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.

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.

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.

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.

Characterization of Phosphorylated Proteins Using Mass Spectrometry.

Phosphorylation is arguably the most important post-translational modification that occurs within proteins. Phosphorylation is used as a signal to control numerous physiological activities ranging from gene expression to metabolism. Identifying phosphorylation sites within proteins was historically a challenge as it required either radioisotope labeling or the use of phospho-specific antibodies. The advent of mass spectrometry (MS) has had a major impact on the ability to qualitatively and quantitatively characterize phosphorylated proteins. In this article, we describe MS methods for characterizing phosphorylation sites within individual proteins as well as entire proteome samples. The utility of these methods is illustrated in examples that show the information that can be gained using these MS techniques.

Stability of the Human Plasma Proteome to Pre-analytical Variability as Assessed by an Aptamer-Based Approach.

Variable processing and storage of whole blood and/or plasma are potential confounders in biomarker development and clinical assays. The goal of the study was to investigate how pre-analytical variables impact the human plasma proteome. Whole blood obtained from 16 apparently healthy individuals was collected in six EDTA tubes and processed randomly under six pre-analytical variable conditions including blood storage at 0 °C or RT for 6 h (B6h0C or B6hRT) before processing to plasma, plasma storage at 4 °C or RT for 24 h (P24h4C or P24hRT), low centrifugal force at 1300 × g, (Low×g), and immediate processing to plasma under 2500 × g (control) followed by plasma storage at -80 °C. An aptamer-based proteomic assay was performed to identify significantly changed proteins (fold change ≥1.2, P < 0.05, and false discovery rate < 0.05) relative to the control from a total of 1305 proteins assayed. Pre-analytical conditions Low×g and B6h0C resulted in the most plasma proteome changes with 200 and 148 proteins significantly changed, respectively. Only 36 proteins were changed under B6hRT. Conditions P24h4C and P24hRT yielded changes of 28 and 75 proteins, respectively. The complement system was activated in vitro under the conditions B6hRT, P24h4C, and P24hRT. The results suggest that particular pre-analytical variables should be controlled for clinical measurement of specific biomarkers.

An Integrated Analysis of Metabolites, Peptides, and Inflammation Biomarkers for Assessment of Preanalytical Variability of Human Plasma.

Discrepancies in blood sample collection and processing could have a significant impact on levels of metabolites, peptides, and protein biomarkers of inflammation in the blood; thus, sample quality control is critical for successful biomarker identification and validation. In this study, we analyzed the effects of several preanalytical processing conditions, including different storage times and temperatures for blood or plasma samples and different centrifugation forces on the levels of metabolites, peptides, and inflammation biomarkers in human plasma samples using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. Temperature was found to be the major factor for metabolite variation, and both time and temperature were identified as major factors for peptide variation. For inflammation biomarkers, temperature played different roles depending on the sample type (blood or plasma). Low temperature affected inflammation biomarkers in blood, while room temperature impacted inflammation biomarkers in plasma.

Aptamer-Based Proteomics Identifies Mortality-Associated Serum Biomarkers in Dialysis-Dependent AKI Patients.

INTRODUCTION: Currently, no effective therapies exist to reduce the high mortality associated with dialysis-dependent acute kidney injury (AKI-D). Serum biomarkers may be useful in understanding the pathophysiological processes involved with AKI and the severity of injury, and point to novel therapeutic targets. METHODS: Study day 1 serum samples from 100 patients and day 8 samples from 107 patients enrolled in the Veteran's Affairs/National Institutes of Health Acute Renal Failure Trial Network study were analyzed by the slow off-rate modified aptamers scan proteomic platform to profile 1305 proteins in each sample. Patients in each cohort were classified into tertiles based on baseline biomarker measurements. Cox regression analyses were performed to examine the relationships between serum levels of each biomarker and mortality. RESULTS: Changes in the serum levels of 54 proteins, 33 of which increased and 21 of which decreased, were detected when comparing samples of patients who died in the first 8 days versus patients who survived >8 days. Among the 33 proteins that increased, higher serum levels of fibroblast growth factor-23 (FGF23), tissue plasminogen activator (tPA), neutrophil collagenase (matrix metalloproteinase-8), and soluble urokinase plasminogen activator receptor, when stratified by tertiles, were associated with higher mortality. The association with mortality persisted for each of these proteins after adjusting for other potential risk factors, including age, sex, cardiovascular sequential organ failure assessment score, congestive heart failure, and presence of diabetes. Upper tertile levels of FGF23, tPA, and interleukin-6 on day 8 were associated with increased mortality; however, FGF23 barely lost significance after multivariable adjustment. CONCLUSIONS: Our results underscore an emerging proteomics tool capable of identifying low-abundance serum proteins important not only in the pathogenesis of AKI-D, but which is also helpful in discriminating AKI-D patients with high mortality.

Apoptosis of enterocytes and nitration of junctional complex proteins promote alcohol-induced gut leakiness and liver injury.

BACKGROUND & AIMS: Binge alcohol exposure causes gut leakiness, contributing to increased endotoxemia and inflammatory liver injury, although the molecular mechanisms are still elusive. This study was aimed at investigating the roles of apoptosis of enterocytes and nitration followed by degradation of intestinal tight junction (TJ) and adherens junction (AJ) proteins in binge alcohol-induced gut leakiness. METHODS: The levels of intestinal (ileum) junctional complex proteins, oxidative stress markers and apoptosis-related proteins in rodents, T84 colonic cells and autopsied human ileums were determined by immunoblot, immunoprecipitation, immunofluorescence, and mass-spectral analyses. RESULTS: Binge alcohol exposure caused apoptosis of gut enterocytes with elevated serum endotoxin and liver injury. The levels of intestinal CYP2E1, iNOS, nitrated proteins and apoptosis-related marker proteins were significantly elevated in binge alcohol-exposed rodents. Differential, quantitative mass-spectral analyses of the TJ-enriched fractions of intestinal epithelial layers revealed that several TJ, AJ and desmosome proteins were decreased in binge alcohol-exposed rats compared to controls. Consistently, the levels of TJ proteins (claudin-1, claudin-4, occludin and zonula occludens-1), AJ proteins (ß-catenin and E-cadherin) and desmosome plakoglobin were very low in binge alcohol-exposed rats, wild-type mice, and autopsied human ileums but not in Cyp2e1-null mice. Additionally, pretreatment with specific inhibitors of CYP2E1 and iNOS prevented disorganization and/or degradation of TJ proteins in alcohol-exposed T84 colonic cells. Furthermore, immunoprecipitation followed by immunoblot confirmed that intestinal TJ and AJ proteins were nitrated and degraded via ubiquitin-dependent proteolysis, resulting in their decreased levels. CONCLUSIONS: These results demonstrated for the first time the critical roles of CYP2E1, apoptosis of enterocytes, and nitration followed by ubiquitin-dependent proteolytic degradation of the junctional complex proteins, in promoting binge alcohol-induced gut leakiness and endotoxemia, contributing to inflammatory liver disease. LAY SUMMARY: Binge alcohol exposure causes gut leakiness, contributing to increased endotoxemia and inflammatory liver injury. Our results demonstrated for the first time the critical roles of apoptosis of enterocytes and nitration followed by ubiquitin-dependent proteolytic degradation of the junctional complex proteins in promoting this gut leakiness and endotoxemia. These results provide insight into the molecular mechanisms of alcohol-induced inflammatory liver disease.

Immune response proteins as predictive biomarkers of doxorubicin-induced cardiotoxicity in breast cancer patients.

Cancer treatment with doxorubicin (DOX) can induce cumulative dose-dependent cardiotoxicity. Currently, there are no specific biomarkers that can identify patients at risk during the initial doses of chemotherapy. The aim of this study was to examine plasma cytokines/chemokines and potential cardiovascular biomarkers for the prediction of DOX-induced cardiotoxicity. Plasma samples were collected before (T0), and after the first (T1) and the second (T2) cycles of DOX-based chemotherapy of 27 breast cancer patients, including five patients who presented with >10% decline of left ventricular ejection fraction (LVEF), five patients with LVEF decline of 5-10%, and 17 patients who maintained normal LVEF at the end of chemotherapy (240 mg/m2 cumulative dose of DOX from four cycles of treatment). Multiplex immunoassays were used to screen plasma samples for 40 distinct chemokines, nine matrix metalloproteinases, 33 potential markers of cardiovascular diseases, and the fourth-generation cardiac troponin T assay. The results showed that the patients with abnormal decline of LVEF (>10%) had lower levels of CXCL6 and sICAM-1 and higher levels of CCL23 and CCL27 at T0; higher levels of CCL23 and lower levels of CXCL5, CCL26, CXCL6, GM-CSF, CXCL1, IFN-γ, IL-2, IL-8, CXCL11, CXCL9, CCL17, and CCL25 at T1; and higher levels of MIF and CCL23 at T2 than the patients who maintained normal LVEF. Patients with LVEF decline of 5-10% had lower plasma levels of CXCL1, CCL3, GDF-15, and haptoglobin at T0; lower levels of IL-16, FABP3, and myoglobin at T1; and lower levels of myoglobin and CCL23 at T2 as compared to the patients who maintained normal LVEF. This pilot study identified potential biomarkers that may help predict which patients are vulnerable to DOX-induced cardiotoxicity although further validation is needed in a larger cohort of patients. Impact statement Drug-induced cardiotoxicity is one of the major concerns in drug development and clinical practice. It is critical to detect potential cardiotoxicity early before onset of symptomatic cardiac dysfunction or heart failure. Currently there are no qualified clinical biomarkers for the prediction of cardiotoxicity caused by cancer treatment such as doxorubicin (DOX). By using multiplex immunoassays, we identified proteins with significantly changed plasma levels in a group of breast cancer patients who were treated with DOX-based chemotherapy and produced cardiotoxicity. These proteins were associated with immune response and were identified before DOX treatment or at early doses of treatment, thus they could be potential predictive biomarkers of cardiotoxicity although further validation is required to warrant their clinical values.

MicroRNA hsa-miR-370-3p suppresses the expression and induction of CYP2D6 by facilitating mRNA degradation.

Cytochrome P450 2D6 (CYP2D6) participates in the metabolism of approximately 20-25% of prescribed drugs. Genetic polymorphisms influence the expression and/or activity of CYP2D6, and inter-individual differences in drug activation and elimination caused by CYP2D6 genetic variants were reported. However, little is known about the potential modulation of CYP2D6 expression by microRNAs (miRNAs). In the current study, by using in silico prediction of the stabilities of miRNA/mRNA complexes, we screened 38 miRNA candidates that may interact with the transcript of CYP2D6. An inverse correlation between the expression of miRNA hsa-miR-370-3p and the expression of CYP2D6 was observed in human liver tissue samples. Electrophoretic mobility shift assays confirmed that hsa-miR-370-3p was able to directly bind to its cognate target within the coding region of the CYP2D6 transcript. The transfection of hsa-miR-370-3p mimics into the HepG2CYP2D6 cell line, a genetically modified cell line that overexpresses exogenous CYP2D6, was able to suppress the expression of CYP2D6 significantly at both mRNA and protein levels. The transfection of hsa-miR-370-3p mimics was also able to inhibit endogenous mRNA expression and/or protein production of CYP2D6 in HepaRG cells. Furthermore, in HepaRG, HepG2, and Huh7 cells, dexamethasone-induced expression of CYP2D6 was inhibited by hsa-miR-370-3p mimics. To investigate whether the miRNA mediated suppression is caused by inhibiting protein translation or promoting mRNA degradation, an actinomycin D assay was used to measure the stability of CYP2D6 transcripts. The results indicated that hsa-miR-370-3p mimics facilitated significantly the degradation of CYP2D6 mRNA. In addition, proteomics analyses of proteins isolated from the miRNA/mRNA/protein complex suggested that a group of multifunctional proteins facilitated the interaction between hsa-miR-370-3p and CYP2D6, thereby promoting mRNA degradation.

CHD4 Has Oncogenic Functions in Initiating and Maintaining Epigenetic Suppression of Multiple Tumor Suppressor Genes.

An oncogenic role for CHD4, a NuRD component, is defined for initiating and supporting tumor suppressor gene (TSG) silencing in human colorectal cancer. CHD4 recruits repressive chromatin proteins to sites of DNA damage repair, including DNA methyltransferases where it imposes de novo DNA methylation. At TSGs, CHD4 retention helps maintain DNA hypermethylation-associated transcriptional silencing. CHD4 is recruited by the excision repair protein OGG1 for oxidative damage to interact with the damage-induced base 8-hydroxydeoxyguanosine (8-OHdG), while ZMYND8 recruits it to double-strand breaks. CHD4 knockdown activates silenced TSGs, revealing their role for blunting colorectal cancer cell proliferation, invasion, and metastases. High CHD4 and 8-OHdG levels plus low expression of TSGs strongly correlates with early disease recurrence and decreased overall survival.

A systematic evaluation of microRNAs in regulating human hepatic CYP2E1.

Cytochrome P450 2E1 (CYP2E1) is an important drug metabolizing enzyme for processing numerous xenobiotics in the liver, including acetaminophen and ethanol. Previous studies have shown that microRNAs (miRNAs) can suppress CYP2E1 expression by binding to the 3'-untranslated region (3'-UTR) of its transcript. However, a systematic analysis of CYP2E1 regulation by miRNAs has not been described. Here, we applied in silico, in vivo, and in vitro approaches to investigate miRNAs involved in the regulation of CYP2E1. Initially, potential miRNA binding sites in the CYP2E1 mRNA transcript were identified and screened using in silico methods. Next, inverse correlations were found in human liver samples between the expression of CYP2E1 mRNA and the levels of two miRNA species, hsa-miR-214-3p and hsa-miR-942-5p. In a HepG2-derived CYP2E1 over-expression cell model, hsa-miR-214-3p exhibited strong suppression of CYP2E1 expression by targeting the coding region of its mRNA transcript, but hsa-miR-942-5p did not inhibit CYP2E1 levels. Electrophoretic mobility shift assays confirmed that hsa-miR-214-3p recruited other cellular protein factors to form stable complexes with specific sequences present in the CYP2E1 mRNA open reading frame. Transfection of HepaRG cells with hsa-miR-214-3p mimics inhibited expression of the endogenous CYP2E1 gene. Further, hsa-miR-214-3p mimics partially blocked ethanol-dependent increases in CYP2E1 mRNA and protein levels in HepG2 cells and they reduced the release of alanine aminotransferase from CYP2E1-overexpressing HepG2 cells exposed to acetaminophen. These results substantiate the suppressing effect of hsa-miR-214-3p on CYP2E1 expression.

Transforming Growth Factor-ß (TGF-ß) Directly Activates the JAK1-STAT3 Axis to Induce Hepatic Fibrosis in Coordination with the SMAD Pathway.

Transforming growth factor-ß (TGF-ß) signals through both SMAD and non-SMAD pathways to elicit a wide array of biological effects. Existing data have shown the association and coordination between STATs and SMADs in mediating TGF-ß functions in hepatic cells, but it is not clear how STATs are activated under these circumstances. Here, we report that JAK1 is a constitutive TGFßRI binding protein and is absolutely required for phosphorylation of STATs in a SMAD-independent manner within minutes of TGF-ß stimulation. Following the activation of SMADs, TGF-ß also induces a second phase of STAT phosphorylation that requires SMADs, de novo protein synthesis, and contribution from JAK1. Our global gene expression profiling indicates that the non-SMAD JAK1/STAT pathway is essential for the expression of a subset of TGF-ß target genes in hepatic stellate cells, and the cooperation between the JAK1-STAT3 and SMAD pathways is critical to the roles of TGF-ß in liver fibrosis.