The bacterial pathogen Staphylococcus aureus harbors numerous virulence factors that impact infection severity. Beyond virulence gene presence or absence, the expression level of virulence proteins is known to vary across S. aureus lineages and isolates. However, the impact of expression level on severity is poorly understood due to the lack of high-throughput quantification methods of virulence proteins.
We present a targeted proteomic approach able to monitor 42 staphylococcal proteins in a single experiment. Using this approach, we compared the quantitative virulomes of 136 S. aureus isolates from a nationwide cohort of French patients with severe community-acquired staphylococcal pneumonia, all requiring intensive care. We used multivariable regression models adjusted for patient baseline health (Charlson comorbidity score) to identify the virulence factors whose in vitro expression level predicted pneumonia severity markers, namely leukopenia and hemoptysis, as well as patient survival.
We found that leukopenia was predicted by higher expression of HlgB, Nuc, and Tsst-1 and lower expression of BlaI and HlgC, while hemoptysis was predicted by higher expression of BlaZ and HlgB and lower expression of HlgC. Strikingly, mortality was independently predicted in a dose-dependent fashion by a single phage-encoded virulence factor, the Panton-Valentine leucocidin (PVL), both in logistic (OR 1.28; 95%CI[1.02;1.60]) and survival (HR 1.15; 95%CI[1.02;1.30]) regression models.
These findings demonstrate that the in vitro expression level of virulence factors can be correlated with infection severity using targeted proteomics, a method that may be adapted to other bacterial pathogens.

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF454-62, ITIH4429-438, and APOE198-207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF454-62, ITIH4429-438, and APOE198-207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.

UNASSIGNED: Pregnancy-induced hypertension (PIH) is associated with an increased number of neonatal complications, but its impact on neonatal metabolism remains unclear. This study aimed to investigate the differential metabolomics of infants born to mothers with and without PIH.
UNASSIGNED: Blood samples of a total of 115 infants born to mothers with (n=56) and without (n=59) PIH were collected and assigned to two groups, respectively, from the neonatal department of Sixth Affiliated Hospital of Sun Yat-Sen University. A tandem mass spectrometer was used to generate metabolic profiling of amino acid, free carnitine, and acyl-carnitines. The resulting data were analyzed using orthogonal partial least squares discriminant analysis based on the difference between infants born to mothers with or without PIH.
UNASSIGNED: A significant relationship was observed between the two groups (with and without PIH) in the metabolic fingerprint. According to the pattern recognition analysis combined with variance importance, 25 metabolites with high importance were found. The top ten substances were selected for analysis. Compared with infants born to mothers without PIH, glycine levels increased, and C14DC, C22, C4DC, C5:1, C6DC, C5-OH, proline, C14-OH, and C20 decreased in infants born to mothers with PIH.
UNASSIGNED: Using liquid chromatography (LC)-MS/MS metabolomics, a significant relationship was detected between neonatal metabolism and maternal hypertension. It is important to correct the subsequent infantile metabolic disorder by balancing the biomarker metabolites and suppling adequate nutrition to improve the health and growth of newborns of PIH mothers.

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Tricyclic antidepressants (TCA) are used to treat major depressive disorder and other psychological conditions. The efficacy of these drugs is tied to a narrow therapeutic window. Inappropriately high drug concentrations can result in serious side effects such as hypotension, tachycardia, or coma. As a result, concentrations of tricyclic antidepressants are routinely monitored to ensure compliance and to prevent adverse side effects by dose adjustments. We describe a method for the determination of concentrations of amitriptyline, desipramine, imipramine, and nortriptyline in human serum using high-performance liquid chromatography coupled to a tandem mass spectrometer with electrospray ionization (HPLC-ESI-MS/MS). The method is rapid, requiring only 3.5 min per analysis. The method requires 100 μL of serum. Concentrations of each TCA were quantified by a calibration curve relating the peak area ratio of each TCA analyte to a deuterated internal standard (amitriptyline-D3, desipramine-D3, imipramine-D3, and nortriptyline-D3). The method was linear from ~70 ng/mL to ~1000 ng/mL for all TCAs, with imprecision ≤ 12%.