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

Omics Analyses Uncover Host Networks Defining Virus-Permissive and -Hostile Cellular States

The capacity of host cells to sustain or restrict virus infection is influenced by their proteome. Understanding the compendium of proteins defining cellular permissiveness is key to many questions in fundamental virology. Here, we apply a multi-omic approach to determine the proteins that are associated with highly permissive, intermediate, and hostile cellular states. We observed two groups of differentially regulated genes: (i) with robust changes in mRNA and protein levels and (ii) with protein/RNA discordances. While many of the latter are classified as interferon-stimulated genes (ISGs), most exhibit no antiviral effects in overexpression screens. This suggests that IFN-dependent protein changes can be better indicators of antiviral function than mRNA levels. Phosphoproteomics revealed an additional regulatory layer involving non-signaling proteins with altered phosphorylation. Indeed, we confirmed that several permissiveness-associated proteins with changes in abundance or phosphorylation regulate infection fitness. Altogether, our study provides a comprehensive and systematic map of the cellular alterations driving virus susceptibility.

Short-chain fatty acids in viral infection: the underlying mechanisms, opportunities, and challenges

Viral infections can cause cellular pathway derangements, cell death, and immunopathological responses, leading to host inflammation. Short-chain fatty acids (SCFAs), produced by the microbiota, have emerged as a potential therapeutic for viral infections due to their ability to modulate these processes. However, SCFAs have been reported to have both beneficial and detrimental effects, necessitating a comprehensive understanding of the underlying mechanisms. This review highlights the complex mechanisms underlying SCFAs' effects on viral infection outcomes. We also emphasize the importance of considering how SCFAs' activities may differ under diverse contexts, including but not limited to target cells with different metabolic wiring, different viral causes of infection, the target organism/cell's nutrient availability and/or energy balance, and hosts with varying microbiome compositions.

Feline Calicivirus Infection Manipulates Central Carbon Metabolism

Viruses can manipulate the host metabolism to achieve optimal replication conditions, and central carbon metabolism (CCM) pathways are often crucial in determining viral infections. Feline calicivirus (FCV), a diminutive RNA viral agent, induces upper respiratory tract infections in feline hosts, with highly pathogenic strains capable of precipitating systemic infections and subsequent host cell necrosis, thereby presenting a formidable challenge to feline survival and protection. However, the relationship between FCV and host cell central carbon metabolism (CCM) remains unclear, and the precise pathogenic mechanisms of FCV are yet to be elucidated. Upon FCV infection of Crandell-Rees Feline Kidney (CRFK) cells, an enhanced cellular uptake of glucose and glutamine was observed. Metabolomics analyses disclosed pronounced alterations in the central carbon metabolism of the infected cells. FCV infection was found to augment glycolytic activity while sustaining the tricarboxylic acid (TCA) cycle flux, with cellular ATP levels remaining invariant. Concurrently, both glutamine metabolism and the flux of the pentose phosphate pathway (PPP) were noted to be intensified. The application of various inhibitory agents targeting glycolysis, glutamine metabolism, and the PPP resulted in a significant suppression of FCV proliferation. Experiments involving glucose and glutamine deprivation demonstrated that the absence of either nutrient markedly curtailed FCV replication. Collectively, these findings suggest a critical interplay between central carbon metabolism and FCV proliferation. FCV infection stimulates CRFK cells to augment glucose and glutamine uptake, thereby supplying the necessary metabolic substrates and energy for viral replication. During the infection, glutamine emerges as the primary energy substrate, ensuring ATP production and energy homeostasis, while glucose is predominantly channeled into the pentose phosphate pathway to facilitate nucleotide synthesis.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Valuable Contributions and Lessons Learned from Proteomics and Metabolomics Studies of COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus infected more than 775,686,716 humans and was responsible for the death of more than 7,054,093 individuals. COVID-19 has taught us that the development of vaccines, repurposing of drugs, and understanding the mechanism of a disease can be done within a short time. The COVID-19 proteomics and metabolomics has contributed to its diagnosis, understanding of its progression, host-virus interaction, disease mechanism, and also in the search of suitable anti-COVID therapeutics. Mass spectrometry based proteomics was used to find the potential biomarkers of different stages of COVID-19 including severe and nonsevere cases in the blood serum. Notably, protein-protein interaction techniques to understand host-virus interactions were also significantly useful. The single-cell proteomics studies were carried out to ascertain the changes in immune cell composition and its activation in mild COVID-19 patients versus severe COVID-19 patients using whole-blood and peripheral-blood mononuclear cells. Modern technologies were helpful to deal with the pandemic; however, there is still scope for further development. Further, attempts were made to understand the protein-protein, metabolite-metabolite, and protein-metabolite interactomes, derived from proteins and metabolite fingerprints of COVID-19 patients by reanalysis of COVID-19 public mass spectrometry based proteomics and metabolomics studies. Further, some of these interactions were supported by the literature as validations in the COVID-19 studies.

System-level integrative omics analysis to identify the virus-host immunometabolic footprint during infection

The emergence and re-emergence of infectious diseases present significant global health threats. Understanding their pathogenesis is crucial for developing diagnostics, therapeutics, and preventive strategies. System-level integrative omics analysis offers a comprehensive approach to deciphering virus-host immunometabolic interactions during infections. Multi-omics approaches, integrating genomics, transcriptomics, proteomics, and metabolomics, provide holistic insights into disease mechanisms, host-pathogen interactions, and immune responses. The interplay between the immune system and metabolic processes, termed immunometabolism, has gained attention, particularly in infectious diseases. Immunometabolic studies reveal how metabolic processes regulate immune cell function, shaping immune responses and influencing infection outcomes. Metabolic reprogramming is crucial for immune cell activation, differentiation, and function. Using systems biological algorithms to understand the immunometabolic alterations can provide a holistic view of immune and metabolic pathway interactions, identifying regulatory nodes and predicting responses to perturbations. Understanding these pathways enhances the knowledge of immune regulation and offers avenues for therapeutic interventions. This review highlights the contributions of multi-omics systems biology studies in understanding infectious disease pathogenesis, focusing on RNA viruses. The integrative approach enables personalized medicine strategies, considering individual metabolic and immune variations. Leveraging these interdisciplinary approaches promises advancements in combating RNA virus infections and improving health outcomes, highlighting the transformative impact of multi-omics technologies in infectious disease research.

Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10

Antiviral signaling, immune response and cell metabolism are dysregulated by SARS-CoV-2, the causative agent of COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes with critical roles in mitochondrial function and morphology. On the other hand, all four ORFs altered mitochondrial dynamics and function, but only ORF3a and ORF9c induced a marked alteration in mitochondrial cristae structure. Genome-Scale Metabolic Models identified both metabolic flux reprogramming features both shared across all accessory proteins and specific for each accessory protein. Notably, a downregulated amino acid metabolism was observed in ORF9b, ORF9c and ORF10, while an upregulated lipid metabolism was distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

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.

Whole transcriptome analysis in offspring whose fathers were exposed to a developmental insult: a novel avian model

Although the effects of paternal exposure to insults on the offspring received limited attention in the past, it is currently gaining interest especially after understanding the mechanisms which may mediate such exposure effects. In the current study, the well-controlled avian model (Fayoumi) was utilized to investigate the effects of paternal exposure to the developmental insult, chlorpyrifos on the offspring's gene expression via mRNA and small RNA sequencing. Numerous mRNA gene expression changes were detected in the offspring after paternal exposure to the developmental insult, especially in genes related to neurogenesis, learning and memory. qPCR analysis of several genes, that were significantly changed in mRNA sequencing, confirmed the results obtained in mRNA sequencing. On the other hand, small RNA sequencing did not identify significant microRNA genes expression changes in the offspring after paternal exposure to the developmental insult. The effects of the paternal exposure were more pronounced in the female offspring compared to the male offspring. The results identified expression alterations in major genes (some of which were pertinent to the functional changes observed in other forms of early developmental exposure) after paternal insult exposure and provided a direction for future studies involving the most affected genes.

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Cellular metabolism hijacked by viruses for immunoevasion: potential antiviral targets

Cellular metabolism plays a central role in the regulation of both innate and adaptive immunity. Immune cells utilize metabolic pathways to modulate the cellular differentiation or death. The intricate interplay between metabolism and immune response is critical for maintaining homeostasis and effective antiviral activities. In recent years, immunometabolism induced by viral infections has been extensively investigated, and accumulating evidence has indicated that cellular metabolism can be hijacked to facilitate viral replication. Generally, virus-induced changes in cellular metabolism lead to the reprogramming of metabolites and metabolic enzymes in different pathways (glucose, lipid, and amino acid metabolism). Metabolic reprogramming affects the function of immune cells, regulates the expression of immune molecules and determines cell fate. Therefore, it is important to explore the effector molecules with immunomodulatory properties, including metabolites, metabolic enzymes, and other immunometabolism-related molecules as the antivirals. This review summarizes the relevant advances in the field of metabolic reprogramming induced by viral infections, providing novel insights for the development of antivirals.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets' inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

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.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Reconstructed Genome-Scale Metabolic Model Characterizes Adaptive Metabolic Flux Changes in Peripheral Blood Mononuclear Cells in Severe COVID-19 Patients

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses a mortal threat to human health. The elucidation of the relationship between peripheral immune cells and the development of inflammation is essential for revealing the pathogenic mechanism of COVID-19 and developing related antiviral drugs. The immune cell metabolism-targeting therapies exhibit a desirable anti-inflammatory effect in some treatment cases. In this study, based on differentially expressed gene (DEG) analysis, a genome-scale metabolic model (GSMM) was reconstructed by integrating transcriptome data to characterize the adaptive metabolic changes in peripheral blood mononuclear cells (PBMCs) in severe COVID-19 patients. Differential flux analysis revealed that metabolic changes such as enhanced aerobic glycolysis, impaired oxidative phosphorylation, fluctuating biogenesis of lipids, vitamins (folate and retinol), and nucleotides played important roles in the inflammation adaptation of PBMCs. Moreover, the main metabolic enzymes such as the solute carrier (SLC) family 2 member 3 (SLC2A3) and fatty acid synthase (FASN), responsible for the reactions with large differential fluxes, were identified as potential therapeutic targets. Our results revealed the inflammation regulation potentials of partial metabolic reactions with differential fluxes and their metabolites. This study provides a reference for developing potential PBMC metabolism-targeting therapy strategies against COVID-19.

Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication

We aimed at investigating host-virus co-metabolism during SARS-CoV-2 infection. Therefore, we extended comprehensive sex-specific, whole-body organ resolved models of human metabolism with the necessary reactions to replicate SARS-CoV-2 in the lung as well as selected peripheral organs. Using this comprehensive host-virus model, we obtained the following key results: 1. The predicted maximal possible virus shedding rate was limited by isoleucine availability. 2. The supported initial viral load depended on the increase in CD4+ T-cells, consistent with the literature. 3. During viral infection, the whole-body metabolism changed including the blood metabolome, which agreed well with metabolomic studies from COVID-19 patients and healthy controls. 4. The virus shedding rate could be reduced by either inhibition of the guanylate kinase 1 or availability of amino acids, e.g., in the diet. 5. The virus variants differed in their maximal possible virus shedding rates, which could be inversely linked to isoleucine occurrences in the sequences. Taken together, this study presents the metabolic crosstalk between host and virus and emphasises the role of amino acid metabolism during SARS-CoV-2 infection, in particular of isoleucine. As such, it provides an example of how computational modelling can complement more canonical approaches to gain insight into host-virus crosstalk and to identify potential therapeutic strategies.

Oxidoreduction potential controlling for increasing the fermentability of enzymatically hydrolyzed steam-exploded corn stover for butanol production

Background

Lignocellulosic biomass is recognized as an effective potential substrate for biobutanol production. Though many pretreatment and detoxification methods have been set up, the fermentability of detoxicated lignocellulosic substrate is still far lower than that of starchy feedstocks. On the other hand, the number of recent efforts on rational metabolic engineering approaches to increase butanol production in Clostridium strains is also quite limited, demonstrating the physiological complexity of solventogenic clostridia. In fact, the strain performance is greatly impacted by process control. developing efficient process control strategies could be a feasible solution to this problem.

Results

In this study, oxidoreduction potential (ORP) controlling was applied to increase the fermentability of enzymatically hydrolyzed steam-exploded corn stover (SECS) for butanol production. When ORP of detoxicated SECS was controlled at − 350 mV, the period of fermentation was shortened by 6 h with an increase of 27.5% in the total solvent (to 18.1 g/L) and 34.2% in butanol (to 10.2 g/L) respectively. Silico modeling revealed that the fluxes of NADPH, NADH and ATP strongly differed between the different scenarios. Quantitative analysis showed that intracellular concentrations of ATP, NADPH/NADP⁺, and NADH/NAD⁺ were increased by 25.1%, 81.8%, and 62.5%. ORP controlling also resulted in a 2.1-fold increase in butyraldehyde dehydrogenase, a 1.2-fold increase in butanol dehydrogenase and 29% increase in the cell integrity.

Conclusion

ORP control strategy effectively changed the intracellular metabolic spectrum and significantly improved Clostridium cell growth and butanol production. The working mechanism can be summarized into three aspects: First, Glycolysis and TCA circulation pathways were strengthened through key nodes such as pyruvate carboxylase [EC: 6.4.1.1], which provided sufficient NADH and NADPH for the cell. Second, sufficient ATP was provided to avoid “acid crash”. Third, the key enzymes activities regulating butanol biosynthesis and cell membrane integrity were improved.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01824-2.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC 50 ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

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.

Synthetic lethality-based prediction of anti-SARS-CoV-2 targets

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal and synthetic dosage lethal (SL/SDL) partners of such altered host genes. Pursuing this disparate antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL/SDL with altered host genes. The predicted SL/SDL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. We further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming noninfected healthy cells.

Synergistic Effects of Multiple Factors Involved in COVID-19-dependent Muscle Loss

The COVID-19 pandemic caused by the novel SARS-CoV-2 coronavirus is an ongoing pandemic causing severe health crisis worldwide. Recovered COVID-19 patients go through several long-term side effects such as fatigue, headaches, dizziness, weight loss, and muscle loss among others. Our study sought to determine the molecular mechanisms behind muscle loss in COVID-19 patients. We hypothesized that multiple factors such as cytokine storm and therapeutic drugs (glucocorticoid and antiviral drugs) might be involved in muscle loss. Using the Gene Expression Omnibus database, we identified several studies that performed RNA sequencing on skeletal muscles with the treatment of cytokine, glucocorticoid, and antiviral drugs. Our study identified cytokines, such as IL-1b, and IL-6, associated with altered regulation of several genes involved in the myogenic processes, including Ttn, Cxxc5, Malat1, and Foxo1. We also observed that glucocorticoid altered the expression of Foxo1, Lcn2, Slc39a14, and Cdkn1a. Finally, we found out that the antiviral (RNA-dependent RNA polymerase inhibitor) drug regulates the expression of some of the muscle-related genes (Txnip, Ccnd1, Hdac9, and Fbxo32). Based on our findings, we hypothesize that the cytokine storm, glucocorticoids, and antiviral drugs might be synergistically involved in COVID-19-dependent muscle loss.