Amniotic fluid metabolomics identifies impairment of glycerophospholipid and amino acid metabolism during congenital Zika syndrome development

PURPOSE

Our main goal is to identify the alterations in the amniotic fluid (AF) metabolome in Zika virus (ZIKV)-infected patients and their relation to congenital Zika syndrome (CZS) progression.

EXPERIMENTAL DESIGN

We applied an untargeted metabolomics strategy to analyze seven AF of pregnant women: healthy women and ZIKV-infected women bearing non-microcephalic and microcephalic fetuses.

RESULTS

Infected patients were characterized by glycerophospholipid metabolism impairment, which is accentuated in microcephalic phenotypes. Glycerophospholipid decreased concentration in AF can be a consequence of intracellular transport of lipids to the placental or fetal tissues under development. The increased intracellular concentration of lipids can lead to mitochondrial dysfunction and neurodegeneration caused by lipid droplet accumulation. Furthermore, the dysregulation of amino acid metabolism was a molecular fingerprint of microcephalic phenotypes, specifically serine, and proline metabolisms. Both amino acid deficiencies were related to neurodegenerative disorders, intrauterine growth retardation, and placental abnormalities.

CONCLUSIONS AND CLINICAL RELEVANCE

This study enhances our understanding of the development of CZS pathology and sheds light on dysregulated pathways that could be relevant for future studies.

Coordination chemistry applied to anti-doping analysis: Cobalt quantification as its diethyldithiocarbamate complex by liquid chromatography coupled to high resolution tandem mass spectrometry

Cobalt was included on the World Anti-Doping Agency Prohibited List in 2015 due to its effect on stimulus of erythropoiesis via stabilization of hypoxia-inducible factor. Although it has proven benefits for performance enhancement, the unavailability of inductively coupled plasma-mass spectrometry on routine of the accredited laboratories is a factor that reduces its applicability in anti-doping analysis. Therefore, an analytical method for quantification of urinary cobalt as its diethyldithiocarbamate complex by liquid chromatography coupled with high-resolution tandem mass spectrometry was developed and validated. Palladium was proposed as internal standard and rhodium as a complexation control. A microwave-assisted acid digestion of the urine samples was essential, not only to eliminate the matrix effect but mainly to avoid the non-specific bond of cobalt to endogenous molecules. A linear method was obtained over the studied range from a negative urine control to a spiked concentration of 25 ng/mL, with an estimated limit of quantification of 2.5 ng/mL, and an adequate combined standard uncertainty of 11.4%. Considering that all reagents are commercially available, the proposed strategy is feasible to be included on routine sample preparation. Monitoring urinary cobalt concentrations globally opens the perspective to support the anti-doping system to define a suitable threshold value and to understand its potential misuse by athletes seeking for performance improvement.

Variable Data Independent Acquisition and Data Mining Exploring Feature-Based Molecular Networking Analysis for Untargeted Screening of Synthetic Cannabinoids in Oral Fluid

Novel psychoactive substances (NPS) are constantly emerging in the drug market, and synthetic cannabinoids (SCs) are included in this NPS family. Forensic laboratories often struggle with these continually emerging SCs, forcing them to develop an untargeted workflow to incorporate these psychoactive drugs in their procedures. Usually, forensic laboratories select analytical methods based on targeted mass spectrometry (MS) technologies for strictly tracking already known NPS. The appropriate way to tackle unknown substances is to develop pipelines for untargeted analysis that include LC-HRMS analytical methods and data analysis. Once established, this strategy would allow drug testing laboratories to be always one step ahead of the new trends concerning the "designer drugs" market. To address this challenge an untargeted workflow based on mass spectrometry data acquisition and data analysis was developed to detect SCs in oral fluid (OF) samples at a low concentration range. The samples were extracted by mixed-mode solid-phase extraction and analyzed by Liquid Chromatography - High-Resolution Mass Spectrometry (LC-HRMS). Tandem mass spectra (MS2) were recorded performing a variable isolation width across a mass range of all theoretical precursor ions (vDIA) after the chromatographic separation. After raw data processing with the MSDial software, the deconvoluted features were sent to GNPS for Feature-Based Molecular Networking (FBMN) construction for nontargeted data mining. The FBMN analysis created a unique integrated network for most of the SCs assessed in the OF at a low level (20 ng/mL). These results demonstrate the potential of an untargeted approach to detect different derivatives of SCs at trace levels for forensic applications.

Monitoring casbene synthase in Jatropha curcas tissues using targeted proteomics

BACKGROUND

Casbene synthase (CS) is responsible for the first committed step in the biosynthesis of phorbol esters (PE) in the Euphorbiaceae. PE are abundant in the seeds of the biofuel crop Jatropha curcas and its toxicity precludes the use of the protein-rich cake obtained after oil extraction as an animal feed and the toxicity of the fumes derived from burning PE containing biofuel is also a matter of concern. This toxicity is a major hindrance to exploit the potential of this crop as a source of raw material to produce biodiesel. For this reason, the current research on J. curcas is mainly focused on the understanding of the biosynthesis and site of synthesis of PE, as an avenue for the development of genotypes unable to synthesize PE in its seeds.

RESULTS

Here, we present targeted proteomics assays (SRM and PRM) to detect and quantify CS in leaves, endosperm, and roots of two J. curcas genotypes with contrasting levels of PE. These assays were based on the use of reference isotopic labeled synthetic peptides (ILSP) predicted from 12 gene models of CS from the J. curcas genome.

CONCLUSION

Our targeted proteomics methods were able to detect and quantify, for the first time, CS gene products and demonstrate the distribution of CS isoforms only in roots from J. curcas genotypes with a high and low concentration of PE. These methods can be expanded to monitor CS, at the protein level, in different tissues and genotypes of J. curcas.

Quantitative Subcellular Proteomics of the Orbitofrontal Cortex of Schizophrenia Patients

Schizophrenia is a chronic disease characterized by the impairment of mental functions with a marked social dysfunction. A quantitative proteomic approach using iTRAQ labeling and SRM, applied to the characterization of mitochondria (MIT), crude nuclear fraction (NUC), and cytoplasm (CYT), can allow the observation of dynamic changes in cell compartments providing valuable insights concerning schizophrenia physiopathology. Mass spectrometry analyses of the orbitofrontal cortex from 12 schizophrenia patients and 8 healthy controls identified 655 protein groups in the MIT fraction, 1500 in NUC, and 1591 in CYT. We found 166 groups of proteins dysregulated among all enriched cellular fractions. Through the quantitative proteomic analysis, we detect as the main biological pathways those related to calcium and glutamate imbalance, cell signaling disruption of CREB activation, axon guidance, and proteins involved in the activation of NF-kB signaling along with the increase of complement protein C3. Based on our data analysis, we suggest the activation of NF-kB as a possible pathway that links the deregulation of glutamate, calcium, apoptosis, and the activation of the immune system in schizophrenia patients. All MS data are available in the ProteomeXchange Repository under the identifier PXD015356 and PXD014350.