides a method for rapid assessment of the A. fumigatus response to external stimuli.

Uterine deletion of Trp53 compromises antioxidant responses in the mouse decidua

Preterm birth is a global health issue impacting millions of mothers and babies. However, the etiology of preterm birth is not clearly understood. Our recent finding that premature decidual senescence with terminal differentiation is a cause of preterm birth in mice with uterine Trp53 deletion, encoding p53 protein, led us to explore other potential factors that are related to preterm birth. Using proteomics approaches, here, we show that 183 candidate proteins show significant changes in deciduae with Trp53 deletion as compared with normal deciduae. Functional categorization of these proteins unveiled new pathways that are influenced by p53. In particular, down-regulation of a cluster of antioxidant enzymes in p53-deficient deciduae suggests that increased oxidative stress could be one cause of preterm birth in mice harboring uterine deletion of Trp53.

Mass spectrometry for translational proteomics: progress and clinical implications

The utility of mass spectrometry (MS)-based proteomic analyses and their clinical applications have been increasingly recognized over the past decade due to their high sensitivity, specificity and throughput. MS-based proteomic measurements have been used in a wide range of biological and biomedical investigations, including analysis of cellular responses and disease-specific post-translational modifications. These studies greatly enhance our understanding of the complex and dynamic nature of the proteome in biology and disease. Some MS techniques, such as those for targeted analysis, are being successfully applied for biomarker verification, whereas others, including global quantitative analysis (for example, for biomarker discovery), are more challenging and require further development. However, recent technological improvements in sample processing, instrumental platforms, data acquisition approaches and informatics capabilities continue to advance MS-based applications. Improving the detection of significant changes in proteins through these advances shows great promise for the discovery of improved biomarker candidates that can be verified pre-clinically using targeted measurements, and ultimately used in clinical studies - for example, for early disease diagnosis or as targets for drug development and therapeutic intervention. Here, we review the current state of MS-based proteomics with regard to its advantages and current limitations, and we highlight its translational applications in studies of protein biomarkers.

Cerebrospinal fluid proteome of patients with acute Lyme disease

During acute Lyme disease, bacteria can disseminate to the central nervous system (CNS), leading to the development of meningitis and other neurologic symptoms. Here we have analyzed pooled cerebrospinal fluid (CSF) allowing a deep view into the proteome for patients diagnosed with early disseminated Lyme disease and CSF inflammation. Additionally, we analyzed individual patient samples and quantified differences in protein abundance employing label-free quantitative mass spectrometry-based methods. We identified 108 proteins that differ significantly in abundance in patients with acute Lyme disease from controls. Comparison between infected patients and control subjects revealed differences in proteins in the CSF associated with cell death localized to brain synapses and others that likely originate from brain parenchyma.

Identification of protein interfaces between α-synuclein, the principal component of Lewy bodies in Parkinson disease, and the molecular chaperones human Hsc70 and the yeast Ssa1p

Fibrillar α-synuclein (α-Syn) is the principal component of Lewy bodies, which are evident in individuals affected by Parkinson disease (PD). This neuropathologic form of α-Syn plays a central role in PD progression as it has been shown to propagate between neurons. Tools that interfere with α-Syn assembly or change the physicochemical properties of the fibrils have potential therapeutic properties as they may be sufficient to interfere with and/or halt cell-to-cell transmission and the systematic spread of α-Syn assemblies within the central nervous system. Vertebrate molecular chaperones from the constitutive/heat-inducible heat shock protein 70 (Hsc/p70) family have been shown to hinder the assembly of soluble α-Syn into fibrils and to bind to the fibrils and very significantly reduce their toxicity. To understand how Hsc70 family members sequester soluble α-Syn, we set up experiments to identify the molecular chaperone-α-Syn surface interfaces. We cross-linked human Hsc70 and its yeast homologue Ssa1p and α-Syn using a chemical cross-linker and mapped the Hsc70- and Ssa1p-α-Syn interface. We show that the client binding domain of Hsc70 and Ssa1p binds two regions within α-Syn similar to a tweezer, with the first spanning residues 10-45 and the second spanning residues 97-102. Our findings define what is necessary and sufficient for engineering Hsc70- and Ssa1p-derived polypeptide with minichaperone properties with a potential as therapeutic agents in Parkinson disease through their ability to affect α-Syn assembly and/or toxicity.

Fully automated multifunctional ultrahigh pressure liquid chromatography system for advanced proteome analyses

A multifunctional liquid chromatography system that performs 1-dimensional, 2-dimensional (strong cation exchange/reverse phase liquid chromatography or SCX/RPLC) separations and online phosphopeptide enrichment using a single binary nanoflow pump has been developed. With a simple operation of a function selection valve equipped with a SCX column and a TiO2 (titanium dioxide) column, a fully automated selection of three different experiment modes was achieved. Because the current system uses essentially the same solvent flow paths, the same trap column, and the same separation column for reverse-phase separation of 1D, 2D, and online phosphopeptides enrichment experiments, the elution time information obtained from these experiments is in excellent agreement, which facilitates correlating peptide information from different experiments. The final reverse-phase separation of the three experiments is completely decoupled from all of the function selection processes; thereby salts or acids from SCX or TiO2 column do not affect the efficiency of the reverse-phase separation.

Proteomic analysis of bronchoalveolar lavage fluid proteins from mice infected with Francisella tularensis ssp. novicida

Francisella tularensis causes the zoonosis tularemia in humans and is one of the most virulent bacterial pathogens. We utilized a global proteomic approach to characterize protein changes in bronchoalveolar lavage fluid from mice exposed to one of three organisms, F. tularensis ssp. novicida, an avirulent mutant of F. tularensis ssp. novicida (F.t. novicida-ΔmglA), and Pseudomonas aeruginosa. The composition of bronchoalveolar lavage fluid (BALF) proteins was altered following infection, including proteins involved in neutrophil activation, oxidative stress, and inflammatory responses. Components of the innate immune response were induced including the acute phase response and the complement system; however, the timing of their induction varied. F. tularensis ssp. novicida infected mice do not appear to have an effective innate immune response in the first hours of infection; however, within 24 h, they show an upregulation of innate immune response proteins. This delayed response is in contrast to P. aeruginosa infected animals which show an early innate immune response. Likewise, F.t. novicida-ΔmglA infection initiates an early innate immune response; however, this response is diminished by 24 h. Finally, this study identifies several candidate biomarkers, including Chitinase 3-like-1 (CHI3L1 or YKL-40) and peroxiredoxin 1, that are associated with F. tularensis ssp. novicida but not P. aeruginosa infection.

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

MOTIVATION

Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets.

RESULTS

Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions.

AVAILABILITY

The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials.

CONTACT

ctekwe@stat.tamu.edu.

Mass spectrometry-based proteomics: existing capabilities and future directions

Mass spectrometry (MS)-based proteomics is emerging as a broadly effective means for identification, characterization, and quantification of proteins that are integral components of the processes essential for life. Characterization of proteins at the proteome and sub-proteome (e.g., the phosphoproteome, proteoglycome, or degradome/peptidome) levels provides a foundation for understanding fundamental aspects of biology. Emerging technologies such as ion mobility separations coupled with MS and microchip-based-proteome measurements combined with MS instrumentation and chromatographic separation techniques, such as nanoscale reversed phase liquid chromatography and capillary electrophoresis, show great promise for both broad undirected and targeted highly sensitive measurements. MS-based proteomics increasingly contribute to our understanding of the dynamics, interactions, and roles that proteins and peptides play, advancing our understanding of biology on a systems wide level for a wide range of applications including investigations of microbial communities, bioremediation, and human health.

Morphine produces immunosuppressive effects in nonhuman primates at the proteomic and cellular levels

Morphine has long been known to have immunosuppressive properties in vivo, but the molecular and immunologic changes induced by it are incompletely understood. To explore how these changes interact with lentiviral infections in vivo, animals from two nonhuman primate species (African green monkeys and pigtailed macaques) were provided morphine and studied using a systems biology approach. Biological specimens were obtained from multiple sources (e.g. lymph node, colon, cerebrospinal fluid, and peripheral blood) before and after the administration of morphine (titrated up to a maximum dose of 5 mg/kg over a period of 20 days). Cellular immune, plasma cytokine, and proteome changes were measured and morphine-induced changes in these parameters were assessed on an interorgan, interindividual, and interspecies basis. In both species, morphine was associated with decreased levels of Ki-67(+) T-cell activation but with only minimal changes in overall T-cell counts, neutrophil counts, and NK cell counts. Although changes in T-cell maturation were observed, these varied across the various tissue/fluid compartments studied. Proteomic analysis revealed a morphine-induced suppressive effect in lymph nodes, with decreased abundance of protein mediators involved in the functional categories of energy metabolism, signaling, and maintenance of cell structure. These findings have direct relevance for understanding the impact of heroin addiction and the opioids used to treat addiction as well as on the potential interplay between opioid abuse and the immunological response to an infective agent.

A hybrid approach to protein differential expression in mass spectrometry-based proteomics

MOTIVATION

Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis.

RESULTS

We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate.

AVAILABILITY

All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.

Mass spectrometry-based proteomics: existing capabilities and future directions

Mass spectrometry (MS)-based proteomics is emerging as a broadly effective means for identification, characterization, and quantification of proteins that are integral components of the processes essential for life. Characterization of proteins at the proteome and sub-proteome (e.g., the phosphoproteome, proteoglycome, or degradome/peptidome) levels provides a foundation for understanding fundamental aspects of biology. Emerging technologies such as ion mobility separations coupled with MS and microchip-based-proteome measurements combined with MS instrumentation and chromatographic separation techniques, such as nanoscale reversed phase liquid chromatography and capillary electrophoresis, show great promise for both broad undirected and targeted highly sensitive measurements. MS-based proteomics increasingly contribute to our understanding of the dynamics, interactions, and roles that proteins and peptides play, advancing our understanding of biology on a systems wide level for a wide range of applications including investigations of microbial communities, bioremediation, and human health.

Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142

Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.

Cyanobacteria have been promoted as platforms for biofuel production due to their useful physiological properties such as photosynthesis, relatively rapid growth rates, ability to accumulate high amounts of intracellular compounds and tolerance to extreme environments. However, development of a computational model is an important step to synthesize biochemical, physiological and regulatory understanding of photoautotrophic metabolism (either qualitatively or quantitatively) at a systems level, to make metabolic engineering of these organisms tractable. When integrated with other genome-scale data (e.g., expression data), numerical simulations can provide experimentally testable predictions of carbon fluxes and reductant partitioning to different biosynthetic pathways and macromolecular synthesis. This work is the first to computationally explore the interactions between components of photosynthetic and respiratory systems in detail. In silico predictions obtained from model analysis provided insights into the effects of light quantity and quality upon fluxes through electron transport pathways, alternative pathways for reductant consumption and carbon metabolism. The model will not only serve as a platform to develop genome-scale metabolic models for other cyanobacteria, but also as an engineering tool for manipulation of photosynthetic microorganisms to improve biofuel production.

The cerebrospinal fluid proteome in HIV infection: change associated with disease severity

BACKGROUND

Central nervous system (CNS) infection is a nearly universal feature of untreated systemic HIV infection with a clinical spectrum that ranges from chronic asymptomatic infection to severe cognitive and motor dysfunction. Analysis of cerebrospinal fluid (CSF) has played an important part in defining the character of this evolving infection and response to treatment. To further characterize CNS HIV infection and its effects, we applied advanced high-throughput proteomic methods to CSF to identify novel proteins and their changes with disease progression and treatment.

RESULTS

After establishing an accurate mass and time (AMT) tag database containing 23,141 AMT tags for CSF peptides, we analyzed 91 CSF samples by LC-MS from 12 HIV-uninfected and 14 HIV-infected subjects studied in the context of initiation of antiretroviral therapy and correlated abundances of identified proteins a) within and between subjects, b) with all other proteins across the entire sample set, and c) with "external" CSF biomarkers of infection (HIV RNA), immune activation (neopterin) and neural injury (neurofilament light chain protein, NFL). We identified a mean of 2,333 +/- 328 (SD) peptides covering 307 +/-16 proteins in the 91 CSF sample set. Protein abundances differed both between and within subjects sampled at different time points and readily separated those with and without HIV infection. Proteins also showed inter-correlations across the sample set that were associated with biologically relevant dynamic processes. One-hundred and fifty proteins showed correlations with the external biomarkers. For example, using a threshold of cross correlation coefficient (Pearson's) ≤ -0.3 and ≥0.3 for potentially meaningful relationships, a total of 99 proteins correlated with CSF neopterin (43 negative and 56 positive correlations) and related principally to neuronal plasticity and survival and to innate immunity. Pathway analysis defined several networks connecting the identified proteins, including one with amyloid precursor protein as a central node.

CONCLUSIONS

Advanced CSF proteomic analysis enabled the identification of an array of novel protein changes across the spectrum of CNS HIV infection and disease. This initial analysis clearly demonstrated the value of contemporary state-of-the-art proteomic CSF analysis as a discovery tool in HIV infection with likely similar application to other neurological inflammatory and degenerative diseases.

Probing the conformation of the ISWI ATPase domain with genetically encoded photoreactive crosslinkers and mass spectrometry

We present a strategy for rapidly gaining structural information about a protein from crosslinks formed by genetically encoded unnatural amino acids. We applied it to ISWI, a chromatin remodeling enzyme involved in chromatin assembly, DNA replication and transcription. ISWI is part of the vast Snf2 family of helicase-related proteins, many of which constitute the catalytic cores of chromatin remodeling complexes. Structural information about this family is scarce, hampering our mechanistic understanding of chromatin remodeling. Making use of cells that harbor a special tRNA/aminoacyl-tRNA synthetase pair, several residues within the ATPase domain of ISWI were individually substituted with the UV-reactive unnatural amino acid p-benzoyl-p-phenylalanine. Intramolecular crosslinks could be mapped with amino acid precision by high resolution tandem mass spectrometry and the novel bioinformatic tool "Crossfinder." Most crosslinks were fully consistent with published crystal structures of ISWI-related ATPases. A subset of crosslinks, however, disagreed with the conformations previously captured in crystal structures. We built a structural model using the distance information obtained from the crosslinks and the structure of the closest crystallized relative, Chd1. The model shows the ATPase lobes strongly rotated against each other, a movement postulated earlier to be necessary to achieve a catalytically competent state. The minimal requirements for solubility and protein amounts make our approach ideal for studying structures and conformations of proteins that are not amenable to conventional structural techniques.

Dynamic proteome analysis of Cyanothece sp. ATCC 51142 under constant light

Understanding the dynamic nature of protein abundances provides insights into protein turnover not readily apparent from conventional, static mass spectrometry measurements. This level of data is particularly informative when surveying protein abundances in biological systems subjected to large perturbations or alterations in environment such as cyanobacteria. Our current analysis expands upon conventional proteomic approaches in cyanobacteria by measuring dynamic changes of the proteome using a (13)C(15)N-l-leucine metabolic labeling in Cyanothece ATCC51142. Metabolically labeled Cyanothece ATCC51142 cells grown under nitrogen-sufficient conditions in continuous light were monitored longitudinally for isotope incorporation over a 48 h period, revealing 414 proteins with dynamic changes in abundances. In particular, proteins involved in carbon fixation, pentose phosphate pathway, cellular protection, redox regulation, protein folding, assembly, and degradation showed higher levels of isotope incorporation, suggesting that these biochemical pathways are important for growth under continuous light. Calculation of relative isotope abundances (RIA) values allowed the measurement of actual active protein synthesis over time for different biochemical pathways under high light exposure. Overall results demonstrated the utility of "non-steady state" pulsed metabolic labeling for systems-wide dynamic quantification of the proteome in Cyanothece ATCC51142 that can also be applied to other cyanobacteria.

Characterizing the Escherichia coli O157:H7 proteome including protein associations with higher order assemblies

Background

The recent outbreak of severe infections with Shiga toxin (Stx) producing Escherichia coli (STEC) serotype O104:H4 highlights the need to understand horizontal gene transfer among E. coli strains, identify novel virulence factors and elucidate their pathogenesis. Quantitative shotgun proteomics can contribute to such objectives, allowing insights into the part of the genome translated into proteins and the connectivity of biochemical pathways and higher order assemblies of proteins at the subcellular level.

Methodology/Principal Findings

We examined protein profiles in cell lysate fractions of STEC strain 86-24 (serotype O157:H7), following growth in cell culture or bacterial isolation from intestines of infected piglets, in the context of functionally and structurally characterized biochemical pathways of E. coli. Protein solubilization in the presence of Triton X-100, EDTA and high salt was followed by size exclusion chromatography into the approximate M_r ranges greater than 280 kDa, 280-80 kDa and 80-10 kDa. Peptide mixtures resulting from these and the insoluble fraction were analyzed by quantitative 2D-LC-nESI-MS/MS. Of the 2521 proteins identified at a 1% false discovery rate, representing 47% of all predicted E. coli O157:H7 gene products, the majority of integral membrane proteins were enriched in the high M_r fraction. Hundreds of proteins were enriched in a M_r range higher than that predicted for a monomer supporting their participation in protein complexes. The insoluble STEC fraction revealed enrichment of aggregation-prone proteins, including many that are part of large structure/function entities such as the ribosome, cytoskeleton and O-antigen biosynthesis cluster.

Significance

Nearly all E. coli O157:H7 proteins encoded by prophage regions were expressed at low abundance levels or not detected. Comparative quantitative analyses of proteins from distinct cell lysate fractions allowed us to associate uncharacterized proteins with membrane attachment, potential participation in stable protein complexes, and susceptibility to aggregation as part of larger structural assemblies.

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.

Membrane stresses induced by overproduction of free fatty acids in Escherichia coli

Microbially produced fatty acids are potential precursors to high-energy-density biofuels, including alkanes and alkyl ethyl esters, by either catalytic conversion of free fatty acids (FFAs) or enzymatic conversion of acyl-acyl carrier protein or acyl-coenzyme A intermediates. Metabolic engineering efforts aimed at overproducing FFAs in Escherichia coli have achieved less than 30% of the maximum theoretical yield on the supplied carbon source. In this work, the viability, morphology, transcript levels, and protein levels of a strain of E. coli that overproduces medium-chain-length FFAs was compared to an engineered control strain. By early stationary phase, an 85% reduction in viable cell counts and exacerbated loss of inner membrane integrity were observed in the FFA-overproducing strain. These effects were enhanced in strains endogenously producing FFAs compared to strains exposed to exogenously fed FFAs. Under two sets of cultivation conditions, long-chain unsaturated fatty acid content greatly increased, and the expression of genes and proteins required for unsaturated fatty acid biosynthesis were significantly decreased. Membrane stresses were further implicated by increased expression of genes and proteins of the phage shock response, the MarA/Rob/SoxS regulon, and the nuo and cyo operons of aerobic respiration. Gene deletion studies confirmed the importance of the phage shock proteins and Rob for maintaining cell viability; however, little to no change in FFA titer was observed after 24 h of cultivation. The results of this study serve as a baseline for future targeted attempts to improve FFA yields and titers in E. coli.

SILACtor: software to enable dynamic SILAC studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods.

Aging enhances the production of reactive oxygen species and bactericidal activity in peritoneal macrophages by upregulating classical activation pathways

Maintenance of macrophages in their basal state and their rapid activation in response to pathogen detection are central to the innate immune system, acting to limit nonspecific oxidative damage and promote pathogen killing following infection. To identify possible age-related alterations in macrophage function, we have assayed the function of peritoneal macrophages from young (3-4 months) and aged (14-15 months) Balb/c mice. In agreement with prior suggestions, we observe age-dependent increases in the extent of recruitment of macrophages into the peritoneum, as well as ex vivo functional changes involving enhanced nitric oxide production under resting conditions that contribute to a reduction in the time needed for full activation of senescent macrophages following exposure to lipopolysaccharides (LPS). Further, we observe enhanced bactericidal activity following Salmonella uptake by macrophages isolated from aged Balb/c mice in comparison with those isolated from young animals. Pathways responsible for observed phenotypic changes were interrogated using tandem mass spectrometry, which identified age-dependent increases in levels of proteins linked to immune cell pathways under basal conditions and following LPS activation. Immune pathways upregulated in macrophages isolated from aged mice include proteins critical to the formation of the immunoproteasome. Detection of these latter proteins is dramatically enhanced following LPS exposure for macrophages isolated from aged animals; in comparison, the identification of immunoproteasome subunits is insensitive to LPS exposure for macrophages isolated from young animals. Consistent with observed global changes in the proteome, quantitative proteomic measurements indicate that there are age-dependent abundance changes involving specific proteins linked to immune cell function under basal conditions. LPS exposure selectively increases the levels of many proteins involved in immune cell function in aged Balb/c mice. Collectively, these results indicate that macrophages isolated from old mice are in a preactivated state that enhances their sensitivities to LPS exposure. The hyper-responsive activation of macrophages in aged animals may act to minimize infection by general bacterial threats that arise due to age-dependent declines in adaptive immunity. However, this hypersensitivity and the associated increase in the level of formation of reactive oxygen species are likely to contribute to observed age-dependent increases in the level of oxidative damage that underlie many diseases of the elderly.

A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies.

Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics

Despite unmet needs for cardiovascular biomarkers, few new protein markers have been approved by the US Food and Drug Administration for the diagnosis or screening of cardiovascular diseases. Mass spectrometry-based proteomics technologies are capable of identifying hundreds to thousands of proteins in cells, tissues, and biofluids. Proteomics may therefore provide the opportunity to elucidate new biomarkers and pathways without a prior known association with cardiovascular disease; however, important obstacles remain. In this review, we focus on emerging techniques that may form a coherently integrated pipeline to overcome present limitations to both the discovery and validation processes.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella

Background

Systemic bacterial infections are highly regulated and complex processes that are orchestrated by numerous virulence factors. Genes that are coordinately controlled by the set of regulators required for systemic infection are potentially required for pathogenicity.

Results

In this study we present a systems biology approach in which sample-matched multi-omic measurements of fourteen virulence-essential regulator mutants were coupled with computational network analysis to efficiently identify Salmonella virulence factors. Immunoblot experiments verified network-predicted virulence factors and a subset was determined to be secreted into the host cytoplasm, suggesting that they are virulence factors directly interacting with host cellular components. Two of these, SrfN and PagK2, were required for full mouse virulence and were shown to be translocated independent of either of the type III secretion systems in Salmonella or the type III injectisome-related flagellar mechanism.

Conclusions

Integrating multi-omic datasets from Salmonella mutants lacking virulence regulators not only identified novel virulence factors but also defined a new class of translocated effectors involved in pathogenesis. The success of this strategy at discovery of known and novel virulence factors suggests that the approach may have applicability for other bacterial pathogens.

Comprehensive identification of glycated peptides and their glycation motifs in plasma and erythrocytes of control and diabetic subjects

Nonenzymatic glycation of proteins sets the stage for formation of advanced glycation end-products and development of chronic complications of diabetes. In this report, we extended our previous methods on proteomics analysis of glycated proteins to comprehensively identify glycated proteins in control and diabetic human plasma and erythrocytes. Using immunodepletion, enrichment, and fractionation strategies, we identified 7749 unique glycated peptides, corresponding to 3742 unique glycated proteins. Semiquantitative comparisons showed that glycation levels of a number of proteins were significantly increased in diabetes and that erythrocyte proteins were more extensively glycated than plasma proteins. A glycation motif analysis revealed that some amino acids were favored more than others in the protein primary structures in the vicinity of the glycation sites in both sample types. The glycated peptides and corresponding proteins reported here provide a foundation for potential identification of novel markers for diabetes, hyperglycemia, and diabetic complications in future studies.

Comparative analysis of proteome and transcriptome variation in mouse

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography–Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

An old dogma in biology states that, in every cell, the flow of biological information is from DNA to RNA to proteins and that the latter act as a working force to determine the organism's phenotype. This model predicts that changes in DNA that affect the clinical phenotype should also similarly change the cellular levels of RNA and protein levels. In this report, we test this prediction by looking at the concordance between DNA variation in population of mouse inbred strains, the RNA and protein variation in the liver tissue of these mice, and variation in metabolic phenotypes. We show that the relationship between various biological traits is not simple and that there is relatively little concordance of RNA levels and the corresponding protein levels in response to DNA perturbations. In addition, we also find that, surprisingly, metabolic traits correlate better to RNA levels than to protein levels. In light of current efforts in searching for the molecular bases of disease susceptibility in humans, our findings highlight the complexity of information flow that underlies clinical outcomes.

Technologies and approaches to elucidate and model the virulence program of salmonella

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.

DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data

Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.

Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells

In this study, we evaluated a concatenated low pH (pH 3) and high pH (pH 10) reversed-phase liquid chromatography strategy as a first dimension for two-dimensional liquid chromatography tandem mass spectrometry ("shotgun") proteomic analysis of trypsin-digested human MCF10A cell sample. Compared with the more traditional strong cation exchange method, the use of concatenated high pH reversed-phase liquid chromatography as a first-dimension fractionation strategy resulted in 1.8- and 1.6-fold increases in the number of peptide and protein identifications (with two or more unique peptides), respectively. In addition to broader identifications, advantages of the concatenated high pH fractionation approach include improved protein sequence coverage, simplified sample processing, and reduced sample losses. The results demonstrate that the concatenated high pH reversed-phased strategy is an attractive alternative to strong cation exchange for two-dimensional shotgun proteomic analysis.

Integrated post-experiment monoisotopic mass refinement: an integrated approach to accurately assign monoisotopic precursor masses to tandem mass spectrometric data

Accurate assignment of monoisotopic precursor masses to tandem mass spectrometric (MS/MS) data is a fundamental and critically important step for successful peptide identifications in mass spectrometry based proteomics. Here we describe an integrated approach that combines three previously reported methods of treating MS/MS data for precursor mass refinement. This combined method, "integrated post-experiment monoisotopic mass refinement" (iPE-MMR), integrates steps (1) generation of refined MS/MS data by DeconMSn; (2) additional refinement of the resultant MS/MS data by a modified version of PE-MMR; and (3) elimination of systematic errors of precursor masses using DtaRefinery. iPE-MMR is the first method that utilizes all MS information from multiple MS scans of a precursor ion including multiple charge states, in an MS scan, to determine precursor mass. With the combination of these methods, iPE-MMR increases sensitivity in peptide identification and provides increased accuracy when applied to complex high-throughput proteomics data.

Distinct cerebrospinal fluid proteomes differentiate post-treatment lyme disease from chronic fatigue syndrome

Background

Neurologic Post Treatment Lyme disease (nPTLS) and Chronic Fatigue (CFS) are syndromes of unknown etiology. They share features of fatigue and cognitive dysfunction, making it difficult to differentiate them. Unresolved is whether nPTLS is a subset of CFS.

Methods and Principal Findings

Pooled cerebrospinal fluid (CSF) samples from nPTLS patients, CFS patients, and healthy volunteers were comprehensively analyzed using high-resolution mass spectrometry (MS), coupled with immunoaffinity depletion methods to reduce protein-masking by abundant proteins. Individual patient and healthy control CSF samples were analyzed directly employing a MS-based label-free quantitative proteomics approach. We found that both groups, and individuals within the groups, could be distinguished from each other and normals based on their specific CSF proteins (p<0.01). CFS (n = 43) had 2,783 non-redundant proteins, nPTLS (n = 25) contained 2,768 proteins, and healthy normals had 2,630 proteins. Preliminary pathway analysis demonstrated that the data could be useful for hypothesis generation on the pathogenetic mechanisms underlying these two related syndromes.

Conclusions

nPTLS and CFS have distinguishing CSF protein complements. Each condition has a number of CSF proteins that can be useful in providing candidates for future validation studies and insights on the respective mechanisms of pathogenesis. Distinguishing nPTLS and CFS permits more focused study of each condition, and can lead to novel diagnostics and therapeutic interventions.

Global proteomic analysis of two tick-borne emerging zoonotic agents: anaplasma phagocytophilum and ehrlichia chaffeensis

Anaplasma phagocytophilum and Ehrlichia chaffeensis are obligatory intracellular α-proteobacteria that infect human leukocytes and cause potentially fatal emerging zoonoses. In the present study, we determined global protein expression profiles of these bacteria cultured in the human promyelocytic leukemia cell line, HL-60. Mass spectrometric (MS) analyses identified a total of 1,212 A. phagocytophilum and 1,021 E. chaffeensis proteins, representing 89.3 and 92.3% of the predicted bacterial proteomes, respectively. Nearly all bacterial proteins (≥99%) with known functions were expressed, whereas only approximately 80% of “hypothetical” proteins were detected in infected human cells. Quantitative MS/MS analyses indicated that highly expressed proteins in both bacteria included chaperones, enzymes involved in biosynthesis and metabolism, and outer membrane proteins, such as A. phagocytophilum P44 and E. chaffeensis P28/OMP-1. Among 113 A. phagocytophilum p44 paralogous genes, 110 of them were expressed and 88 of them were encoded by pseudogenes. In addition, bacterial infection of HL-60 cells up-regulated the expression of human proteins involved mostly in cytoskeleton components, vesicular trafficking, cell signaling, and energy metabolism, but down-regulated some pattern recognition receptors involved in innate immunity. Our proteomics data represent a comprehensive analysis of A. phagocytophilum and E. chaffeensis proteomes, and provide a quantitative view of human host protein expression profiles regulated by bacterial infection. The availability of these proteomic data will provide new insights into biology and pathogenesis of these obligatory intracellular pathogens.