Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients

Metabolomic Insights into COVID-19 Severity: A Scoping Review

BACKGROUND

In 2019, SARS-CoV-2, the novel coronavirus, entered the world scene, presenting a global health crisis with a broad spectrum of clinical manifestations. Recognizing the significance of metabolomics as the omics closest to symptomatology, it has become a useful tool for predicting clinical outcomes. Several metabolomic studies have indicated variations in the metabolome corresponding to different disease severities, highlighting the potential of metabolomics to unravel crucial insights into the pathophysiology of SARS-CoV-2 infection.

METHODS

The PRISMA guidelines were followed for this scoping review. Three major scientific databases were searched: PubMed, the Directory of Open Access Journals (DOAJ), and BioMed Central, from 2020 to 2024. Initially, 2938 articles were identified and vetted with specific inclusion and exclusion criteria. Of these, 42 articles were retrieved for analysis and summary.

RESULTS

Metabolites were identified that were repeatedly noted to change with COVID-19 and its severity. Phenylalanine, glucose, and glutamic acid increased with severity, while tryptophan, proline, and glutamine decreased, highlighting their association with COVID-19 severity. Additionally, pathway analysis revealed that phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis were the most significantly impacted pathways in COVID-19 severity.

CONCLUSIONS

COVID-19 severity is intricately linked to significant metabolic alterations that span amino acid metabolism, energy production, immune response modulation, and redox balance.

AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium

Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains.

An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial for studying complex biological systems. Traditional text-mining methods often have limited accuracy because they don't capture semantic and contextual nuances. Deep-learning models can be computationally expensive and typically have low interpretability, though efforts in explainable AI aim to mitigate this. Furthermore, transformer-based models have a tendency to produce false or made-up information-a problem known as hallucination-which is especially prevalent in large language models (LLMs). This study proposes a hybrid approach combining text-mining techniques with graph neural networks (GNNs) and fine-tuned large language models (LLMs) to extend biomedical knowledge graphs and interpret predicted edges based on published literature. An LLM is used to validate predictions and provide explanations. Evaluated on a corpus of experimentally confirmed protein interactions, the approach achieved a Matthews correlation coefficient (MCC) of 0.772. Applied to insomnia, the approach identified 25 interactions between 32 human proteins absent in known knowledge bases, including regulatory interactions between MAOA and 5-HT2C, binding between ADAM22 and 14-3-3 proteins, which is implicated in neurological diseases, and a circadian regulatory loop involving RORB and NR1D1. The hybrid GNN-LLM method analyzes biomedical literature efficiency to uncover potential molecular interactions for complex disorders. It can accelerate therapeutic target discovery by focusing expert verification on the most relevant automatically extracted information.

Ceramides As Potential New Predictors of the Severity of Acute Coronary Syndrome in Conjunction with SARS-CoV-2 Infection

Acute coronary events (ACEs) associated with a SARS-CoV-2 infection can significantly differ from classic ACEs. New biomarkers, such as ceramides, may help in the diagnosis and treatment of this disease. This study included 73 ACE patients for whom the SARS-CoV-2 infection was verified. Two subgroups were formed: the favorable outcome subgroup and the fatal outcome subgroup. Plasma samples were collected from all patients at the time of admission for a metabolomic analysis. The analysis of metabolites revealed that the ceramide levels were significantly lower in the fatal outcome subgroup than in the survivor subgroup. Therefore, determining ceramide levels in patients with ACEs in conjunction with COVID-19 may help assess the prognosis of these patients and manage their risks.

Global LC-MS/MS targeted metabolomics using a combination of HILIC and RP LC separation modes on an organic monolithic column based on 1-vinyl-1,2,4-triazole

The paper presents an LC-MS/MS-based approach to targeted screening of both polar and non-polar metabolites using a synthesized monolithic column which is a copolymer of styrene, divinylbenzene, and 1-vinyl-1,2,4-triazole. It was shown that this column in combination with eluents 20 mM (NH4)2CO3 + NH3 (pH = 9.8, eluent A) and ACN (eluent B) allows for separation of metabolites of different nature in two modes, HILIC and RP LC, and these methods are mutually complementary. A combination of analyses based on these two modes was proposed, allowing detection of about 400 metabolites in a total time of less than 30 min. Comparison of the developed method with those utilizing commercially available columns with sorbents of various types showed that it could provide a broader metabolite coverage. Using the developed approach, metabolomic screening of dried blood spots samples of mice exposed with X-ray was performed, and metabolites that could be considered as possible markers of irradiation exposure and organ tissue damage were detected. Analysis of marker metabolites revealed metabolic pathways that were altered by radiation exposure. Comparison of the results with literature data showed the effectiveness of the developed metabolomic screening approach.

Gene networks for use in metabolomic data analysis of blood plasma from patients with postoperative delirium

Postoperative delirium (POD) is considered one of the most severe complications, resulting in impaired cognitive function, extended hospitalization, and higher treatment costs. The challenge of early POD diagnosis becomes particularly significant in cardiac surgery cases, as the incidence of this complication exceeds 50 % in certain patient categories. While it is known that neuroinflammation, neurotransmitter imbalances, disruptions in neuroendocrine regulation, and interneuronal connections contribute significantly to the development of POD, the molecular, genetic mechanisms of POD in cardiac surgery patients, along with potential metabolomic diagnostic markers, remain inadequately understood. In this study, blood plasma was collected from a group of patients over 65 years old after cardiac surgery involving artificial circulation. The collected samples were analyzed for sphingomyelin content and quantity using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS) methods. The analysis revealed four significantly different sphingomyelin contents in patients with POD compared to those who did not develop POD (control group). Employing gene network reconstruction, we perceived a set of 82 regulatory enzymes affiliated with the genetic coordination of the sphingolipid metabolism pathway. Within this set, 47 are assumed to be regulators of gene expression, governing the transcription of enzymes pivotal to the metabolic cascade. Complementing this, an additional assembly of 35 regulators are considered to be regulators of activity, degradation, and translocation dynamics of enzymes integral to the aforementioned pathway. Analysis of the overrepresentation of diseases with which these regulatory proteins are associated showed that the regulators can be categorized into two groups, associated with cardiovascular pathologies (CVP) and neuropsychiatric diseases (NPD), respectively. The regulators associated with CVP are expectedly related to the effects on myocardial tissue during surgery. It is hypothesized that dysfunction of NPD-associated regulators may specifically account for the development of POD after cardiac surgery. Thus, the identified regulatory genes may provide a basis for planning further experiments, in order to study disorders at the level of expression of these genes, as well as impaired function of proteins encoded by them in patients with POD. The identified significant sphingolipids can be considered as potential markers of POD.

Prioritization of potential pharmacological targets for the development of anti-hepatocarcinoma drugs modulating the extrinsic apoptosis pathway: the reconstruction and analysis of associative gene networks help

Hepatocellular carcinoma (HCC) is a common severe type of liver cancer characterized by an extremely aggressive course and low survival rates. It is known that disruptions in the regulation of apoptosis activation are some of the key features inherent in most cancer cells, which determines the pharmacological induction of apoptosis as an important strategy for cancer therapy. The computer design of chemical compounds capable of specifically regulating the external signaling pathway of apoptosis induction represents a promising approach for creating new effective ways of therapy for liver cancer and other oncological diseases. However, at present, most of the studies are devoted to pharmacological effects on the internal (mitochondrial) apoptosis pathway. In contrast, the external pathway induced via cell death receptors remains out of focus. Aberrant gene methylation, along with hepatitis C virus (HCV) infection, are important risk factors for the development of hepatocellular carcinoma. The reconstruction of gene networks describing the molecular mechanisms of interaction of aberrantly methylated genes with key participants of the extrinsic apoptosis pathway and their regulation by HCV proteins can provide important information when searching for pharmacological targets. In the present study, 13 criteria were proposed for prioritizing potential pharmacological targets for developing anti-hepatocarcinoma drugs modulating the extrinsic apoptosis pathway. The criteria are based on indicators of the structural and functional organization of reconstructed gene networks of hepatocarcinoma, the extrinsic apoptosis pathway, and regulatory pathways of virus-extrinsic apoptosis pathway interaction and aberrant gene methylation-extrinsic apoptosis pathway interaction using ANDSystem. The list of the top 100 gene targets ranked according to the prioritization rating was statistically significantly (p-value = 0.0002) enriched for known pharmacological targets approved by the FDA, indicating the correctness of the prioritization method. Among the promising potential pharmacological targets, six highly ranked genes (JUN, IL10, STAT3, MYC, TLR4, and KHDRBS1) are likely to deserve close attention.

Molecular-genetic pathways of hepatitis C virus regulation of the expression of cellular factors PREB and PLA2G4C, which play an important role in virus replication

The participants of Hepatitis C virus (HCV) replication are both viral and host proteins. Therapeutic approaches based on activity inhibition of viral non-structural proteins NS3, NS5A, and NS5B are undergoing clinical trials. However, rapid mutation processes in the viral genome and acquisition of drug resistance to the existing drugs remain the main obstacles to fighting HCV. Identifying the host factors, exploring their role in HCV RNA replication, and studying viral effects on their expression is essential for understanding the mechanisms of viral replication and developing novel, effective curative approaches. It is known that the host factors PREB (prolactin regulatory element binding) and PLA2G4C (cytosolic phospholipase A2 gamma) are important for the functioning of the viral replicase complex and the formation of the platforms of HCV genome replication. The expression of PREB and PLA2G4C was significantly elevated in the presence of the HCV genome. However, the mechanisms of its regulation by HCV remain unknown. In this paper, using a text-mining technology provided by ANDSystem, we reconstructed and analyzed gene networks describing regulatory effects on the expression of PREB and PLA2G4C by HCV proteins. On the basis of the gene network analysis performed, we put forward hypotheses about the modulation of the host factors functions resulting from protein-protein interaction with HCV proteins. Among the viral proteins, NS3 showed the greatest number of regulatory linkages. We assumed that NS3 could inhibit the function of host transcription factor (TF) NOTCH1 by protein-protein interaction, leading to upregulation of PREB and PLA2G4C. Analysis of the gene networks and data on differential gene expression in HCV-infected cells allowed us to hypothesize further how HCV could regulate the expression of TFs, the binding sites of which are localized within PREB and PLA2G4C gene regions. The results obtained can be used for planning studies of the molecular-genetic mechanisms of viral-host interaction and searching for potential targets for anti-HCV therapy.

Reconstruction of the regulatory hypermethylation network controlling hepatocellular carcinoma development during hepatitis C viral infection

Hepatocellular carcinoma (HCC) has been associated with hepatitis C viral (HCV) infection as a potential risk factor. Nonetheless, the precise genetic regulatory mechanisms triggered by the virus, leading to virus-induced hepatocarcinogenesis, remain unclear. We hypothesized that HCV proteins might modulate the activity of aberrantly methylated HCC genes through regulatory pathways. Virus-host regulatory pathways, interactions between proteins, gene expression, transport, and stability regulation, were reconstructed using the ANDSystem. Gene expression regulation was statistically significant. Gene network analysis identified four out of 70 HCC marker genes whose expression regulation by viral proteins may be associated with HCC: DNA-binding protein inhibitor ID - 1 (ID1), flap endonuclease 1 (FEN1), cyclin-dependent kinase inhibitor 2A (CDKN2A), and telomerase reverse transcriptase (TERT). It suggested the following viral protein effects in HCV/human protein heterocomplexes: HCV NS3(p70) protein activates human STAT3 and NOTC1; NS2-3(p23), NS5B(p68), NS1(E2), and core(p21) activate SETD2; NS5A inhibits SMYD3; and NS3 inhibits CCN2. Interestingly, NS3 and E1(gp32) activate c-Jun when it positively regulates CDKN2A and inhibit it when it represses TERT. The discovered regulatory mechanisms might be key areas of focus for creating medications and preventative therapies to decrease the likelihood of HCC development during HCV infection.

Computer analysis of regulation of hepatocarcinoma marker genes hypermethylated by HCV proteins

Hepatitis C virus (HCV) is a risk factor that leads to hepatocellular carcinoma (HCC) development. Epigenetic changes are known to play an important role in the molecular genetic mechanisms of virus-induced oncogenesis. Aberrant DNA methylation is a mediator of epigenetic changes that are closely associated with the HCC pathogenesis and considered a biomarker for its early diagnosis. The ANDSystem software package was used to reconstruct and evaluate the statistical significance of the pathways HCV could potentially use to regulate 32 hypermethylated genes in HCC, including both oncosuppressor and protumorigenic ones identified by genome-wide analysis of DNA methylation. The reconstructed pathways included those affecting protein-protein interactions (PPI), gene expression, protein activity, stability, and transport regulations, the expression regulation pathways being statistically significant. It has been shown that 8 out of 10 HCV proteins were involved in these pathways, the HCV NS3 protein being implicated in the largest number of regulatory pathways. NS3 was associated with the regulation of 5 tumor-suppressor genes, which may be the evidence of its central role in HCC pathogenesis. Analysis of the reconstructed pathways has demonstrated that following the transcription factor inhibition caused by binding to viral proteins, the expression of a number of oncosuppressors (WT1, MGMT, SOCS1, P53) was suppressed, while the expression of others (RASF1, RUNX3, WIF1, DAPK1) was activated. Thus, the performed gene-network reconstruction has shown that HCV proteins can influence not only the methylation status of oncosuppressor genes, but also their transcriptional regulation. The results obtained can be used in the search for pharmacological targets to develop new drugs against HCV-induced HCC.

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.