| 1. |
- Ambikan, Anoop T., et al.
(författare)
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Genome-scale metabolic models for natural and long-term drug-induced viral control in HIV infection
- 2022
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Ingår i: Life Science Alliance. - : Life Science Alliance, LLC. - 2575-1077. ; 5:9
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GSMMs) can provide novel insights into metabolic reprogramming during disease progression and therapeutic interventions. We developed a context-specific system-level GSMM of people living with HIV (PLWH) using global RNA sequencing data from PBMCs with suppressive viremia either by natural (elite controllers, PLWHEC) or drug-induced (PLWHART) control. This GSMM was compared with HIV-negative controls (HC) to provide a comprehensive systems-level metabo-transcriptomic characterization. Transcriptomic analysis identified up-regulation of oxidative phosphorylation as a characteristic of PLWHART, differentiating them from PLWHEC with dysregulated complexes I, III, and IV. The flux balance analysis identified altered flux in several intermediates of glycolysis including pyruvate, a-ketoglutarate, and glutamate, among others, in PLWHART. The in vitro pharmacological inhibition of OXPHOS complexes in a latent lymphocytic cell model (J-Lat 10.6) suggested a role for complex IV in latency reversal and immunosenescence. Furthermore, inhibition of complexes I/III/IV induced apoptosis, collectively indicating their contribution to reservoir dynamics.
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| 2. |
- Ambikan, Anoop T., et al.
(författare)
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Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity
- 2022
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Ingår i: Cell systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 13:8, s. 665-681
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Tidskriftsartikel (refereegranskat)abstract
- The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolo-mics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a sub-stantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and person-alized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as a-ketoglutarate, succi-nate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.
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