Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy production

COVID-19: A complex disease with a unique metabolic signature

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Understanding COVID-19 progression with longitudinal peripheral blood mononuclear cell proteomics: Changes in the cellular proteome over time

The clinical presentation of COVID-19 is highly variable, and understanding the underlying biological processes is crucial. This study utilized a proteomic analysis to investigate dysregulated processes in the peripheral blood mononuclear cells of patients with COVID-19 compared to healthy volunteers. Samples were collected at different stages of the disease, including hospital admission, after 7 days of hospitalization, and 30 days after discharge. Metabolic pathway alterations and increased abundance of neutrophil-related proteins were observed in patients. Patients progressing to critical illness had significantly low-abundance proteins in the pentose phosphate and glycolysis pathways compared with those presenting clinical recovery. Important biological processes, such as fatty acid concentration and glucose metabolism disorder, remained altered even after 30 days of hospital discharge. Temporal proteomic changes revealed distinct pathways in critically ill and non-critically ill patients. Our study emphasizes the significance of longitudinal cellular proteomic studies in identifying disease progression-related pathways and persistent protein changes post-hospitalization.

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer

Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. This tutorial presents a protocol for applying machine learning models in survival analysis for both clinical and transcriptomic data. We show that integrating clinical and mRNA expression data is essential to explain the multiple biological processes driving cancer progression. Our results reveal that machine-learning-based models such as random survival forests, gradient boosted survival model, and survival support vector machine can outperform the traditional statistical methods, i.e., Cox proportional hazard model. The highest C-index among the machine learning models was recorded when using survival support vector machine, with a value 0.688, whereas the C-index recorded using the Cox model was 0.677. Shapley Additive Explanation (SHAP) values were also applied to identify the feature importance of the models and their impact on the prediction outcomes.

CD36 accelerates the progression of hepatocellular carcinoma by promoting FAs absorption

CD36 is emerging as a potential strategy for cancer treatment because of its function of regulating fatty acid intake. The purpose of this study was to clarify the molecular mechanism of CD36 in the progression of HCC. TCGA database was used to analyze the relationship of CD36 with HCC. The expression of CD36 in HCC clinical samples and cell lines was detected by qRT-PCR and western blot. Huh7 cells and HCCLM3 cells were transfected and treated into different group. CCK-8 and clone formation assay were used to detect the cell proliferation ability. Wound healing and transwell experiment were used to detect the metastatic ability. HCC xenografts were constructed in nude mice by subcutaneous injection of stably transfected Huh7 cells. The expression of CD36 in HCC was detected by immunohistochemistry (IHC). The contents of phospholipids and triglycerides in HCC cells were detected by ELISA. And the content of neutral lipids in HCC cells was detected by staining with BODIPY 493/503 and DAPI dye. Then transcriptional sequencing was used to determine the downstream mechanism of CD36 in HCC, and the differentially expressed genes (DEGs) were analyzed. CD36 was upregulated in HCC. Knockdown of CD36 could suppress the proliferation and metastasis of HCC in vitro and in vivo by regulating FAs intake in HCC. In addition, the expression of AKR1C2 was suppressed by sh-CD36, and which was also involved in the regulation of FAs intake. The molecular mechanism by which CD36 accelerated the progression of HCC was to promote the expression of AKR1C2 and thus enhance fatty acids (FAs) intake.

Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts

Duchenne muscular dystrophy (DMD) affects myofibers and muscle stem cells, causing progressive muscle degeneration and repair defects. It was unknown whether dystrophic myoblasts—the effector cells of muscle growth and regeneration—are affected. Using transcriptomic, genome-scale metabolic modelling and functional analyses, we demonstrate, for the first time, convergent abnormalities in primary mouse and human dystrophic myoblasts. In Dmd^mdx myoblasts lacking full-length dystrophin, the expression of 170 genes was significantly altered. Myod1 and key genes controlled by MyoD (Myog, Mymk, Mymx, epigenetic regulators, ECM interactors, calcium signalling and fibrosis genes) were significantly downregulated. Gene ontology analysis indicated enrichment in genes involved in muscle development and function. Functionally, we found increased myoblast proliferation, reduced chemotaxis and accelerated differentiation, which are all essential for myoregeneration. The defects were caused by the loss of expression of full-length dystrophin, as similar and not exacerbated alterations were observed in dystrophin-null Dmd^mdx-βgeo myoblasts. Corresponding abnormalities were identified in human DMD primary myoblasts and a dystrophic mouse muscle cell line, confirming the cross-species and cell-autonomous nature of these defects. The genome-scale metabolic analysis in human DMD myoblasts showed alterations in the rate of glycolysis/gluconeogenesis, leukotriene metabolism, and mitochondrial beta-oxidation of various fatty acids. These results reveal the disease continuum: DMD defects in satellite cells, the myoblast dysfunction affecting muscle regeneration, which is insufficient to counteract muscle loss due to myofiber instability. Contrary to the established belief, our data demonstrate that DMD abnormalities occur in myoblasts, making these cells a novel therapeutic target for the treatment of this lethal disease.

COVID-19 metabolism: Mechanisms and therapeutic targets

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) dysregulates antiviral signaling, immune response, and cell metabolism in human body. Viral genome and proteins hijack host metabolic network to support viral biogenesis and propagation. However, the regulatory mechanism of SARS‐CoV‐2‐induced metabolic dysfunction has not been elucidated until recently. Multiomic studies of coronavirus disease 2019 (COVID‐19) revealed an intensive interaction between host metabolic regulators and viral proteins. SARS‐CoV‐2 deregulated cellular metabolism in blood, intestine, liver, pancreas, fat, and immune cells. Host metabolism supported almost every stage of viral lifecycle. Strikingly, viral proteins were found to interact with metabolic enzymes in different cellular compartments. Biochemical and genetic assays also identified key regulatory nodes and metabolic dependencies of viral replication. Of note, cholesterol metabolism, lipid metabolism, and glucose metabolism are broadly involved in viral lifecycle. Here, we summarized the current understanding of the hallmarks of COVID‐19 metabolism. SARS‐CoV‐2 infection remodels host cell metabolism, which in turn modulates viral biogenesis and replication. Remodeling of host metabolism creates metabolic vulnerability of SARS‐CoV‐2 replication, which could be explored to uncover new therapeutic targets. The efficacy of metabolic inhibitors against COVID‐19 is under investigation in several clinical trials. Ultimately, the knowledge of SARS‐CoV‐2‐induced metabolic reprogramming would accelerate drug repurposing or screening to combat the COVID‐19 pandemic.

Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity

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 metabolomics 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 substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, 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.

The spike of SARS-CoV-2 promotes metabolic rewiring in hepatocytes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a multi-organ damage that includes hepatic dysfunction, which has been observed in over 50% of COVID-19 patients. Liver injury in COVID-19 could be attributed to the cytopathic effects, exacerbated immune responses or treatment-associated drug toxicity. Herein we demonstrate that hepatocytes are susceptible to infection in different models: primary hepatocytes derived from humanized angiotensin-converting enzyme-2 mice (hACE2) and primary human hepatocytes. Pseudotyped viral particles expressing the full-length spike of SARS-CoV-2 and recombinant receptor binding domain (RBD) bind to ACE2 expressed by hepatocytes, promoting metabolic reprogramming towards glycolysis but also impaired mitochondrial activity. Human and hACE2 primary hepatocytes, where steatosis and inflammation were induced by methionine and choline deprivation, are more vulnerable to infection. Inhibition of the renin-angiotensin system increases the susceptibility of primary hepatocytes to infection with pseudotyped viral particles. Metformin, a common therapeutic option for hyperglycemia in type 2 diabetes patients known to partially attenuate fatty liver, reduces the infection of human and hACE2 hepatocytes. In summary, we provide evidence that hepatocytes are amenable to infection with SARS-CoV-2 pseudovirus, and we propose that metformin could be a therapeutic option to attenuate infection by SARS-CoV-2 in patients with fatty liver.

SARS-CoV-2 pseudovirus infects human hepatocytes leading to metabolic reprogramming towards glycolysis and impaired mitochondrial activity, and metformin can reduce infection under steatotic conditions.

Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models

COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.

Two years into COVID-19 - Lessons in SARS-CoV-2 and a perspective from papers in FEBS Letters

The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies.