The enhancement effect of small molecule Lyb24 reveals AzoR as a novel target of polymyxin B.

Given the important role of polymyxin B (PB) in the treatment of drug-resistant Gram-negative bacterial infections, the emergence of PB resistance poses a serious threat to public health. Adjuvant development is a supplementary strategy that can compensate for the lack of novel antibiotics by protecting PB. In this study, we found a small molecule named Lyb24 that showed weak antibacterial activity (minimum inhibitory concentration ≥ 10 µg/ml) but potentiated and revitalized the efficacy of PB against Gram-negative pathogens, including mcr-1- and mgrB-deletion-mediated PB-resistant strains. Our results showed that Lyb24 inhibits the translational levels of genes associated with the modification of lipid A. In addition, Lyb24 increases the permeability, disrupts the integrity and induces the depolarization of the membrane. We further found that both Lyb24 and PB could directly bind to AzoR and inhibit its activity. Structural analysis showed that Lyb24 binds to the isoalloxazine ring of flavin mononucleotide (FMN) through pi-pi stacking and loop η4 of AzoR. A pneumonia model was used to confirm that the activity against clinical PB-resistant Klebsiella pneumoniae was enhanced due to Lyb24 on PB. In conclusion, we provide a potential therapeutic regimen by combining Lyb24 and PB to treat Gram-negative-resistant bacterial infections. Our findings not only explain the synergistic effect of Lyb24, but also expand our knowledge on the mechanism of action of PB.

Establishment and clinical application evaluations of a deep mining strategy of plasma proteomics based on nanomaterial protein coronas.

Research on plasma proteomics has received extensive attention, because human plasma is an important sample for disease biomarker research due to its easy clinical accessibility and richness in biological information. Plasma samples contain a large number of leaked proteins from different tissues in the body, immune proteins and communication signal proteins. However, MS signal suppression from high-abundance proteins results in a large number of proteins that are present in low abundance in plasma not being detected by the LC-MS method. This situation makes it more difficult to study neurological diseases, where tissue sampling is difficult and body fluid samples such as plasma or cerebrospinal fluid are both affected by signal suppression. A large number of methods have been developed to deeply mine plasma proteomics information; however, their application limitations remain to some extent. Traditional immuno- or affinity-based depletion, fractionation and subproteome enrichment methods cannot meet the challenges of large clinical cohort applications due to limited time efficiency. In this study, a deep mining strategy of plasma proteomics was established by combing the protein corona formed by deep mining beads (DMB beads, hereafter referred to as magnetic covalent organic frameworks Fe3O4@TpPa-1), DIA-MS detection and the DIA-NN library searching method. By optimizing the enrichment step, mass spectrometry acquisition and data processing, the evaluation results of the deep mining strategy showed the following: depth, the strategy identified and quantified results of 2000+ proteins per plasma sample; stability, more than 87% of the enriched low-abundance proteins had CV < 20%; accuracy, good agreement between measured and theoretical values (1.81/2, 8.68/10, 38.36/50) for the gradient addition of E. coli proteins to a plasma sample; time efficiency, the processing time was reduced from >12h in the traditional method to <5h (incubation 30 min, washing 15 min, reductive/alkylation/digestion/desalting 4 h), and more importantly, 96 samples can be processed simultaneously in combination with the magnetic module of the automated device. The optimal strategy enables greater enrichment of neurological disease-related proteins, including SNCA and BDNF. Finally, the deep mining strategy was applied in a pilot study of multiple system atrophy (MSA) for biomarker discovery. The results showed that a total of 215 proteins were upregulated and 184 proteins were downregulated (p < 0.05) in the MSA group compared with the healthy control group. Eighteen proteins of these differentially expressed proteins were reported to be associated with neurological diseases or expressed specifically in brain tissue, 8 and 4 of which have reference concentrations of µg/L and ng/L, respectively. The alterations of ENPP2 and SLC2A1/Glut1 were reanalyzed by ELISA, further supporting the results of mass spectrometry. In conclusion, the results of the evaluation and application of the deep mining strategy showed promise for clinical research applications.

Resequencing of 558 Chinese mungbean landraces identifies genetic loci associated with key agronomic traits.

Mungbean is a warm-season annual food legume and plays important role in supplying food and nutritional security in many tropical countries. However, the genetic basis of its agronomic traits remains poorly understood. Therefore, we resequenced 558 Chinese mungbean landraces and produced a comprehensive map of mungbean genomic variation. We phenotyped all landraces in six different environments. Genome-wide association studies (GWAS) produced 110 signals significantly associated with nine agronomic traits, for which several candidate genes were identified. Overall, this study provides new insight into the genetic architecture of mungbean agronomic traits. Moreover, the genome-wide variations identified here should be valuable resources for future breeding studies of this important food legume.

Longitudinal Serum Proteome Characterization of COVID-19 Patients With Different Severities Revealed Potential Therapeutic Strategies.

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation.

Identification of noninvasive diagnostic biomarkers for hepatocellular carcinoma by urinary proteomics.

Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality worldwide, and third in China. Hepatitis B virus (HBV) infection is a main risk factor for HCC in China, and the early diagnosis of HCC in high-risk population is very important. However, the commonly used diagnostic biomarker alpha-fetoprotein has limitations in clinical practice. In order to identify reliable and noninvasive HCC urinary biomarkers, a high-throughput proteomics streamline was applied in the analysis of urine samples from 74 HCC and 82 high-risk patients with chronic HBV infected liver diseases. Candidate diagnostic markers were screened by feature selection algorithm, and were combined with random forest or simple voting algorithms in the training dataset. Then the multiple feature models were validated in an independent test dataset. The selected features were further verified by Multiple Reaction Monitoring (MRM) in another independent dataset. By integrating 7 features screened in the discovery phase, random forest model achieved AUC of 0.92 and 0.87 in training and test datasets, respectively, while voting model performed better with AUC of 0.94 and 0.90, respectively. In the MRM dataset, the 7 features were targeted quantified, and voting model integrating the 7 features achieved AUC of 0.95. Our work highlights the potential of noninvasive urinary protein biomarkers in HCC diagnosis with high-risk population, which will be beneficial to HCC auxiliary diagnosis and HCC surveillance. SIGNIFICANCE: A high throughput urinary proteome analysis platform was committed into the discovery of noninvasive HCC biomarkers in high-risk patients with chronic HBV infected liver diseases. The combination of 7 urinary features achieved good performance in distinguishing HCC from high-risk population. The expression of the 7 features was validated by targeted MRM, and the integration of the features also worked well in the MRM dataset. This is the first time that urinary proteomic strategy was applied in discovering HCC biomarkers from high-risk population. This result will be helpful for HCC auxiliary diagnosis and surveillance in a noninvasive way.

In-depth proteomic analysis of tissue interstitial fluid for hepatocellular carcinoma serum biomarker discovery.

BACKGROUND: Hepatocellular carcinoma (HCC) is a primary malignancy of the liver. New serum biomarkers for HCC screening are needed, especially for alpha-fetoprotein (AFP) negative patients. As a proximal fluid between body fluids and intracellular fluid, tissue interstitial fluid (TIF) is a suitable source for serum biomarker discovery. METHODS: Sixteen paired TIF samples from HCC tumour and adjacent non-tumour tissues were analysed by isobaric tags for relative and absolute quantitation (iTRAQ) method. Two proteins were selected for ELISA validation in serum samples. RESULTS: Totally, 3629 proteins were identified and 3357 proteins were quantified in TIF samples. Among them, 232 proteins were significantly upregulated in HCC-TIF and 257 proteins down-regulated. Two overexpressed extracellular matrix proteins, SPARC and thrombospondin-2 (THBS2) were selected for further validation. ELISA result showed that the serum levels of SPARC and THBS2 in HCC patients were both significantly higher than those in healthy controls. The combination of serum SPARC and THBS2 could distinguish HCC (AUC=0.97, sensitivity=86%, specificity=100%) or AFP-negative HCC (AUC=0.95, sensitivity=91%, specificity=93%) from healthy controls. And the combination of serum SPARC and THBS2 could also distinguish HCC patients from benign liver disease patients (AUC=0.93, sensitivity=80%, specificity=94%). In addition, serum THBS2 was found to be a novel independent indicator for poor prognosis of HCC. CONCLUSIONS: Novel HCC candidate serum markers were found through in-depth proteomic analysis of TIF, which demonstrated the successful utility of TIF in cancer serum biomarker discovery.

Quantitative Proteomics Analysis of Tissue Interstitial Fluid for Identification of Novel Serum Candidate Diagnostic Marker for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the fifth most common malignant cancer in the world. The sensitivity of alpha-fetoprotein (AFP) is still inadequate for HCC diagnosis. Tissue interstitial fluid (TIF), as the liquid microenvironment of cancer cells, was used for biomarker discovery in this study. Paired tumor and nontumor TIF samples from 6 HBV-HCC patients were analyzed by a proteomic technique named iTRAQ (isobaric tag for relative and absolute quantitation). Totally, 241 up-regulated proteins (ratio ≥ 1.3, p < 0.05) and 288 down-regulated proteins (ratio ≤ -1.3, p < 0.05) in tumor TIF were identified. Interestingly, proteins in S100 family were found remarkably up-regulated in tumor TIF. One dramatically up-regulated protein S100A9 (ratio = 19) was further validated by ELISA in sera from liver cirrhosis (LC, HCC high risk population) and HCC patients (n = 47 for each group). The level of this protein was significantly elevated in HCC sera compared with LC (p < 0.0001). The area under the curve of this protein to distinguish HCC from LC was 0.83, with sensitivity of 91% (higher than AFP) and specificity of 66%. This result demonstrated the potential of S100A9 as a candidate HCC diagnostic biomarker. And TIF was a kind of promising material to identify candidate tumor biomarkers that could be detected in serum.

[Preparation and characterization of monoclonal antibodies against S100A9].

OBJECTIVE: To prepare monoclonal antibodies (mAbs) against S100A9, and characterize these antibodies' properties for functional studies. METHODS: We amplified cDNA fragment of S100A9 from adult human liver sample, then constructed the vectors pGEX-4T-1-S100A9 and pET-32a-S100A9 for protein expression. The S100A9 protein fused respectively with the His-tag and GST-tag were expressed in E.coli and purified for immunizing mice as antigen and hybridoma screening, respectively. After hybriodma screening, the titer of mAbs was determined by ELISA and the specificity was identified by Western blotting and indirect immunofluorescence technique. RESULTS: Both GST-tagged and His-tagged S100A9 fusion proteins were successfully constructed and expressed in E.coli. Eighteen hybridoma cell strains secreting S100A9 mAbs were obtained, of which 15 showed strong positive reaction but 3 showed weak positive reaction to the recombinant protein in Western blotting. Two hybridoma cell strains were capable of binding to S100A9 native protein in tumor tissue interstitial fluid. CONCLUSION: We successfully prepared the mAbs against S100A9 protein, which provide the useful tool for further study on biological function and clinical detection of S100A9.

SILAC-based quantitative proteomic analysis of secretome between activated and reverted hepatic stellate cells.

Activated hepatic stellate cell (HSC) is the main myofibroblast cell in the liver fibrosis (LF). An important characteristic of the recovery of LF is not only the apoptosis of activated HSCs but also reversal of myofibroblast-like phenotype to a quiescent-like phenotype. Understanding the changes of secreted proteins in the reversion of activated HSCs may provide the broader view of cellular regulatory networks and discover candidate markers or targets for therapeutic strategies of LF. In this study, stable isotope labeling with amino acids (SILAC) combined with linear ion trap-Fourier transform ion cyclotron resonance mass spectrometer (LTQ-FT MS) was performed on in vitro activated HSCs and reverted HSCs to obtain a proteomic view of secretory proteins. In total, 330 proteins showed significant differences in reverted HSCs. Among these, 109 upregulated proteins were mainly involved in amino acid metabolism pathway and glucose metabolism pathway using GeneGO/MetaCore software, while 221 downregulated proteins are closely associated with HSCs activation, such as cytoskeleton remodeling, chemokines, and cell adhesion. Additionally, a set of novel proteins associated with HSCs activation and reversion were validated by Western blotting in the cell secretion and in the sera of LF, including vitronectin, laminin beta 1, and ubiquitin conjugation factor E4B. Our study provided the valuable insight into the mechanisms in the reversion of activated HSCs and identified some potential biomarkers of LF in clinical studies. All MS data have been deposited in the ProteomeXchange with identifier PXD000773 (http://proteomecentral.proteomexchange.org/dataset/PXD000773).

Characterization of the liver tissue interstitial fluid (TIF) proteome indicates potential for application in liver disease biomarker discovery.

Tissue interstitial fluid (TIF) forms the interface between circulating body fluids and intracellular fluid. Pathological alterations of liver cells could be reflected in TIF, making it a promising source of liver disease biomarkers. Mouse liver TIF was extracted, separated by SDS-PAGE, analyzed by linear ion trap mass spectrometer, and 1450 proteins were identified. These proteins may be secreted, shed from membrane vesicles, or represent cellular breakdown products. They show different profiling patterns, quantities, and possibly modification/cleavage of intracellular proteins. The high solubility and even distribution of liver TIF supports its suitability for proteome analysis. Comparison of mouse liver TIF data with liver tissue and plasma proteome data identified major proteins that might be released from liver to plasma and serve as blood biomarkers of liver origin. This result was partially supported by comparison of human liver TIF data with human liver and plasma proteome data. Paired TIFs from tumor and nontumor liver tissues of a hepatocellular carcinoma patient were analyzed and the profile of subtracted differential proteins supports the potential for biomarker discovery in TIF. This study is the first analysis of the liver TIF proteome and provides a foundation for further application of TIF in liver disease biomarker discovery.