Singapore Grouper Iridovirus Disturbed Glycerophospholipids Homeostasis: Cytosolic Phospholipase A2 Was Essential for Virus Replication

Lopes T, de Oliveira JM, Raimundo JRS, Furtado DZS, Fonseca FLA, Oliveira RV, Cardoso DR, Carrilho E, Assunção NA. Multiplatform Metabolomics: Enhancing the Severity Risk Prognosis of SARS-CoV-2 Infection

Concerns about the SARS-CoV-2 outbreak (COVID-19) continue to persist even years later, with the emergence of new variants and the risk of disease severity. Common clinical symptoms, like cough, fever, and respiratory symptoms, characterize the noncritical patients, classifying them from mild to moderate. In a more severe and complex scenario, the virus infection can affect vital organs, resulting, for instance, in pneumonia and impaired kidney and heart function. However, it is well-known that subclinical symptoms at a metabolic level can be observed previously but require a proper diagnosis because viral replication on the host leaves a track with a different profile depending on the severity of the illness. Metabolomic profiles of mild, moderate, and severe COVID-19 patients were obtained by multiple platforms (LC-HRMS and MALDI-MS), increasing the chance to elucidate a prognosis for severity risk. A strong link was discovered between phenylalanine metabolism and increased COVID-19 severity symptoms, a pathway linked to cardiac and neurological consequences. Glycerophospholipids and sphingolipid metabolisms were also dysregulated linearly with the increasing symptom severity, which can be related to virus proliferation, immune system avoidance, and apoptosis escaping. Our data, endorsed by other literature, strengthens the notion that these pathways might play a vital role in a patient's prognosis.

Elucidating the role of S100A10 in CD8+ T cell exhaustion and HCC immune escape via the cPLA2 and 5-LOX axis

Hepatocellular carcinoma (HCC) is a common malignant tumor with a complex immune evasion mechanism posing a challenge to treatment. The role of the S100A10 gene in various cancers has garnered significant attention. This study aims to elucidate the impact of S100A10 on CD8+ T cell exhaustion via the cPLA2 and 5-LOX axis, thereby elucidating its role in immune evasion in HCC. By analyzing the HCC-related data from the GEO and TCGA databases, we identified differentially expressed genes associated with lipid metabolism and developed a prognostic risk model. Subsequently, through RNA-seq and PPI analyses, we determined vital lipid metabolism genes and downstream factors S100A10, ACOT7, and SMS, which were significantly correlated with CD8+ T cell infiltration. Given the most significant expression differences, we selected S100A10 for further investigation. Both in vitro and in vivo experiments were conducted, including co-culture experiments of CD8+ T cells with MHCC97-L cells, Co-IP experiments, and validation in an HCC mouse model. S100A10 was significantly overexpressed in HCC tissues and potentially regulates CD8+ T cell exhaustion and lipid metabolism reprogramming through the cPLA2 and 5-LOX axis. Silencing S100A10 could inhibit CD8+ T cell exhaustion, further suppressing immune evasion in HCC. S100A10 may activate the cPLA2 and 5-LOX axis, initiating lipid metabolism reprogramming and upregulating LTB4 levels, thus promoting CD8+ T cell exhaustion in HCC tissues, facilitating immune evasion by HCC cells, ultimately impacting the growth and migration of HCC cells. This research highlights the critical role of S100A10 via the cPLA2 and 5-LOX axis in immune evasion in HCC, providing new theoretical foundations and potential targets for diagnosing and treating HCC.

Fish ELOVL7a is involved in virus replication via lipid metabolic reprogramming

The elongation of very long chain fatty acids (ELOVL) proteins are key rate-limiting enzymes that catalyze fatty acid synthesis to form long chain fatty acids. ELOVLs also play regulatory roles in the lipid metabolic reprogramming induced by mammalian viruses. However, little is known about the roles of fish ELOVLs during virus infection. Here, a homolog of ELOVL7 was cloned from Epinephelus coioides (EcELOVL7a), and its roles in red-spotted grouper nervous necrosis virus (RGNNV) and Singapore grouper iridovirus (SGIV) infection were investigated. The transcription level of EcELOVL7a was significantly increased upon RGNNV and SGIV infection or other pathogen-associated molecular patterns stimulation in grouper spleen (GS) cells. Subcellular localization analysis showed that EcELOVL7a encoded an endoplasmic reticulum (ER) related protein. Overexpression of EcELOVL7a promoted the viral production and virus release during SGIV and RGNNV infection. Furthermore, the lipidome profiling showed that EcELOVL7a overexpression reprogrammed cellular lipid components in vitro, evidenced by the increase of glycerophospholipids, sphingolipids and glycerides components. In addition, VLCFAs including FFA (20:2), FFA (20:4), FFA (22:4), FFA (22:5) and FFA (24:0), were enriched in EcELOVL7a overexpressed cells. Consistently, EcELOVL7a overexpression upregulated the transcription level of the key lipid metabolic enzymes, including fatty acid synthase (FASN), phospholipase A 2α (PLA 2α), and cyclooxygenases -2 (COX-2), LPIN1, and diacylglycerol acyltransferase 1α (DGAT1α). Together, our results firstly provided the evidence that fish ELOVL7a played an essential role in SGIV and RGNNV replication by reprogramming lipid metabolism.

Integrated multi-omics analysis reveals liver metabolic reprogramming by fish iridovirus and antiviral function of alpha-linolenic acid

Iridovirus poses a substantial threat to global aquaculture due to its high mortality rate; however, the molecular mechanisms underpinning its pathogenesis are not well elucidated. Here, a multi-omics approach was applied to groupers infected with Singapore grouper iridovirus (SGIV), focusing on the roles of key metabolites. Results showed that SGIV induced obvious histopathological damage and changes in metabolic enzymes within the liver. Furthermore, SGIV significantly reduced the contents of lipid droplets, triglycerides, cholesterol, and lipoproteins. Metabolomic analysis indicated that the altered metabolites were enriched in 19 pathways, with a notable down-regulation of lipid metabolites such as glycerophosphates and alpha-linolenic acid (ALA), consistent with disturbed lipid homeostasis in the liver. Integration of transcriptomic and metabolomic data revealed that the top enriched pathways were related to cell growth and death and nucleotide, carbohydrate, amino acid, and lipid metabolism, supporting the conclusion that SGIV infection induced liver metabolic reprogramming. Further integrative transcriptomic and proteomic analysis indicated that SGIV infection activated crucial molecular events in a phagosome-immune depression-metabolism dysregulation-necrosis signaling cascade. Of note, integrative multi-omics analysis demonstrated the consumption of ALA and linoleic acid (LA) metabolites, and the accumulation of L-glutamic acid (GA), accompanied by alterations in immune, inflammation, and cell death-related genes. Further experimental data showed that ALA, but not GA, suppressed SGIV replication by activating antioxidant and anti-inflammatory responses in the host. Collectively, these findings provide a comprehensive resource for understanding host response dynamics during fish iridovirus infection and highlight the antiviral potential of ALA in the prevention and treatment of iridoviral diseases.

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients

The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.