DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.

The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database. However, this method is based on manual metabolic pathway construction and cannot be used for large genome sequencing data. To address such problems, we apply a supervised machine learning approach consisting of deep neural networks to learn feature representations of metabolic pathways and feed these representations into random forests to predict metabolic pathways. The supervised learning model, DeepRF, predicts all known and unknown metabolic pathways in an organism. Evaluation of DeepRF on over 318,016 instances shows that the model can predict metabolic pathways with high-performance metrics accuracy (>97%), recall (>95%), and precision (>99%). Comparing DeepRF with other methods in the literature shows that DeepRF produces more reliable results than other methods.

Integrated histopathological, lipidomic, and metabolomic profiles reveal mink is a useful animal model to mimic the pathogenicity of severe COVID-19 patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted on mink farms between minks and humans in many countries. However, the systemic pathological features of SARS-CoV-2-infected minks are mostly unknown. Here, we demonstrated that minks were largely permissive to SARS-CoV-2, characterized by severe and diffuse alveolar damage, and lasted at least 14 days post inoculation (dpi). We first reported that infected minks displayed multiple organ-system lesions accompanied by an increased inflammatory response and widespread viral distribution in the cardiovascular, hepatobiliary, urinary, endocrine, digestive, and immune systems. The viral protein partially co-localized with activated Mac-2+ macrophages throughout the body. Moreover, we first found that the alterations in lipids and metabolites were correlated with the histological lesions in infected minks, especially at 6 dpi, and were similar to that of patients with severe and fatal COVID-19. Particularly, altered metabolic pathways, abnormal digestion, and absorption of vitamins, lipids, cholesterol, steroids, amino acids, and proteins, consistent with hepatic dysfunction, highlight metabolic and immune dysregulation. Enriched kynurenine in infected minks contributed to significant activation of the kynurenine pathway and was related to macrophage activation. Melatonin, which has significant anti-inflammatory and immunomodulating effects, was significantly downregulated at 6 dpi and displayed potential as a targeted medicine. Our data first illustrate systematic analyses of infected minks to recapitulate those observations in severe and fetal COVID-19 patients, delineating a useful animal model to mimic SARS-CoV-2-induced systematic and severe pathophysiological features and provide a reliable tool for the development of effective and targeted treatment strategies, vaccine research, and potential biomarkers.

Bioinformatics and in-silico findings reveal medical features and pharmacological targets of biochanin A against colorectal cancer and COVID-19.

Severe mortality due to the COVID-19 pandemic resulted from the lack of effective treatment. Although COVID-19 vaccines are available, their side effects have become a challenge for clinical use in patients with chronic diseases, especially cancer patients. In the current report, we applied network pharmacology and systematic bioinformatics to explore the use of biochanin A in patients with colorectal cancer (CRC) and COVID-19 infection. Using the network pharmacology approach, we identified two clusters of genes involved in immune response (IL1A, IL2, and IL6R) and cell proliferation (CCND1, PPARG, and EGFR) mediated by biochanin A in CRC/COVID-19 condition. The functional analysis of these two gene clusters further illustrated the effects of biochanin A on interleukin-6 production and cytokine-cytokine receptor interaction in CRC/COVID-19 pathology. In addition, pathway analysis demonstrated the control of PI3K-Akt and JAK-STAT signaling pathways by biochanin A in the treatment of CRC/COVID-19. The findings of this study provide a therapeutic option for combination therapy against COVID-19 infection in CRC patients.

Metabolic reprograming shapes neutrophil functions in severe COVID-19.

To better understand the mechanisms at the basis of neutrophil functions during SARS-CoV-2, we studied patients with severe COVID-19 pneumonia. They had high blood proportion of degranulated neutrophils and elevated plasma levels of myeloperoxidase (MPO), elastase, and MPO-DNA complexes, which are typical markers of neutrophil extracellular traps (NET). Their neutrophils display dysfunctional mitochondria, defective oxidative burst, increased glycolysis, glycogen accumulation in the cytoplasm, and increase glycogenolysis. Hypoxia-inducible factor 1α (ΗΙF-1α) is stabilized in such cells, and it controls the level of glycogen phosphorylase L (PYGL), a key enzyme in glycogenolysis. Inhibiting PYGL abolishes the ability of neutrophils to produce NET. Patients displayed significant increases of plasma levels of molecules involved in the regulation of neutrophils' function including CCL2, CXCL10, CCL20, IL-18, IL-3, IL-6, G-CSF, GM-CSF, IFN-γ. Our data suggest that metabolic remodelling is vital for the formation of NET and for boosting neutrophil inflammatory response, thus, suggesting that modulating ΗΙF-1α or PYGL could represent a novel approach for innovative therapies.

RNA-Seq Analysis Reveals an Essential Role of the Tyrosine Metabolic Pathway and Inflammation in Myopia-Induced Retinal Degeneration in Guinea Pigs.

Myopia is the second leading cause of visual impairment globally. Myopia can induce sight-threatening retinal degeneration and the underlying mechanism remains poorly defined. We generated a model of myopia-induced early-stage retinal degeneration in guinea pigs and investigated the mechanism of action. Methods: The form-deprivation-induced myopia (FDM) was induced in the right eyes of 2~3-week-old guinea pigs using a translucent balloon for 15 weeks. The left eye remained untreated and served as a self-control. Another group of untreated age-matched animals was used as naïve controls. The refractive error and ocular biometrics were measured at 3, 7, 9, 12 and 15 weeks post-FDM induction. Visual function was evaluated by electroretinography. Retinal neurons and synaptic structures were examined by confocal microscopy of immunolabelled retinal sections. The total RNAs were extracted from the retinas and processed for RNA sequencing analysis. Results: The FDM eyes presented a progressive axial length elongation and refractive error development. After 15 weeks of intervention, the average refractive power was -3.40 ± 1.85 D in the FDM eyes, +2.94 ± 0.59 D and +2.69 ± 0.56 D in the self-control and naïve control eyes, respectively. The a-wave amplitude was significantly lower in FDM eyes and these eyes had a significantly lower number of rods, secretagogin+ bipolar cells, and GABAergic amacrine cells in selected retinal areas. RNA-seq analysis showed that 288 genes were upregulated and 119 genes were downregulated in FDM retinas compared to naïve control retinas. In addition, 152 genes were upregulated and 12 were downregulated in FDM retinas compared to self-control retinas. The KEGG enrichment analysis showed that tyrosine metabolism, ABC transporters and inflammatory pathways were upregulated, whereas tight junction, lipid and glycosaminoglycan biosynthesis were downregulated in FDM eyes. Conclusions: The long-term (15-week) FDM in the guinea pig models induced an early-stage retinal degeneration. The dysregulation of the tyrosine metabolism and inflammatory pathways may contribute to the pathogenesis of myopia-induced retinal degeneration.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets.

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

The Serum Metabolome of Moderate and Severe COVID-19 Patients Reflects Possible Liver Alterations Involving Carbon and Nitrogen Metabolism.

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.

Data and Text Mining Help Identify Key Proteins Involved in the Molecular Mechanisms Shared by SARS-CoV-2 and HIV-1.

Viruses can be spread from one person to another; therefore, they may cause disorders in many people, sometimes leading to epidemics and even pandemics. New, previously unstudied viruses and some specific mutant or recombinant variants of known viruses constantly appear. An example is a variant of coronaviruses (CoV) causing severe acute respiratory syndrome (SARS), named SARS-CoV-2. Some antiviral drugs, such as remdesivir as well as antiretroviral drugs including darunavir, lopinavir, and ritonavir are suggested to be effective in treating disorders caused by SARS-CoV-2. There are data on the utilization of antiretroviral drugs against SARS-CoV-2. Since there are many studies aimed at the identification of the molecular mechanisms of human immunodeficiency virus type 1 (HIV-1) infection and the development of novel therapeutic approaches against HIV-1, we used HIV-1 for our case study to identify possible molecular pathways shared by SARS-CoV-2 and HIV-1. We applied a text and data mining workflow and identified a list of 46 targets, which can be essential for the development of infections caused by SARS-CoV-2 and HIV-1. We show that SARS-CoV-2 and HIV-1 share some molecular pathways involved in inflammation, immune response, cell cycle regulation.

A Novel Pathway Network Analytics Method Based on Graph Theory.

A biological pathway is an ordered set of interactions between intracellular molecules having collective activity that impacts cellular function, for example, by controlling metabolite synthesis or by regulating the expression of sets of genes. They play a key role in advanced studies of genomics. However, existing pathway analytics methods are inadequate to extract meaningful biological structure underneath the network of pathways. They also lack automation. Given these circumstances, we have come up with a novel graph theoretic method to analyze disease-related genes through weighted network of biological pathways. The method automatically extracts biological structures, such as clusters of pathways and their relevance, significance of each pathway and gene, and so forth hidden in the complex network. We have demonstrated the effectiveness of the proposed method on a set of genes associated with coronavirus disease 2019.

Single-Cell RNA Sequencing Analysis of the Immunometabolic Rewiring and Immunopathogenesis of Coronavirus Disease 2019.

Although immune dysfunction is a key feature of coronavirus disease 2019 (COVID-19), the metabolism-related mechanisms remain elusive. Here, by reanalyzing single-cell RNA sequencing data, we delineated metabolic remodeling in peripheral blood mononuclear cells (PBMCs) to elucidate the metabolic mechanisms that may lead to the progression of severe COVID-19. After scoring the metabolism-related biological processes and signaling pathways, we found that mono-CD14+ cells expressed higher levels of glycolysis-related genes (PKM, LDHA and PKM) and PPP-related genes (PGD and TKT) in severe patients than in mild patients. These genes may contribute to the hyperinflammation in mono-CD14+ cells of patients with severe COVID-19. The mono-CD16+ cell population in COVID-19 patients showed reduced transcription levels of genes related to lysine degradation (NSD1, KMT2E, and SETD2) and elevated transcription levels of genes involved in OXPHOS (ATP6V1B2, ATP5A1, ATP5E, and ATP5B), which may inhibit M2-like polarization. Plasma cells also expressed higher levels of the OXPHOS gene ATP13A3 in COVID-19 patients, which was positively associated with antibody secretion and survival of PCs. Moreover, enhanced glycolysis or OXPHOS was positively associated with the differentiation of memory B cells into plasmablasts or plasma cells. This study comprehensively investigated the metabolic features of peripheral immune cells and revealed that metabolic changes exacerbated inflammation in monocytes and promoted antibody secretion and cell survival in PCs in COVID-19 patients, especially those with severe disease.

Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection.

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.

Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools.

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.

Cholesterol, lipoproteins, and COVID-19: Basic concepts and clinical applications.

Cholesterol is being recognized as a molecule involved in regulating the entry of the SARS-CoV-2 virus into the host cell. However, the data about the possible role of cholesterol carrying lipoproteins and their receptors in relation to infection are scarce and the connection of lipid-associated pathologies with COVID-19 disease is in its infancy. Herein we provide an overview of lipids and lipid metabolism in relation to COVID-19, with special attention on different forms of cholesterol. Cholesterol enriched lipid rafts represent a platform for viruses to enter the host cell by endocytosis. Generally, higher membrane cholesterol coincides with higher efficiency of COVID-19 entry. Inversely, patients with COVID-19 show lowered levels of blood cholesterol, high-density lipoproteins (HDL) and low-density lipoproteins. The modulated efficiency of viral entry can be explained by availability of SR-B1 receptor. HDL seems to have a variety of roles, from being itself a scavenger for viruses, an immune modulator and mediator of viral entry. Due to inverse roles of membrane cholesterol and lipoprotein cholesterol in COVID-19 infected patients, treatment of these patients with cholesterol lowering statins needs more attention. In conclusion, cholesterol and lipoproteins are potential markers for monitoring the viral infection status, while the lipid metabolic pathways and the composition of membranes could be targeted to selectively inhibit the life cycle of the virus as a basis for antiviral therapy.

Polyunsaturated fatty acid biosynthesis pathway and genetics. implications for interindividual variability in prothrombotic, inflammatory conditions such as COVID-19✰,✰✰,☠,☠☠.

COVID-19 symptoms vary from silence to rapid death, the latter mediated by both a cytokine storm and a thrombotic storm. SARS-CoV (2003) induces Cox-2, catalyzing the synthesis, from highly unsaturated fatty acids (HUFA), of eicosanoids and docosanoids that mediate both inflammation and thrombosis. HUFA balance between arachidonic acid (AA) and other HUFA is a likely determinant of net signaling to induce a healthy or runaway physiological response. AA levels are determined by a non-protein coding regulatory polymorphisms that mostly affect the expression of FADS1, located in the FADS gene cluster on chromosome 11. Major and minor haplotypes in Europeans, and a specific functional insertion-deletion (Indel), rs66698963, consistently show major differences in circulating AA (>50%) and in the balance between AA and other HUFA (47-84%) in free living humans; the indel is evolutionarily selective, probably based on diet. The pattern of fatty acid responses is fully consistent with specific genetic modulation of desaturation at the FADS1-mediated 20:3→20:4 step. Well established principles of net tissue HUFA levels indicate that the high linoleic acid and low alpha-linoleic acid in populations drive the net balance of HUFA for any individual. We predict that fast desaturators (insertion allele at rs66698963; major haplotype in Europeans) are predisposed to higher risk and pathological responses to SARS-CoV-2 could be reduced with high dose omega-3 HUFA.