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Sosa-Acosta P, Evaristo GPC, Evaristo JAM, Carneiro GRA, Quiñones-Vega M, Monnerat G, Melo A, Garcez PP, Nogueira FCS, Domont GB. Amniotic fluid metabolomics identifies impairment of glycerophospholipid and amino acid metabolism during congenital Zika syndrome development. Proteomics Clin Appl 2024; 18:e2300008. [PMID: 37329193 DOI: 10.1002/prca.202300008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/02/2023] [Accepted: 06/06/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Patricia Sosa-Acosta
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Laboratory of Proteomics, LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Geisa P C Evaristo
- Center of Applied Biomolecular Studies in Healthy, Osvaldo Cruz Foundation Unit of Rondônia, Porto Velho, Rondonia, Brazil
| | - Joseph A M Evaristo
- Center of Applied Biomolecular Studies in Healthy, Osvaldo Cruz Foundation Unit of Rondônia, Porto Velho, Rondonia, Brazil
| | - Gabriel Reis Alves Carneiro
- Laboratory of Proteomics, LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Mauricio Quiñones-Vega
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Laboratory of Proteomics, LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gustavo Monnerat
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory off Cardiac Electrophysiology Antônio Paes de Carvalho, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Adriana Melo
- Professor Amorim Neto Research Institute, Campina Grande, Paraíba, Brazil
| | - Patrícia P Garcez
- Institute of Biomedical Science, Federal University of Rio de Janeiro, RJ, Brazil
| | - Fábio C S Nogueira
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Laboratory of Proteomics, LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gilberto B Domont
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
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Sosa-Acosta P, Nogueira FCS, Domont GB. Proteomics and Metabolomics in Congenital Zika Syndrome: A Review of Molecular Insights and Biomarker Discovery. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1443:63-85. [PMID: 38409416 DOI: 10.1007/978-3-031-50624-6_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Zika virus (ZIKV) infection can be transmitted vertically, leading to the development of congenital Zika syndrome (CZS) in infected fetuses. During the early stages of gestation, the fetuses face an elevated risk of developing CZS. However, it is important to note that late-stage infections can also result in adverse outcomes. The differences between CZS and non-CZS phenotypes remain poorly understood. In this review, we provide a summary of the molecular mechanisms underlying ZIKV infection and placental and blood-brain barriers trespassing. Also, we have included molecular alterations that elucidate the progression of CZS by proteomics and metabolomics studies. Lastly, this review comprises investigations into body fluid samples, which have aided to identify potential biomarkers associated with CZS.
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Affiliation(s)
- Patricia Sosa-Acosta
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fábio C S Nogueira
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
- Laboratory of Proteomics (LabProt), LADETEC, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
| | - Gilberto B Domont
- Proteomics Unit, Department of Biochemistry, Institute of Chemistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
- Precision Medicine Research Center, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
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Dos Santos EKP, Canuto GAB. Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study. Metabolomics 2023; 19:26. [PMID: 36976375 DOI: 10.1007/s11306-023-01992-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND AND AIMS Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform. METHODS GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples. RESULTS The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh). CONCLUSION This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.
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Delafiori J, Faria AVDS, de Oliveira AN, Sales GM, Dias-Audibert FL, Catharino RR. Unraveling the Metabolic Alterations Induced by Zika Infection in Prostate Epithelial (PNT1a) and Adenocarcinoma (PC-3) Cell Lines. J Proteome Res 2023; 22:193-203. [PMID: 36469742 DOI: 10.1021/acs.jproteome.2c00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The outbreak of Zika virus infection in 2016 led to the identification of its presence in several types of biofluids, including semen. Later discoveries associated Zika infection with sexual transmission and persistent replication in cells of the male reproductive tract. Prostate epithelial and carcinoma cells are favorable to virus replication, with studies pointing to transcriptomics alterations of immune and inflammation genes upon persistence. However, metabolome alterations promoted by the Zika virus in prostate cells are unknown. Given its chronic effects and oncolytic potential, we aim to investigate the metabolic alterations induced by the Zika virus in prostate epithelial (PNT1a) and adenocarcinoma (PC-3) cells using an untargeted metabolomics approach and high-resolution mass spectrometry. PNT1a cells were viable up to 15 days post ZIKV infection, in contrast to its antiproliferative effect in the PC-3 cell lineage. Remarkable alterations in the PNT1a cell metabolism were observed upon infection, especially regarding glycerolipids, fatty acids, and acylcarnitines, which could be related to viral cellular resource exploitation, in addition to the over-time increase in oxidative stress metabolites associated with carcinogenesis. The upregulation of FA20:5 at 5 dpi in PC-3 cells corroborates the antiproliferative effect observed since this metabolite was previously reported to induce PC-3 cell death. Overall, Zika virus promotes extensive lipid alterations on both PNT1a and PC-3 cells, promoting different outcomes based on the cellular metabolic state.
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Affiliation(s)
- Jeany Delafiori
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, SP 13083-970, Brazil
| | - Alessandra V de S Faria
- Department of Biochemistry and Tissue Biology, University of Campinas, Campinas, SP 13083-862, Brazil
| | - Arthur N de Oliveira
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, SP 13083-970, Brazil
| | - Geovana M Sales
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, SP 13083-970, Brazil
| | - Flávia Luísa Dias-Audibert
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, SP 13083-970, Brazil
| | - Rodrigo R Catharino
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, SP 13083-970, Brazil
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Liu F, Ni B, Wei R. Senecavirus A- and Non-Infected Cells at Early Stage of Infection: Comparative Metabolomic Profiles. Front Cell Infect Microbiol 2022; 11:736506. [PMID: 35071028 PMCID: PMC8776658 DOI: 10.3389/fcimb.2021.736506] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 11/08/2021] [Indexed: 11/24/2022] Open
Abstract
Senecavirus A (SVA), classified into the genus Senecavirus in the family Picornaviridae, causes an infectious disease in pigs. This virus can efficiently replicate in some non-pig-derived cells, such as the BHK cell line and its derivative (BSR-T7/5 cell line). We had recovered a wild-type SVA from its cDNA clone previously, and then uncovered the proteomic profile of SVA-infected BSR-T7/5 cells at 12 h post inoculation (hpi). In order to explore the cellular metabolomics further, the SVA-inoculated BSR-T7/5 cell monolayer was collected at 12 hpi for assay via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The resultant data set was comprehensively analyzed using bioinformatics tools. A total of 451 metabolites were identified using in-house and public databases. Out of these metabolites, sixty-one showed significantly differential values (p value < 0.05). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyze metabolic pathways of the significantly differential metabolites. There were eighty-one identified KEGG pathways, out of which twenty-seven showed their p values < 0.05. The pyrimidine metabolism revealed the minimum p value and the maximum number of significantly differential metabolites, implying the pyrimidine played a key role in cellular metabolism after SVA infection. SVA replication must rely on the cellular metabolism. The present study on metabolomics would shed light on impacts of SVA-induced multiple interactions among metabolites on cells or even on natural hosts.
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Affiliation(s)
- Fuxiao Liu
- College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, China
| | - Bo Ni
- Surveillance Laboratory of Livestock Diseases, China Animal Health and Epidemiology Center, Qingdao, China
| | - Rong Wei
- Surveillance Laboratory of Livestock Diseases, China Animal Health and Epidemiology Center, Qingdao, China
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