Glycocapture-assisted global quantitative proteomics (gagQP) reveals multiorgan responses in serum toxicoproteome

The rapid high-throughput screening of ω-transaminases via a colorimetric method using aliphatic α-diketones as amino acceptors

ω-Transaminases (ω-TAs) are widely available for the production of chiral amines and unnatural amino acids. Herein, a rapid spectrophotometric method was developed for screening ω-TAs based on the colored products that can be generated from transamination reactions between aliphatic α-diketones and amino donors catalyzed by ω-TAs. The possible mechanism of the formation of the colored product was investigated according to LC-Q-TOF-MS analysis. Among seven diketones, 2,3-butanedione was selected as the most suitable amino acceptor for colorimetric screening of ω-TAs with high efficiency, high sensitivity, and low background interference. Meanwhile, the absorbance of the colored product generated by 2,3-butanedione catalyzed by ω-TAs in this method was linearly correlated with the results by HPLC analysis. This method was also confirmed to effectively screen ω-TA mutants with high activity towards isopropylamine.

A mouse monoclonal antibody against influenza C virus attenuates acetaminophen-induced liver injury in mice

Molecular mimicry is one of the main processes for producing autoantibodies during infections. Although some autoantibodies are associated with autoimmune diseases, the functions of many autoantibodies remain unknown. Previously, we reported that S16, a mouse (BALB/c) monoclonal antibody against the hemagglutinin-esterase fusion glycoprotein of influenza C virus, recognizes host proteins in some species of animals, but we could not succeed in identifying the proteins. In the present study, we found that S16 cross-reacted with acetyl-CoA acyltransferase 2 (ACAA2), which is expressed in the livers of BALB/c mice. ACAA2 was released into the serum after acetaminophen (APAP) administration, and its serum level correlated with serum alanine aminotransferase (ALT) activity. Furthermore, we observed that S16 injected into mice with APAP-induced hepatic injury prompted the formation of an immune complex between S16 and ACAA2 in the serum. The levels of serum ALT (p < 0.01) and necrotic areas in the liver (p < 0.01) were reduced in the S16-injected mice. These results suggest that S16 may have a mitigation function in response to APAP-induced hepatotoxicity. This study shows the therapeutic function of an autoantibody and suggests that an antibody against extracellular ACAA2 might be a candidate for treating APAP-induced hepatic injury.

Comparative proteomic profiling of the serum differentiates pancreatic cancer from chronic pancreatitis

Finland ranks sixth among the countries having highest incidence rate of pancreatic cancer with mortality roughly equaling incidence. The average age of diagnosis for pancreatic cancer is 69 years in Nordic males, whereas the average age of diagnosis of chronic pancreatitis is 40–50 years, however, many cases overlap in age. By radiology, the evaluation of a pancreatic mass, that is, the differential diagnosis between chronic pancreatitis and pancreatic cancer is often difficult. Preoperative needle biopsies are difficult to obtain and are demanding to interpret. New blood based biomarkers are needed. The accuracy of the only established biomarker for pancreatic cancer, CA 19‐9 is rather poor in differentiating between benign and malignant mass of the pancreas. In this study, we have performed mass spectrometry analysis (High Definition MS^E) of serum samples from patients with chronic pancreatitis (13) and pancreatic cancer (22). We have quantified 291 proteins and performed detailed statistical analysis such as principal component analysis, orthogonal partial least square discriminant analysis and receiver operating curve analysis. The proteomic signature of chronic pancreatitis versus pancreatic cancer samples was able to separate the two groups by multiple statistical techniques. Some of the enriched pathways in the proteomic dataset were LXR/RXR activation, complement and coagulation systems and inflammatory response. We propose that multiple high‐confidence biomarker candidates in our pilot study including Inter‐alpha‐trypsin inhibitor heavy chain H2 (Area under the curve, AUC: 0.947), protein AMBP (AUC: 0.951) and prothrombin (AUC: 0.917), which should be further evaluated in larger patient series as potential new biomarkers for differential diagnosis.

Proteomic analysis of acetaminophen-induced hepatotoxicity and identification of heme oxygenase 1 as a potential plasma biomarker of liver injury

PURPOSE

Overdose of acetaminophen (APAP) is a major cause of acute liver failure. This study was aimed to identify pathways related to hepatotoxicity and potential biomarkers of liver injury.

EXPERIMENTAL DESIGN

Rats were treated with low (100 mg/kg) and high (1250 mg/kg) doses of APAP, and liver tissues at 6 and 24 h post-treatment were analyzed using a proteomic approach of 16O/18O labeling and 2D-LC-MS/MS.

RESULTS

Molecular pathways evolved progressively from scattered and less significant perturbations to more focused and significant alterations in a dose- and time-dependent manner upon APAP treatment. Imbalanced expression of hemeoxygenase 1 (HMOX1) and biliverdin reductase A (BLVRA) was associated with hepatotoxicity. Protein abundance changes of a total of 31 proteins were uniquely correlated to liver damage, among which a dramatic increase of HMOX1 levels in plasma was observed. Liver injury-associated significant elevation of plasma HMOX1 was further validated in mice treated with APAP.

CONCLUSIONS AND CLINICAL RELEVANCE

This study unveiled molecular changes associated with APAP-induced liver toxicity at the pathway levels and identified HMOX1 as a potential plasma biomarker of liver injury.

Identification of Organ-Enriched Protein Biomarkers of Acute Liver Injury by Targeted Quantitative Proteomics of Blood in Acetaminophen- and Carbon-Tetrachloride-Treated Mouse Models and Acetaminophen Overdose Patients

Organ-enriched blood proteins, those produced primarily in one organ and secreted or exported to the blood, potentially afford a powerful and specific approach to assessing diseases in their cognate organs. We demonstrate that quantification of organ-enriched proteins in the blood offers a new strategy to find biomarkers for diagnosis and assessment of drug-induced liver injury (and presumably the assessment of other liver diseases). We used selected reaction monitoring (SRM) mass spectrometry to quantify 81 liver-enriched proteins plus three aminotransferases (ALT1, AST1, and AST2) in plasma of C57BL/6J and NOD/ShiLtJ mice exposed to acetaminophen or carbon tetrachloride. Plasma concentrations of 49 liver-enriched proteins were perturbed significantly in response to liver injury induced by one or both toxins. We validated four of these toxin-responsive proteins (ALDOB, ASS1, BHMT, and GLUD1) by Western blotting. By both assays, these four proteins constitute liver injury markers superior to currently employed markers such as ALT and AST. A similar approach was also successful in human serum where we had analyzed 66 liver-enriched proteins in acetaminophen overdose patients. Of these, 23 proteins were elevated in patients; 15 of 23 overlapped with the concentration-increased proteins in the mouse study. A combination of 5 human proteins, AGXT, ALDOB, CRP, FBP1, and MMP9, provides the best diagnostic performance to distinguish acetaminophen overdose patients from controls (sensitivity: 0.85, specificity: 0.84, accuracy: 85%). These five blood proteins are candidates for detecting acetaminophen-induced liver injury using next-generation diagnostic devices (e.g, microfluidic ELISA assays).

Circulating mitochondrial biomarkers for drug-induced liver injury

Liver mitochondria affected by drugs can be released into circulation and serve as biomarkers for drug-induced liver injury (DILI). The tissue specificity of ALT was improved by differentiating cytosolic ALT1 and mitochondrial ALT2 isoforms released in circulation. Prior to ALT elevation, mitochondrial cytochrome c, OCT, GLDH, CPS1 and DNA were increased in circulation following DILI. The baseline expression of mt-Nd6 was predictive of individual DILI susceptibility in animals. As mitochondrial DILI biomarkers appeared to be drug or species dependent, they might have value in clinical scenarios when culprit drugs are established, but may not be ideal tools to assess DILI potentials of new drugs.

Proteomic investigation of signatures for geniposide-induced hepatotoxicity

Evaluating the safety of traditional medicinal herbs and their major active constituents is critical for their widespread usage. Geniposide, a major active constituent with a defined structure from the traditional medicinal herb Gardenia jasminoides ELLIS fruit, exhibits remarkable anti-inflammatory, antiapoptotic, and antifibrotic properties and has been used in a variety of medical fields, mainly for the treatment of liver diseases. However, geniposide-induced hepatotoxicity and methods for the early detection of hepatotoxicity have yet to be reported. In this study, geniposide-induced hepatotoxicity was investigated. In addition, candidate biomarkers for the earlier detection of geniposide-induced hepatotoxicity were identified using a label-free quantitative proteomics approach on a geniposide overdose-induced liver injury in a rat model. Using an accurate intensity-based, absolute quantification (iBAQ)-based, one-step discovery and verification approach, a candidate biomarker panel was easily obtained from individual samples in response to different conditions. To determine the biomarkers' early detection abilities, five candidate biomarkers were selected and tested using enzyme-linked immunosorbent assays (ELISAs). Two biomarkers, glycine N-methyltransferase (GNMT) and glycogen phosphorylase (PYGL), were found to indicate hepatic injuries significantly earlier than the current gold standard liver biomarker. This study provides a first insight into geniposide-induced hepatotoxicity in a rat model and describes a method for the earlier detection of this hepatotoxicity, facilitating the efficient monitoring of drug-induced hepatotoxicity.

Differential analysis of N-glycoproteome between hepatocellular carcinoma and normal human liver tissues by combination of multiple protease digestion and solid phase based labeling

Background

Dysregulation of glycoproteins is closely related with many diseases. Quantitative proteomics methods are powerful tools for the detection of glycoprotein alterations. However, in almost all quantitative glycoproteomics studies, trypsin is used as the only protease to digest proteins. This conventional method is unable to quantify N-glycosites in very short or long tryptic peptides and so comprehensive glycoproteomics analysis cannot be achieved.

Methods

In this study, a comprehensive analysis of the difference of N-glycoproteome between hepatocellular carcinoma (HCC) and normal human liver tissues was performed by an integrated workflow combining the multiple protease digestion and solid phase based labeling. The quantified N-glycoproteins were analyzed by GoMiner to obtain a comparative view of cellular component, biological process and molecular function.

Results/conclusions

An integrated workflow was developed which enabled the processes of glycoprotein coupling, protease digestion and stable isotope labeling to be performed in one reaction vessel. This workflow was firstly evaluated by analyzing two aliquots of the same protein extract from normal human liver tissue. It was demonstrated that the multiple protease digestion improved the glycoproteome coverage and the quantification accuracy. This workflow was further applied to the differential analysis of N-glycoproteome of normal human liver tissue and that with hepatocellular carcinoma. A total of 2,329 N-glycosites on 1,052 N-glycoproteins were quantified. Among them, 858 N-glycosites were quantified from more than one digestion strategy with over 99% confidence and 1,104 N-glycosites were quantified from only one digestion strategy with over 95% confidence. By comparing the GoMiner results of the N-glycoproteins with and without significant changes, the percentage of membrane and secreted proteins and their featured biological processes were found to be significant different revealing that protein glycosylation may play the vital role in the development of HCC.

Glycopeptide capture for cell surface proteomics

Cell surface proteins, including extracellular matrix proteins, participate in all major cellular processes and functions, such as growth, differentiation, and proliferation. A comprehensive characterization of these proteins provides rich information for biomarker discovery, cell-type identification, and drug-target selection, as well as helping to advance our understanding of cellular biology and physiology. Surface proteins, however, pose significant analytical challenges, because of their inherently low abundance, high hydrophobicity, and heavy post-translational modifications. Taking advantage of the prevalent glycosylation on surface proteins, we introduce here a high-throughput glycopeptide-capture approach that integrates the advantages of several existing N-glycoproteomics means. Our method can enrich the glycopeptides derived from surface proteins and remove their glycans for facile proteomics using LC-MS. The resolved N-glycoproteome comprises the information of protein identity and quantity as well as their sites of glycosylation. This method has been applied to a series of studies in areas including cancer, stem cells, and drug toxicity. The limitation of the method lies in the low abundance of surface membrane proteins, such that a relatively large quantity of samples is required for this analysis compared to studies centered on cytosolic proteins.

Protein-centric N-glycoproteomics analysis of membrane and plasma membrane proteins

The advent of proteomics technology has transformed our understanding of biological membranes. The challenges for studying membrane proteins have inspired the development of many analytical and bioanalytical tools, and the techniques of glycoproteomics have emerged as an effective means to enrich and characterize membrane and plasma-membrane proteomes. This Review summarizes the development of various glycoproteomics techniques to overcome the hurdles formed by the unique structures and behaviors of membrane proteins with a focus on N-glycoproteomics. Example contributions of N-glycoproteomics to the understanding of membrane biology are provided, and the areas that require future technical breakthroughs are discussed.

Systems biology and p4 medicine: past, present, and future

Studying complex biological systems in a holistic rather than a "one gene or one protein" at a time approach requires the concerted effort of scientists from a wide variety of disciplines. The Institute for Systems Biology (ISB) has seamlessly integrated these disparate fields to create a cross-disciplinary platform and culture in which "biology drives technology drives computation." To achieve this platform/culture, it has been necessary for cross-disciplinary ISB scientists to learn one another's languages and work together effectively in teams. The focus of this "systems" approach on disease has led to a discipline denoted systems medicine. The advent of technological breakthroughs in the fields of genomics, proteomics, and, indeed, the other "omics" is catalyzing striking advances in systems medicine that have and are transforming diagnostic and therapeutic strategies. Systems medicine has united genomics and genetics through family genomics to more readily identify disease genes. It has made blood a window into health and disease. It is leading to the stratification of diseases (division into discrete subtypes) for proper impedance match against drugs and the stratification of patients into subgroups that respond to environmental challenges in a similar manner (e.g. response to drugs, response to toxins, etc.). The convergence of patient-activated social networks, big data and their analytics, and systems medicine has led to a P4 medicine that is predictive, preventive, personalized, and participatory. Medicine will focus on each individual. It will become proactive in nature. It will increasingly focus on wellness rather than disease. For example, in 10 years each patient will be surrounded by a virtual cloud of billions of data points, and we will have the tools to reduce this enormous data dimensionality into simple hypotheses about how to optimize wellness and avoid disease for each individual. P4 medicine will be able to detect and treat perturbations in healthy individuals long before disease symptoms appear, thus optimizing the wellness of individuals and avoiding disease. P4 medicine will 1) improve health care, 2) reduce the cost of health care, and 3) stimulate innovation and new company creation. Health care is not the only subject that can benefit from such integrative, cross-disciplinary, and systems-driven platforms and cultures. Many other challenges plaguing our planet, such as energy, environment, nutrition, and agriculture can be transformed by using such an integrated and systems-driven approach.