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Petrosyan V, Dobrolecki LE, Thistlethwaite L, Lewis AN, Sallas C, Srinivasan RR, Lei JT, Kovacevic V, Obradovic P, Ellis MJ, Osborne CK, Rimawi MF, Pavlick A, Shafaee MN, Dowst H, Jain A, Saltzman AB, Malovannaya A, Marangoni E, Welm AL, Welm BE, Li S, Wulf GM, Sonzogni O, Huang C, Vasaikar S, Hilsenbeck SG, Zhang B, Milosavljevic A, Lewis MT. Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach. iScience 2023; 26:105799. [PMID: 36619972 DOI: 10.1016/j.isci.2022.105799] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/20/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.
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Milosavljevic S, Glinton KE, Li X, Medeiros C, Gillespie P, Seavitt JR, Graham BH, Elsea SH. Untargeted Metabolomics of Slc13a5 Deficiency Reveal Critical Liver-Brain Axis for Lipid Homeostasis. Metabolites 2022; 12:metabo12040351. [PMID: 35448538 PMCID: PMC9032242 DOI: 10.3390/metabo12040351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/29/2022] [Accepted: 04/03/2022] [Indexed: 01/17/2023] Open
Abstract
Though biallelic variants in SLC13A5 are known to cause severe encephalopathy, the mechanism of this disease is poorly understood. SLC13A5 protein deficiency reduces citrate transport into the cell. Downstream abnormalities in fatty acid synthesis and energy generation have been described, though biochemical signs of these perturbations are inconsistent across SLC13A5 deficiency patients. To investigate SLC13A5-related disorders, we performed untargeted metabolic analyses on the liver, brain, and serum from a Slc13a5-deficient mouse model. Metabolomic data were analyzed using the connect-the-dots (CTD) methodology and were compared to plasma and CSF metabolomics from SLC13A5-deficient patients. Mice homozygous for the Slc13a5tm1b/tm1b null allele had perturbations in fatty acids, bile acids, and energy metabolites in all tissues examined. Further analyses demonstrated that for several of these molecules, the ratio of their relative tissue concentrations differed widely in the knockout mouse, suggesting that deficiency of Slc13a5 impacts the biosynthesis and flux of metabolites between tissues. Similar findings were observed in patient biofluids, indicating altered transport and/or flux of molecules involved in energy, fatty acid, nucleotide, and bile acid metabolism. Deficiency of SLC13A5 likely causes a broader state of metabolic dysregulation than previously recognized, particularly regarding lipid synthesis, storage, and metabolism, supporting SLC13A5 deficiency as a lipid disorder.
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Affiliation(s)
- Sofia Milosavljevic
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; (S.M.); (K.E.G.); (X.L.); (J.R.S.)
- Harvard Medical School, Boston, MA 02215, USA
| | - Kevin E. Glinton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; (S.M.); (K.E.G.); (X.L.); (J.R.S.)
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; (S.M.); (K.E.G.); (X.L.); (J.R.S.)
| | - Cláudia Medeiros
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (C.M.); (P.G.); (B.H.G.)
| | - Patrick Gillespie
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (C.M.); (P.G.); (B.H.G.)
| | - John R. Seavitt
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; (S.M.); (K.E.G.); (X.L.); (J.R.S.)
| | - Brett H. Graham
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (C.M.); (P.G.); (B.H.G.)
| | - Sarah H. Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; (S.M.); (K.E.G.); (X.L.); (J.R.S.)
- Correspondence: ; Tel.: +1-713-798-5484
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Thistlethwaite LR, Petrosyan V, Li X, Miller MJ, Elsea SH, Milosavljevic A. Correction: CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models. PLoS Comput Biol 2021; 17:e1009551. [PMID: 34695129 PMCID: PMC8544872 DOI: 10.1371/journal.pcbi.1009551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pcbi.1008550.].
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Gilard V, Ferey J, Marguet F, Fontanilles M, Ducatez F, Pilon C, Lesueur C, Pereira T, Basset C, Schmitz-Afonso I, Di Fioré F, Laquerrière A, Afonso C, Derrey S, Marret S, Bekri S, Tebani A. Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma. Cancers (Basel) 2021; 13:5157. [PMID: 34680306 DOI: 10.3390/cancers13205157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/01/2021] [Accepted: 10/03/2021] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.
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Ducatez F, Mauhin W, Boullier A, Pilon C, Pereira T, Aubert R, Benveniste O, Marret S, Lidove O, Bekri S, Tebani A. Parsing Fabry Disease Metabolic Plasticity Using Metabolomics. J Pers Med 2021; 11:jpm11090898. [PMID: 34575675 PMCID: PMC8468728 DOI: 10.3390/jpm11090898] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/03/2021] [Accepted: 09/05/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Fabry disease (FD) is an X-linked lysosomal disease due to a deficiency in the activity of the lysosomal α-galactosidase A (GalA), a key enzyme in the glycosphingolipid degradation pathway. FD is a complex disease with a poor genotype–phenotype correlation. FD could involve kidney, heart or central nervous system impairment that significantly decreases life expectancy. The advent of omics technologies offers the possibility of a global, integrated and systemic approach well-suited for the exploration of this complex disease. Materials and Methods: Sixty-six plasmas of FD patients from the French Fabry cohort (FFABRY) and 60 control plasmas were analyzed using liquid chromatography and mass spectrometry-based targeted metabolomics (188 metabolites) along with the determination of LysoGb3 concentration and GalA enzymatic activity. Conventional univariate analyses as well as systems biology and machine learning methods were used. Results: The analysis allowed for the identification of discriminating metabolic profiles that unambiguously separate FD patients from control subjects. The analysis identified 86 metabolites that are differentially expressed, including 62 Glycerophospholipids, 8 Acylcarnitines, 6 Sphingomyelins, 5 Aminoacids and 5 Biogenic Amines. Thirteen consensus metabolites were identified through network-based analysis, including 1 biogenic amine, 2 lysophosphatidylcholines and 10 glycerophospholipids. A predictive model using these metabolites showed an AUC-ROC of 0.992 (CI: 0.965–1.000). Conclusion: These results highlight deep metabolic remodeling in FD and confirm the potential of omics-based approaches in lysosomal diseases to reveal clinical and biological associations to generate pathophysiological hypotheses.
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Affiliation(s)
- Franklin Ducatez
- Department of Metabolic Biochemistry, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France; (F.D.); (C.P.); (R.A.); (S.B.)
- Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France;
| | - Wladimir Mauhin
- Department of Internal Medicine, Groupe Hospitalier Diaconesses Croix Saint Simon, Site Avron & UMRS 974, 75013 Paris, France; (W.M.); (O.L.)
| | - Agnès Boullier
- MP3CV-UR7517, CURS-Université de Picardie Jules Verne, Avenue de la Croix Jourdain, 80054 Amiens, France;
- Laboratoire de Biochimie CHU Amiens-Picardie, Avenue de la Croix Jourdain, 80054 Amiens, France
| | - Carine Pilon
- Department of Metabolic Biochemistry, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France; (F.D.); (C.P.); (R.A.); (S.B.)
| | - Tony Pereira
- CHU Rouen, Institut de Biologie Clinique, 76000 Rouen, France;
| | - Raphaël Aubert
- Department of Metabolic Biochemistry, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France; (F.D.); (C.P.); (R.A.); (S.B.)
| | - Olivier Benveniste
- Department of Internal Medicine, Hôpital Pitié-Salpêtrière & INSERM U 974, 75013 Paris, France;
| | - Stéphane Marret
- Department of Neonatal Pediatrics, Intensive Care, and Neuropediatrics, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France;
| | - Olivier Lidove
- Department of Internal Medicine, Groupe Hospitalier Diaconesses Croix Saint Simon, Site Avron & UMRS 974, 75013 Paris, France; (W.M.); (O.L.)
| | - Soumeya Bekri
- Department of Metabolic Biochemistry, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France; (F.D.); (C.P.); (R.A.); (S.B.)
| | - Abdellah Tebani
- Department of Metabolic Biochemistry, Normandie University, UNIROUEN, INSERM U1245, CHU Rouen, 76000 Rouen, France; (F.D.); (C.P.); (R.A.); (S.B.)
- Correspondence:
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