Identification of Proteomic Biomarkers of Acetaminophen-Induced Hepatotoxicity Using Stable Isotope Labeling.

Quantitative proteomics approaches based on stable isotopic labeling and mass spectrometry have been widely applied to disease research, drug target discovery, biomarker identification, and systems biology. One of the notable stable isotopic labeling approaches is trypsin-catalyzed 18O/16O labeling, which has its own advantages of low sample consumption, simple labeling procedure, cost-effectiveness, and absence of chemical reactions that potentially generate by-products. In this chapter, a protocol for 18O/16O labeling-based quantitative proteomics approach is described with an application to the identification of proteomic biomarkers of acetaminophen (APAP)-induced hepatotoxicity in rats. The protocol involves first the extraction of proteins from liver tissues of control and APAP-treated rats and digestion into peptides by trypsin. After cleaning of the peptides by solid-phase extraction, equal amounts of peptides from the APAP treatment and the control groups are then subject to trypsin-catalyzed 18O/16O labeling. The labeled peptides are combined and fractionated by off-line strong cation exchange liquid chromatography (SCXLC), and each fraction is then analyzed by nanoflow reversed-phase LC coupled online with tandem mass spectrometry (RPLC-MS/MS) for identification and quantification of differential protein expression between APAP-treated rats and controls. The protocol is applicable to quantitative proteomic analysis for a variety of biological samples.

Phenotypic, genotypic and proteomic variations between poor and robust colonizing Campylobacter jejuni strains.

Campylobacter jejuni is one of the major causes of bacterial gastrointestinal disease in humans worldwide. This foodborne pathogen colonizes the intestinal tracts of chickens, and consumption of chicken and poultry products is identified as a common route of transmission. We analyzed two C. jejuni strains after oral challenge with 105 CFU/ml of C. jejuni per chick; one strain was a robust colonizer (A74/C) and the other a poor colonizer (A74/O). We also found extensive phenotypic differences in growth rate, biofilm production, and in vitro adherence, invasion, intracellular survival, and transcytosis. Strains A74/C and A74/O were genotypically similar with respect to their whole genome alignment, core genome, and ribosomal MLST, MLST, flaA, porA, and PFGE typing. The global proteomes of the two congenic strains were quantitatively analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and 618 and 453 proteins were identified from A74/C and A74/O isolates, respectively. Cluster of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that carbon metabolism and motility proteins were distinctively overexpressed in strain A74/C. The robust colonizer also exhibited a unique proteome profile characterized by significantly increased expression of proteins linked to adhesion, invasion, chemotaxis, energy, protein synthesis, heat shock proteins, iron regulation, two-component regulatory systems, and multidrug efflux pump. Our study underlines phenotypic, genotypic, and proteomic variations of the poor and robust colonizing C. jejuni strains, suggesting that several factors may contribute to mediating the different colonization potentials of the isogenic isolates.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics.

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management.

BACKGROUND: Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. OBJECTIVE: The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. KEY REPORTING PRACTICES: Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.

Microcalorimetric Investigations of Reversible Staphylococcal Enterotoxin Unfolding.

Staphylococcal food poisoning (SFP) is a common food-borne illness often associated with contamination during food handling. The genes for Staphylococcal enterotoxin (SE) isoforms SEA and SEB are frequently detected in human nasal Staphylococcus aureus isolates and these toxins are commonly associated with SFP. Past studies described the resistance of preformed SE proteins to heat inactivation and their reactivation upon cooling in foods. Full thermodynamic analyses for these processes have not been reported, however. The thermal stabilities of SEA, SEB, and SEH and reversibility of unfolding in simple buffers were investigated at pH 4.5 and pH 6.8 using differential scanning calorimetry (DSC). SEA and SEB unfolding was irreversible at pH 6.8 and at least partially reversible at pH 4.5 while SEH unfolding was irreversible at pH 4.5 and reversible at pH 6.8. Additional studies showed maximum refolding for SEB at pH 3.5-4.0 and diminished refolding at pH 4.5 with increasing ionic strength. SE-stimulated secretion of interferon-gamma by human peripheral blood mononuclear cells was used to assess residual SE biological activity following heat treatments using conditions matching those used for DSC studies. The biological activities of SEB and SEH exhibited greater resistance to heat inactivation than that of SEA. The residual activities of heat-treated SEB and SEH were measurable but diminished further in the presence of reconstituted nonfat dry milk adjusted to pH 4.5 or pH 6.8. To different extents, the pH and ionic strengths typical for foods influenced the thermal stabilities of SEA, SEB, and SEH and their potentials to renature spontaneously after heat treatments.

Mass Spectrometry-Based Proteomics for Biomarker Discovery.

Proteomics plays a pivotal role in systems medicine, in which pharmacoproteomics and toxicoproteomics have been developed to address questions related to efficacy and toxicity of drugs. Mass spectrometry is the core technology for quantitative proteomics, providing the capabilities of identification and quantitation of thousands of proteins. The technology has been applied to biomarker discovery and understanding the mechanisms of drug action. Both stable isotope labeling of proteins or peptides and label-free approaches have been incorporated with multidimensional LC separation and tandem mass spectrometry (LC-MS/MS) to increase the coverage and depth of proteome analysis. A protocol of such an approach exemplified by dimethyl labeling in combination with 2D-LC-MS/MS is described. With further development of novel proteomic tools and increase in sample throughput, the full spectrum of mass spectrometry-based proteomic research will greatly advance systems medicine.

Factors influencing career success of clinical nurses in northwestern China based on Kaleidoscope Career Model: Structural equation model.

AIM: To explore the relationships among self-efficacy, information literacy, social support and career success of clinical nurses and identify factors influencing clinical nurses' career success in northwestern China. BACKGROUND: Understanding the influencing factors of career success is important for the professional development of nurses and the improvement of clinical nursing quality. Many influencing factors of career success have been identified, but there is no large-scale research on the relationships among self-efficacy, information literacy, social support and career success of clinical nurses based on Kaleidoscope Career Model. Studies examining the association of the four factors remain limited. METHODS: A total of 3011 clinical nurses from 30 hospitals in northwestern China were selected in the cross-sectional survey, and the response rate was 94.71%. The clinical nurses completed the online self-report questionnaires including self-efficacy, information literacy, social support rating scale and career success scale. The data were analysed by SPSS23.0 statistical software using t test, analysis of variance, Pearson's correlation and multiple linear regression. Structural equation model (SEM) was used to analyse the influencing factors of career success using Mplus 8.3. RESULTS: The career success of clinical nurses in northwestern China was at a medium level. The linear multivariate regression analysis showed that self-efficacy (ß = .513), social support (ß = .230), information support (ß = .106), information consciousness (ß = -.097), information knowledge (ß = .067), information ethics (ß = -.053), hospital grade (ß = .118), marital status (ß = -.071) and age (ß = -.037) entered regression equation of clinical nurses' career success (all P < .05). SEM results showed that the career success was negatively correlated with demographic characteristics and positively correlated with social support and self-efficacy. CONCLUSION: Demographic characteristics, self-efficacy, social support and information literacy are the influencing factors of nurses' career success, which should be considered in the process of promoting nurses' career success. IMPLICATIONS FOR NURSING MANAGEMENT: Nursing managers need to acknowledge the significance of nurses' career success both for the realization of their own value and for the improvement of clinical nursing quality. They should encourage nurses to enhance self-efficacy and render more social support through incentive policies and foster nurses' information literacy through information technology training so as to improve their career success.

Discovery of Novel Proteomic Biomarkers for the Prediction of Kidney Recovery from Dialysis-Dependent AKI Patients.

BACKGROUND: AKI requiring dialysis (AKI-D) is associated with prolonged hospitalization, mortality, and progressive CKD among survivors. Previous studies have examined only select urine or serum biomarkers for predicting kidney recovery from AKI. METHODS: Serum samples collected on day 8 of randomized RRT from 72 patients enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were analyzed by the SOMAscan proteomic platform to profile 1305 proteins in each sample. Of these patients, 38 recovered kidney function and dialysis was discontinued, whereas another 34 patients remained on dialysis by day 28. RESULTS: Differential serum levels of 119 proteins, with 53 higher and 66 lower, were detected in samples from patients who discontinued dialysis, compared with patients who remained on dialysis by day 28. Patients were classified into tertiles on the basis of SOMAscan protein measurements for the 25 proteins most differentially expressed. The association of serum levels of each protein with kidney recovery was further evaluated using logistic regression analysis. Higher serum levels of CXCL11, CXCL2/CXCL3, CD86, Wnt-7a, BTK, c-Myc, TIMP-3, CCL5, ghrelin, PDGF-C, survivin, CA2, IL-9, EGF, and neuregulin-1, and lower levels of soluble CXCL16, IL1RL1, stanniocalcin-1, IL-6, and FGF23 when classified in tertiles were significantly associated with better kidney recovery. This significant association persisted for each of these proteins after adjusting for potential confounding risk factors including age, sex, cardiovascular SOFA score, congestive heart failure, diabetes, modality of intensive dialysis treatment, cause of AKI, baseline serum creatinine, day 8 urine volume, and estimated 60-day mortality risk. CONCLUSIONS: These results suggest concerted changes between survival-related proteins and immune-regulatory chemokines in regulating angiogenesis, endothelial and epithelial remodeling, and kidney cell regeneration, illustrating potential mechanisms of kidney recovery. Thus, this study identifies potential novel predictive biomarkers of kidney recovery in patients with AKI-D.

Serum metabolite profiles predict outcomes in critically ill patients receiving renal replacement therapy.

Acute kidney injury (AKI) requiring renal replacement therapy (RRT) is associated with increased incidence of dialysis dependence and portends high mortality in critically ill patients. At the early stage of RRT, serum metabolic biomarkers might differntiate patients with a high risk of mortality or permanent kidney injury from those who can recover. Serum samples from participants enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were collected on day 1 (n = 97) and day 8 (n = 105) of randomized RRT. The samples were further evaluated using LC/MS-based metabolic profiling. A model predicting mortality by day 8 was built from samples collected on day 1 and based on four metabolites with an area under the curve (AUC) of 0.641. A model most predictive of mortality by day 28 was built from the levels of 11 serum metabolites from day 8 with an AUC of 0.789. Both day 1 and day 8 samples had lower serum levels of 1-arachidonoyl-lysoPC and 1-eicosatetraenoyl-lysoPC (involved in anti-inflammatory processes) in the critically ill patients who died by day 8 or day 28. Higher levels of amino acids and amino acid metabolites in the day 8 model predicting < day 28 mortality may be indicative of muscle wasting. A kidney recovery biomarker panel based on the serum levels of three metabolites from day 8 samples with an AUC of 0.70 was devised. Serum metabolic profiling of AKI critically ill patients requiring RRT revealed predictive models of mortality based on observed differences in four serum metabolites at day 1 and 11 metabolites at day 8 which were predictive of mortality. Significant changes in the levels of these metabolites suggest links to inflammatory processes and/or muscle wasting.