Omics-based technologies have been largely adopted during this unprecedented global COVID-19 pandemic, allowing the scientific community to perform research on a large scale to understand the pathobiology of the SARS-CoV-2 infection and its replication into human cells. The application of omics techniques has been addressed to every level of application, from the detection of mutations, methods of diagnosis or monitoring, drug target discovery, and vaccine generation, to the basic definition of the pathophysiological processes and the biochemical mechanisms behind the infection and spread of SARS-CoV-2. Thus, the term COVIDomics wants to include those efforts provided by omics-scale investigations with application to the current COVID-19 research. This review summarizes the diverse pieces of knowledge acquired with the application of COVIDomics techniques, with the main focus on proteomics and metabolomics studies, in order to capture a common signature in terms of proteins, metabolites, and pathways dysregulated in COVID-19 disease. Exploring the multiomics perspective and the concurrent data integration may provide new suitable therapeutic solutions to combat the COVID-19 pandemic.

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COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19

Plenty of research has revealed virus induced alternations in metabolic pathways, which is known as metabolic reprogramming. Studies focusing on COVID-19 have uncovered significant changes in metabolism, resulting in the perspective that COVID-19 is a metabolic disease. Reprogramming of amino acid, glucose, cholesterol and fatty acid is distinctive characteristic of COVID-19 infection. These metabolic changes in COVID-19 have a critical role not only in producing energy and virus constituent elements, but also in regulating immune response, offering new insights into COVID-19 pathophysiology. Remarkably, metabolic reprogramming provides great opportunities for developing novel biomarkers and therapeutic agents for COVID-19 infection. Such novel agents are expected to be effective adjuvant therapies. In this review, we integrate present studies about major metabolic reprogramming in COVID-19, as well as the possibility of targeting reprogrammed metabolism to combat virus infection.

Metabolic Reprogramming in COVID-19.

Since the identification of SARS-CoV-2 at the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic has affected more than 410 million people worldwide and killed almost 6 million [1, 2]. The predecessors of COVID-19, i.e. the SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) outbreaks, had been relatively self-limiting, preventing clinicians and researchers from establishing evidence-based specific therapeutic strategies [3]. Conversely, COVID-19 rapidly proved to be extremely fast spreading, which led stakeholders to encourage, guide, build or fund multidirectional therapeutic research strategies based on both repurposing and development of new agents [4–8]. In parallel, considerable efforts were directed at describing the disease and understanding the underlying mechanisms [9–13]. As a result, there has been a huge generation of evidence, as highlighted by the impressive number of COVID-19 publications (more than 200 000 since the end of 2019). As a consequence, it proved rapidly impossible for any clinician, researcher or decision-maker to gather and analyse all the corresponding literature to derive appropriate guidance [14]. The first step of such a process is to select the relevant high-quality research that can be used to answer the question(s) of interest [15]. Even if limiting the search to clinical trials, systematic reviews and meta-analyses, almost 4000 papers appear in the PubMed database, as of mid-February 2022. In June and July 2020, the European Respiratory Society (ERS) and the American Thoracic Society (ATS) released early guidance on several aspects of COVID-19 management (i.e. rehabilitation, palliative care and acute management); at that time, direct specific evidence was sparse or absent [16–18]. Simultaneously, the ERS launched a living guideline on the management of COVID-19. The format was that of a “short” guideline, as per ERS standards [19, 20], in that the purpose was to release the first iteration within 12 months. However, the number of PICO (Population, Intervention, Comparator, Outcomes) questions to be addressed (n=12) already exceeded markedly what the ERS considers as being feasible during such a short timeframe (i.e. n=1–2), which was a direct consequence of the high number of unanswered issues in the field of acute COVID-19 management, all corresponding to outstanding clinical needs. The first version of these guidelines was published in March 2021 and addressed the following potential therapeutic options: corticosteroids, interleukin (IL)-6 receptor antagonists, hydroxychloroquine, azithromycin and both combined, colchicine, lopinavir-ritonavir, remdesivir, interferon-β, anticoagulation and non-invasive ventilatory support [6, 21]. An update of the mortality meta-analyses for corticosteroids, hydroxychloroquine, azithromycin, remdesivir, anti-IL-6 monoclonal antibodies, colchicine, lopinavir/ritonavir and interferon-β was published in December 2021 [22]. The ERS COVID-19 guidelines make recommendations for corticosteroids, anti-IL-6 monoclonal antibodies, baricitinib, anticoagulation and non-invasive respiratory support for hospitalised patients with COVID-19 https://bit.ly/3QgHH7U

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Update June 2022: management of hospitalised adults with coronavirus disease 2019 (COVID-19): a European Respiratory Society living guideline

Coronavirus disease 2019 (COVID-19) has so far infected hundreds of million individuals, with several million deaths worldwide. The lack of understanding of the disease pathophysiology and the host's immune response has resulted in this rapid spread of the disease on a global scale. In this respect, we employed UPLC-MS to compare the metabolites in the serum from COVID-19-positive patients and COVID-19-recovered subjects to determine the metabolic changes responsible for an infection. Our investigations revealed significant increase in the levels of serum phospholipids including sphingomyelins, phosphatidylcholines and arachidonic acid in the serum of COVID-19-positive patients as compared to COVID-19-recovered individuals. We further show increased levels of tryptophan and its metabolites in the serum of COVID-19-positive patients thus emphasizing the role of tryptophan metabolism in the disease pathogenesis of COVID-19. Future studies are required to determine the changes in the lipid and tryptophan metabolism at various stages of COVID-19 disease development, progression and recovery to better understand the host-pathogen interaction and the long-term effects of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection in humans.

SARS-CoV2 Infection Alters Tryptophan Catabolism and Phospholipid Metabolism.

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities. 

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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

The current pandemic of coronavirus disease 19 (COVID-19) has affected more than 160 million of individuals and caused millions of deaths worldwide at least in part due to the unclarified pathophysiology of this disease. Therefore, identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual by acquiring extensive insights into the pathophysiological significance during disease progression. We provide a comprehensive view of metabolic characterization of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both discovery cohort and validation cohort, which suggest that metabolic reprogramming of glucose metabolism and urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and urea cycle may be a viable approach to fight against COVID-19 at various stages along the disease course.

https://erj.ersjournals.com/content/erj/early/2021/07/08/13993003.00284-2021.full.pdf

Metabolomic analyses reveals new stage-specific features of the COVID-19.

Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs.

Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children.

Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B-precursor ALL patients by gene expression profiles, using non-negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse-free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3-PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high-relapse-risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B-precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cam4.3842

Huiying Liang

Papers

Metabolomic analyses reveals new stage-specific features of the COVID-19.

Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children.

Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia.