Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment.

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 µm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures.

ABSTRACT: Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS's initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways.

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

Macrophage-derived MLKL in alcohol-associated liver disease: Regulation of phagocytosis.

BACKGROUND AND AIMS: Mixed lineage kinase domain-like pseudokinase (MLKL), a key terminal effector of necroptosis, also plays a role in intracellular vesicle trafficking that is critical for regulating liver inflammation and injury in alcohol-associated liver disease (ALD). Although receptor interacting protein kinase 3 (Rip3)-/- mice are completely protected from ethanol-induced liver injury, Mlkl-/- mice are only partially protected. Therefore, we hypothesized that cell-specific functions of MLKL may contribute to ethanol-induced injury. APPROACH AND RESULTS: Bone marrow transplants between Mlkl-/- mice and littermates were conducted to distinguish the role of myeloid versus nonmyeloid Mlkl in the Gao-binge model of ALD. Ethanol-induced hepatic injury, steatosis, and inflammation were exacerbated in Mlkl-/- →wild-type (WT) mice, whereas Mlkl deficiency in nonmyeloid cells (WT→ Mlkl-/- ) had no effect on Gao-binge ethanol-induced injury. Importantly, Mlkl deficiency in myeloid cells exacerbated ethanol-mediated bacterial burden and accumulation of immune cells in livers. Mechanistically, challenging macrophages with lipopolysaccharide (LPS) induced signal transducer and activator of transcription 1-mediated expression and phosphorylation of MLKL, as well as translocation and oligomerization of MLKL to intracellular compartments, including phagosomes and lysosomes but not plasma membrane. Importantly, pharmacological or genetic inhibition of MLKL suppressed the phagocytic capability of primary mouse Kupffer cells (KCs) at baseline and in response to LPS with/without ethanol as well as peripheral monocytes isolated from both healthy controls and patients with alcohol-associated hepatitis. Further, in vivo studies revealed that KCs of Mlkl-/- mice phagocytosed fewer bioparticles than KCs of WT mice. CONCLUSION: Together, these data indicate that myeloid MLKL restricts ethanol-induced liver inflammation and injury by regulating hepatic immune cell homeostasis and macrophage phagocytosis.

Integrated Transcriptomic and Proteomic Analysis Identifies Plasma Biomarkers of Hepatocellular Failure in Alcohol-Associated Hepatitis.

Alcohol-associated hepatitis (AH) is a form of liver failure with high short-term mortality. Recent studies have shown that defective function of hepatocyte nuclear factor 4 alpha (HNF4a) and systemic inflammation are major disease drivers of AH. Plasma biomarkers of hepatocyte function could be useful for diagnostic and prognostic purposes. Herein, an integrative analysis of hepatic RNA sequencing and liquid chromatography-tandem mass spectrometry was performed to identify plasma protein signatures for patients with mild and severe AH. Alcohol-related liver disease cirrhosis, nonalcoholic fatty liver disease, and healthy subjects were used as comparator groups. Levels of identified proteins primarily involved in hepatocellular function were decreased in patients with AH, which included hepatokines, clotting factors, complement cascade components, and hepatocyte growth activators. A protein signature of AH disease severity was identified, including thrombin, hepatocyte growth factor α, clusterin, human serum factor H-related protein, and kallistatin, which exhibited large abundance shifts between severe and nonsevere AH. The combination of thrombin and hepatocyte growth factor α discriminated between severe and nonsevere AH with high sensitivity and specificity. These findings were correlated with the liver expression of genes encoding secreted proteins in a similar cohort, finding a highly consistent plasma protein signature reflecting HNF4A and HNF1A functions. This unbiased proteomic-transcriptome analysis identified plasma protein signatures and pathways associated with disease severity, reflecting HNF4A/1A activity useful for diagnostic assessment in AH.

An antibody-free platform for multiplexed, sensitive quantification of protein biomarkers in complex biomatrices.

Sensitive, multiplexed protein quantification remains challenging despite recent advancements in LC-MS assays for targeted protein biomarker quantification. High-sensitivity protein biomarker measurements usually require immuno-affinity enrichment of target protein; a process which is highly dependent on capture reagent and limited in capability to measure multiple analytes. Herein, we report a novel antibody-free platform, which measures multiple biomarkers from complex matrices employing a strategically optimized solid-phase extraction cleanup and orthogonal multidimensional LC-MS. Eight human protein biomarkers with different specifications were spiked into canine plasma as a model investigation system. The developed strategy achieved the desired sensitivity, robustness, and throughput via the following steps: (1) post digestion mixed-mode cation exchange-reverse phase SPE enrichment cleaned up the sample initially; (2) rapid, high-pH peptide fractionation further eliminated background components efficiently while selectively enriched signature peptides (SP) to provide sufficient sensitivity for multiple targets; and (3) trapping-micro-LC-MS analysis delivered high sensitivity comparable to a nano-LC-MS method but with much better robustness and throughput for the final analysis. Compared with a conventional LC-MS assay with direct protein digestion and limited clean-up, analysis with this antibody-free platform improved the LLOQ by 1-2 orders of magnitude for the eight protein biomarkers, reaching as low as 5 ng/mL in plasma, with feasible robustness and throughput. This platform was applied for the quantification of biomarkers of respiratory conditions in patients with various lung diseases, demonstrating real-world applicability.

Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol.

Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or "omics," quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain.

Mass Spectrometry-Based Proteomics for Analysis of Hydrophilic Phosphopeptides.

Protein phosphorylation is a critical posttranslational modification (PTM), with cell signaling networks being tightly regulated by protein phosphorylation. Despite recent technological advances in reversed-phase liquid chromatography (RPLC)-mass spectrometry (MS)-based proteomics, comprehensive phosphoproteomic coverage in complex biological systems remains challenging, especially for hydrophilic phosphopeptides that often have multiple phosphorylation sites. Herein, we describe an MS-based phosphoproteomics protocol for effective quantitative analysis of hydrophilic phosphopeptides. This protocol was built upon a simple tandem mass tag (TMT)-labeling method for significantly increasing peptide hydrophobicity, thus effectively enhancing RPLC-MS analysis of hydrophilic peptides. Through phosphoproteomic analyses of MCF7 cells, this method was demonstrated to greatly increase the number of identified hydrophilic phosphopeptides and improve MS signal detection. With the TMT labeling method, we were able to identify a previously unreported phosphopeptide from the G protein-coupled receptor (GPCR) CXCR3, QPpSSSR, which is thought to be important in regulating receptor signaling. This protocol is easy to adopt and implement and thus should have broad utility for effective RPLC-MS analysis of the hydrophilic phosphoproteome as well as other highly hydrophilic analytes.

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics.

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-ß-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for 'all-in-one' one-pot sample preparation. This 'all-in-one' method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

Tandem Mass Tag Labeling Facilitates Reversed-Phase Liquid Chromatography-Mass Spectrometry Analysis of Hydrophilic Phosphopeptides.

Protein phosphorylation is a critical post-translational modification (PTM). Despite recent technological advances in reversed-phase liquid chromatography (RPLC)-mass spectrometry (MS)-based proteomics, comprehensive phosphoproteomic coverage in complex biological systems remains challenging, especially for hydrophilic phosphopeptides with enriched regions of serines, threonines, and tyrosines that often orchestrate critical biological functions. To address this issue, we developed a simple, easily implemented method to introduce a commonly used tandem mass tag (TMT) to increase peptide hydrophobicity, effectively enhancing RPLC-MS analysis of hydrophilic peptides. Different from conventional TMT labeling, this method capitalizes on using a nonprimary amine buffer and TMT labeling occurring before C18-based solid phase extraction. Through phosphoproteomic analyses of MCF7 cells, we have demonstrated that this method can greatly increase the number of identified hydrophilic phosphopeptides and improve MS detection signals. We applied this method to study the peptide QPSSSR, a very hydrophilic tryptic peptide located on the C-terminus of the G protein-coupled receptor (GPCR) CXCR3. Identification of QPSSSR has never been reported, and we were unable to detect it by traditional methods. We validated our TMT labeling strategy by comparative RPLC-MS analyses of both a hydrophilic QPSSSR peptide library as well as common phosphopeptides. We further confirmed the utility of this method by quantifying QPSSSR phosphorylation abundances in HEK 293 cells under different treatment conditions predicted to alter QPSSSR phosphorylation. We anticipate that this simple TMT labeling method can be broadly used not only for decoding GPCR phosphoproteome but also for effective RPLC-MS analysis of other highly hydrophilic analytes.

A Targeted Mass Spectrometric Assay for Reliable Sensitive Hepcidin Quantification.

Hepcidin, a cysteine-rich peptide hormone, secreted mainly by the liver, plays a central role in iron metabolism regulation. Emerging evidence suggests that disordered iron metabolism is a risk factor for various types of diseases including cancers. However, it remains challenging to apply current mass spectrometry (MS)-based hepcidin assays for precise quantification due to the low fragmentation efficiency of intact hepcidin as well as synthesis difficulties for the intact hepcidin standard. To address these issues we recently developed a reliable sensitive targeted MS assay for hepcidin quantification from clinical samples that uses fully alkylated rather than intact hepcidin as the internal standard. Limits of detection and quantification were determined to be <0.5 ng/mL and 1 ng/mL, respectively. Application of the alkylated hepcidin assay to 70 clinical plasma samples (42 non-cancerous and 28 ovarian cancer patient samples) enabled reliable detection of endogenous hepcidin from the plasma samples, as well as conditioned culture media. The hepcidin concentrations ranged from 0.0 to 95.6 ng/mL across non-cancerous and cancer plasma specimens. Interestingly, cancer patients were found to have significantly higher hepcidin concentrations compared to non-cancerous patients (mean: 20.6 ng/ml for cancer; 5.94 ng/ml for non-cancerous) (p value < 0.001). Our results represent the first application of the alkylated hepcidin assay to clinical samples and demonstrate that the developed assay has better sensitivity and quantification accuracy than current MS-based hepcidin assays without the challenges in synthesis of intact hepcidin standard and accurately determining its absolute amount.

Mitotic protein kinase CDK1 phosphorylation of mRNA translation regulator 4E-BP1 Ser83 may contribute to cell transformation.

Mammalian target of rapamycin (mTOR)-directed eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1) phosphorylation promotes cap-dependent translation and tumorigenesis. During mitosis, cyclin-dependent kinase 1 (CDK1) substitutes for mTOR and fully phosphorylates 4E-BP1 at canonical sites (T37, T46, S65, and T70) and the noncanonical S83 site, resulting in a mitosis-specific hyperphosphorylated δ isoform. Colocalization studies with a phospho-S83 specific antibody indicate that 4E-BP1 S83 phosphorylation accumulates at centrosomes during prophase, peaks at metaphase, and decreases through telophase. Although S83 phosphorylation of 4E-BP1 does not affect general cap-dependent translation, expression of an alanine substitution mutant 4E-BP1.S83A partially reverses rodent cell transformation induced by Merkel cell polyomavirus small T antigen viral oncoprotein. In contrast to inhibitory mTOR 4E-BP1 phosphorylation, these findings suggest that mitotic CDK1-directed phosphorylation of δ-4E-BP1 may yield a gain of function, distinct from translation regulation, that may be important in tumorigenesis and mitotic centrosome function.

The landscape of viral proteomics and its potential to impact human health.

INTRODUCTION: Advances in mass spectrometry-based proteomic technologies are enhancing studies of viral pathogenesis. Identification and quantification of host and viral proteins and modifications in cells and extracellular fluids during infection provides useful information about pathogenesis, and will be critical for directing clinical interventions and diagnostics. AREAS COVERED: Herein we review and discuss a broad range of global proteomic studies conducted during viral infection, including those of cellular responses, protein modifications, virion packaging, and serum proteomics. We focus on viruses that impact human health and focus on experimental designs that reveal disease processes and surrogate markers. Expert commentary: Global proteomics is an important component of systems-level studies that aim to define how the interaction of humans and viruses leads to disease. Viral-community resource centers and strategies from other fields (e.g., cancer) will facilitate data sharing and platform-integration for systems-level analyses, and should provide recommended standards and assays for experimental designs and validation.

Proteome-wide light/dark modulation of thiol oxidation in cyanobacteria revealed by quantitative site-specific redox proteomics.

Reversible protein thiol oxidation is an essential regulatory mechanism of photosynthesis, metabolism, and gene expression in photosynthetic organisms. Herein, we present proteome-wide quantitative and site-specific profiling of in vivo thiol oxidation modulated by light/dark in the cyanobacterium Synechocystis sp. PCC 6803, an oxygenic photosynthetic prokaryote, using a resin-assisted thiol enrichment approach. Our proteomic approach integrates resin-assisted enrichment with isobaric tandem mass tag labeling to enable site-specific and quantitative measurements of reversibly oxidized thiols. The redox dynamics of ∼2,100 Cys-sites from 1,060 proteins under light, dark, and 3-(3,4-dichlorophenyl)-1,1-dimethylurea (a photosystem II inhibitor) conditions were quantified. In addition to relative quantification, the stoichiometry or percentage of oxidation (reversibly oxidized/total thiols) for ∼1,350 Cys-sites was also quantified. The overall results revealed broad changes in thiol oxidation in many key biological processes, including photosynthetic electron transport, carbon fixation, and glycolysis. Moreover, the redox sensitivity along with the stoichiometric data enabled prediction of potential functional Cys-sites for proteins of interest. The functional significance of redox-sensitive Cys-sites in NADP-dependent glyceraldehyde-3-phosphate dehydrogenase, peroxiredoxin (AhpC/TSA family protein Sll1621), and glucose 6-phosphate dehydrogenase was further confirmed with site-specific mutagenesis and biochemical studies. Together, our findings provide significant insights into the broad redox regulation of photosynthetic organisms.

Integrative transcriptomic and proteomic analysis of osteocytic cells exposed to fluid flow reveals novel mechano-sensitive signaling pathways.

Osteocytes, positioned within bone׳s porous structure, are subject to interstitial fluid flow upon whole bone loading. Such fluid flow is widely theorized to be a mechanical signal transduced by osteocytes, initiating a poorly understood cascade of signaling events mediating bone adaptation to mechanical load. The objective of this study was to examine the time course of flow-induced changes in osteocyte gene transcript and protein levels using high-throughput approaches. Osteocyte-like MLO-Y4 cells were subjected to 2h of oscillating fluid flow (1Pa peak shear stress) and analyzed following 0, 2, 8, and 24h post-flow incubation. Transcriptomic microarray analysis, followed by gene ontology pathway analysis, demonstrated fluid flow regulation of genes consistent with both known and unknown metabolic and inflammatory responses in bone. Additionally, two of the more highly up-regulated gene products - chemokines Cxcl1 and Cxcl2, supported by qPCR - have not previously been reported as responsive to fluid flow. Proteomic analysis demonstrated greatest up-regulation of the ATP-producing enzyme NDK, calcium-binding Calcyclin, and G protein-coupled receptor kinase 6. Finally, an integrative pathway analysis merging fold changes in transcript and protein levels predicted signaling nodes not directly detected at the sampled time points, including transcription factors c-Myc, c-Jun, and RelA/NF-κB. These results extend our knowledge of the osteocytic response to fluid flow, most notably up-regulation of Cxcl1 and Cxcl2 as possible paracrine agents for osteoblastic and osteoclastic recruitment. Moreover, these results demonstrate the utility of integrative, high-throughput approaches in place of a traditional candidate approach for identifying novel mechano-sensitive signaling molecules.

A semiautomated framework for integrating expert knowledge into disease marker identification.

BACKGROUND: The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. OBJECTIVE: To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. METHODS: The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. RESULTS: The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. CONCLUSIONS: Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

Proteome and computational analyses reveal new insights into the mechanisms of hepatitis C virus-mediated liver disease posttransplantation.

UNLABELLED: Liver transplant tissues offer the unique opportunity to model the longitudinal protein abundance changes occurring during hepatitis C virus (HCV)-associated liver disease progression in vivo. In this study, our goal was to identify molecular signatures, and potential key regulatory proteins, representative of the processes influencing early progression to fibrosis. We performed global protein profiling analyses on 24 liver biopsy specimens obtained from 15 HCV(+) liver transplant recipients at 6 and/or 12 months posttransplantation. Differentially regulated proteins associated with early progression to fibrosis were identified by analysis of the area under the receiver operating characteristic curve. Analysis of serum metabolites was performed on samples obtained from an independent cohort of 60 HCV(+) liver transplant patients. Computational modeling approaches were applied to identify potential key regulatory proteins of liver fibrogenesis. Among 4,324 proteins identified, 250 exhibited significant differential regulation in patients with rapidly progressive fibrosis. Patients with rapid fibrosis progression exhibited enrichment in differentially regulated proteins associated with various immune, hepatoprotective, and fibrogenic processes. The observed increase in proinflammatory activity and impairment in antioxidant defenses suggests that patients who develop significant liver injury experience elevated oxidative stresses. This was supported by an independent study demonstrating the altered abundance of oxidative stress-associated serum metabolites in patients who develop severe liver injury. Computational modeling approaches further highlight a potentially important link between HCV-associated oxidative stress and epigenetic regulatory mechanisms impacting on liver fibrogenesis. CONCLUSION: Our proteome and metabolome analyses provide new insights into the role for increased oxidative stress in the rapid fibrosis progression observed in HCV(+) liver transplant recipients. These findings may prove useful in prognostic applications for predicting early progression to fibrosis.

Coupled transcriptome and proteome analysis of human lymphotropic tumor viruses: insights on the detection and discovery of viral genes.

BACKGROUND: Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) are related human tumor viruses that cause primary effusion lymphomas (PEL) and Burkitt's lymphomas (BL), respectively. Viral genes expressed in naturally-infected cancer cells contribute to disease pathogenesis; knowing which viral genes are expressed is critical in understanding how these viruses cause cancer. To evaluate the expression of viral genes, we used high-resolution separation and mass spectrometry coupled with custom tiling arrays to align the viral proteomes and transcriptomes of three PEL and two BL cell lines under latent and lytic culture conditions. RESULTS: The majority of viral genes were efficiently detected at the transcript and/or protein level on manipulating the viral life cycle. Overall the correlation of expressed viral proteins and transcripts was highly complementary in both validating and providing orthogonal data with latent/lytic viral gene expression. Our approach also identified novel viral genes in both KSHV and EBV, and extends viral genome annotation. Several previously uncharacterized genes were validated at both transcript and protein levels. CONCLUSIONS: This systems biology approach coupling proteome and transcriptome measurements provides a comprehensive view of viral gene expression that could not have been attained using each methodology independently. Detection of viral proteins in combination with viral transcripts is a potentially powerful method for establishing virus-disease relationships.

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors.

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.