The central role of the glutamate metabolism in long-term antiretroviral treated HIV-infected individuals with metabolic syndrome

Abstract

Metabolic syndrome (MetS) is a significant factor for cardiometabolic comorbidities in people living with HIV (PLWH) and a barrier to healthy aging. The long-term consequences of HIV-infection and combination antiretroviral therapy (cART) in metabolic reprogramming are unknown. In this study, we investigated metabolic alterations in well-treated PLWH with MetS to identify potential mechanisms behind the MetS phenotype using advanced statistical and machine learning algorithms. We included 200 PLWH from the Copenhagen Comorbidity in HIV-infection (COCOMO) study. PLWH were grouped into PLWH with MetS ( n = 100) defined according to the International Diabetes Federation (IDF) consensus worldwide definition of the MetS or without MetS ( n = 100). The untargeted plasma metabolomics was performed using ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS/MS) and immune-phenotyping of Glut1 (glucose transporter), xCT (glutamate/cysteine transporter) and MCT1 (pyruvate/lactate transporter) by flow cytometry. We applied several conventional approaches, machine learning algorithms, and linear classification models to identify the biologically relevant metabolites associated with MetS in PLWH. Of the 877 identified biochemicals, 9% (76/877) differed significantly between PLWH with and without MetS (false discovery rate < 0.05). The majority belonged to amino acid metabolism (43%). A consensus identification by combining supervised and unsupervised methods indicated 11 biomarkers of MetS phenotype in PLWH. A weighted co-expression network identified seven communities of positively intercorrelated metabolites. A single community contained six of the potential biomarkers mainly related to glutamate metabolism. Transporter expression identified altered xCT and MCT in both lymphocytic and monocytic cells. Combining metabolomics and immune-phenotyping indicated altered glutamate metabolism associated with MetS in PLWH, which has clinical significance.

Integrative Lipidomics and Metabolomics for System-Level Understanding of the Metabolic Syndrome in Long-Term Treated HIV-Infected Individuals

“…Our data indicated an increased abundance of the glycerolipids DAGs and TAGs in PLWH with MetS. The comprehensive network integration of the lipidomics and metabolomics ( 8 ) data suggested interactions between specific glycerolipids patterns and key metabolites involved in the glutamate metabolism. Finally, our data also indicated a relationship between the structural composition patterns of these specific glycerolipids with HIV and MetS-specific clinical variables, suggesting their involvement in driving the disease pathogenesis in PLWH with MetS.…”

“…Of the 1099 participants in the COCOMO study, 100 PLWH ≥ 40 years old were included and matched according to age, sex, duration of cART, smoking status, and current CD4+ T-cells count to 100 PLWH without MetS ( 5 , 9 ). The MetS was defined according to the International Diabetes Federation (IDF) consensus worldwide definition of the MetS as previously ( 8 , 10 ). For each individual, we collected clinical data from the COCOMO database with the following 13 HIV and MetS specific variables.…”

“…The MetS, sex, age, ethnicity, immunodeficiency (i.e., lowest CD4+ T-cell count < 200 cells/ µ l or previous AIDS condition), exposure to early-generation antiretroviral therapy (ART) (i.e., medicated with thymidine analogs, didanosine and/or indinavir), visceral adipose tissue (VAT) [cm2], subcutaneous adipose tissue (SAT) [cm2], and ART drugs including the active agents; nucleotide reverse transcriptase inhibitors (NRTIs), non-nucleotide reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), integrase strand transfer inhibitors (INSTIs), and other/unknown active agents). Furthermore, a lipidomics dataset (see below) and a metabolomics dataset with 11 key metabolites [i.e., 1-carboxyethylisoleucine, 4-cholesten-3-one, 4-hydroxyglutamate, α -ketoglutarate, carotene diol(2), γ -glutamylglutamate, glutamate, glycerate, isoleucine, pimeloylcarnitine/3-methyladipoylcarnitine (C7-DC) (PC/3-MAPC), and palmitoyl-sphingosinephosphoethanolamine (d18:1/16:0) (PSP)] previously identified by using a combination of standard biostatistical, machine learning and network analysis technique, were collected ( 8 ). Ethical approval was obtained by the Regional Ethics Committee of Copenhagen (COCOMO: H-15017350).…”

“…Network analyses were used to build a biological network consisting of lipids (n = 917) and previously identified key metabolites (n = 11) ( 8 ) after Spearman’s rank correlation across all species. Edges connecting nodes (i.e., biomolecules) were weighted based on positive correlations.…”

“…The pathophysiology and alterations of the lipidome in PLWH with MetS are yet to be explored and may help in the discovery of new patterns and disease markers associated with MetS in PLWH ( 3 ). In prior work from our group, we identified key metabolites, which influenced and altered the metabolome of PLWH with MetS ( 8 ).…”