Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Michele Costanzo
 + 2162 word(s) 2162 2022-03-12 09:36:52 |
2 Format correct
Vicky Zhou
 Meta information modification 2162 2022-03-14 11:10:43 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Costanzo, M. COVIDomics. Encyclopedia. Available online: https://encyclopedia.pub/entry/20542 (accessed on 06 July 2024).
Costanzo M. COVIDomics. Encyclopedia. Available at: https://encyclopedia.pub/entry/20542. Accessed July 06, 2024.
Costanzo, Michele. "COVIDomics" Encyclopedia, https://encyclopedia.pub/entry/20542 (accessed July 06, 2024).
Costanzo, M. (2022, March 14). COVIDomics. In Encyclopedia. https://encyclopedia.pub/entry/20542
Costanzo, Michele. "COVIDomics." Encyclopedia. Web. 14 March, 2022.
COVIDomics
Edit
This entry is adapted from the peer-reviewed paper 10.3390/ijms23052414

COVIDomics, namely, the proteomic and metabolomic signatures of COVID-19. Omics-based technologies have been largely adopted during the 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 COVID-19 research. 

COVID-19 SARS-CoV-2 COVIDomics proteomics metabolomics multiomics COVID-19 signature data integration pandemic

1. Introduction

In the actual background of the world coronavirus disease 2019 (COVID-19) pandemic, scientists and researchers worldwide have made significant efforts to unravel the clinical and molecular aspects regarding the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the progression of COVID-19 disease. Meanwhile, many attempts were simultaneously made to disclose the cellular and pathophysiological processes affected in COVID-19 patients; the urgent need for pharmacological treatments prompted a rapid approach to drug repurposing and vaccines’ development, to limit the number of deaths and the spread of the infection [1][2][3][4][5]. Classification of COVID-19 disease can be made according to the clinical characteristics of patients (Figure 1). According to the high variability and heterogeneity of symptoms and comorbidities [6], patients can be categorized as symptomatic or asymptomatic, and a crescent grade of severity is usually referred to as mild, moderate, or severe. The outcome of COVID-19 patients defines their status, classifying them as hospitalized, needing intensive care unit (ICU), or fatal. Very often, even after recovery, many patients can experience post-acute COVID syndrome (PACS), during which symptoms may last for several months [7]. The World Health Organization (WHO) has organized a system score for clinical improvement and management of COVID-19 disease, based on an ordinal scale to categorize patients according to their clinical manifestations [8].
Figure 1. Application and integration of omics technologies to characterize the molecular biology of SARS-CoV-2 and COVID-19 pathogenic mechanisms and therapeutic approaches, with the main focus on proteomics and metabolomics investigations. This figure was drawn adapting the vector image from the Servier Medical Art bank (http://smart.servier.com/; last accessed 2 January 2022). COVID-19 = coronavirus disease 2019; SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2; ICU = intensive care unit; PACS = post-acute COVID syndrome.
In the fight against the COVID-19 pandemic, single- and multiomics-based strategies have been largely adopted with the main aim of dissecting a plethora of aspects related to the SARS-CoV-2 infection. In fact, omics-based technologies, such as genomics, transcriptomics, proteomics, and metabolomics, can serve at every stage of COVID-19 investigations, from diagnosis and progression of the disease, to the discovery of altered molecular pathways or potential drug targets and vaccines (Figure 1).
Genomics and transcriptomics have suitably allowed to promptly identify in diverse biological matrices the mutations of SARS-CoV-2, including those responsible for the occurrence of new variants, and the changes in the coronavirus genes expression [9][10][11][12]. On the other hand, the value of proteomics and metabolomics concerns the possibility of understanding the functional alterations that depend on virus infection and its interaction with the host cell. In fact, with the increasing advance and the high throughput provided by liquid chromatography—tandem mass spectrometry (LC-MS/MS) technologies in biomedicine, the identification and quantification of several thousands of proteins and metabolites is possible in a targeted or untargeted fashion, or using the newest data-independent acquisition (DIA)-MS methods [13][14][15][16], besides the traditional data-dependent acquisition (DDA) ones [17][18][19][20][21]. With such sophisticated techniques, many aspects of the pathogenesis of COVID-19 can be disclosed, acquiring an important piece of knowledge to understand the molecular processes perturbed by virus infection and to predict or ameliorate the clinical outcome of patients by finding promising druggable targets. On this matter, even out of the scope here, an interactomics study performed in human cells using affinity-purification mass spectrometry found several interacting partners in 26 out of the 29 proteins of SARS-CoV-2, concluding that some of these interactors were druggable proteins [22].
In parallel, the introduction of a metabolomic workflow in a work provides a rich source of information, allowing the phenotypic biochemical and metabolic characterization of cells, tissues, or body fluids, and discovering and monitoring biomarkers or disease signatures [23][24][25]. What is more, the challenging advances in mass spectrometry and metabolomics research provided scientists with a powerful tool able to technically separate the analysis of small molecules from lipid molecules, diving into the deep of the metabolome and the lipidome, respectively [26][27]. Lipids represent the structural building blocks of cell and virus membranes, thus playing an essential role for the virus life cycle, including viral invasion and replication. Since viruses are able to modulate lipid synthesis and signaling into the host cell to produce lipids for their envelopes by creating a lipid micro-environment ideal for replication, the lipid dysregulation is suggested as a drug target to prevent coronavirus infection [28]. Furthermore, considering the high heterogeneity of the clinical manifestations of COVID-19, thus suggesting the involvement of diverse pathophysiological processes [29], multiomics approaches and data integration analyses may result as a winning choice, contributing to a better understanding of the molecular biology of SARS-CoV-2 at every level of complexity [30].
To unify all the efforts made by scientists at the omics-scale level in the research to combat the current pandemic and provide a comprehensive overview of the omics studies made in favor of COVID-19 research, here used the term COVIDomics. Hence, herein summarized the COVIDomics discoveries mainly based on proteomics and metabolomics studies on blood samples from patients. Many overlapping results were found in several publications from different authors for both proteomics and metabolomics applications. The relevant findings of such investigations were highlighted and, finally, summarized and analyzed with bioinformatics tools in order to capture a common signature in terms of proteins, metabolites, and pathways dysregulated in COVID-19 disease.

2. COVIDomics Data Integration

The application of omics and multiomics technologies, enclosed here under the term COVIDomics, offers the great chance to meticulously recognize and dissect the multiple molecular aspects that characterize a complex disease such as COVID-19. The majority of patients that contract the SARS-CoV-2 infection may develop no or mild symptoms, although many others experience a severe or critical symptomatology leading to fatal cases. This high heterogeneity in the clinical manifestation is being investigated in many studies, comprising those here concluded. In fact, many comparisons investigate the differences between the severe disease toward the baseline proteome or metabolome in healthy patients; others focus mostly on the progression of the disease from a condition to another one and highlight correlations of clinical and biochemical parameters with dysregulated proteins and metabolites. Furthermore, the majority of the COVIDomics research was performed on blood, as it is an easily accessible liquid biopsy reflecting the systemic changes subsequent the strong infection of SARS-CoV-2. Thus, the comprehensive and accurate analysis of circulating molecules is helpful to unravel the systemic mechanisms of the disease. In fact, as summarized in Figure 2 and detailed in Table 4, there are several common proteins that were identified in plasma and serum as quantitatively changed between the patients and their controls, representing a relevant proteomic signature of COVID-19.
Figure 2. The main findings obtained from proteomics studies are summarized. In particular, the results from plasma [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] and serum [46][47][48][49][50][51][52] studies were merged to identify the common proteins (top) that should represent the proteome signature of COVID-19. These protein entries were analyzed and clustered using STRING version 11.5, revealing the formation of three main clusters (bottom). The relative biological processes (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were indicated. A detailed list for the proteins depicted in this figure is available in Table 1.
Table 1. The list of the proteins identified in more than one study characterizing the blood proteomic signature of COVID-19.
Protein Symbol UniProt ID Protein Name STRING Cluster
A2M P01023 Alpha-2-macroglobulin Cluster 1
ACTB P60709 Actin, cytoplasmic 1
AHSG P02765 Alpha-2-HS-glycoprotein
ALB P02768 Albumin
C1R P00736 Complement C1r subcomponent
C5 P01031 Complement C5
CFB P00751 Complement Factor B
CFH P08603 Complement factor H
CFI P05156 Complement factor I
CRP P02741 C-reactive protein
CST3 P01034 Cystatin-C
CTSB P07858 Cathepsin B
CTSL P07711 Procathepsin L
F9 P00740 Coagulation factor IX
F10 P00742 Coagulation factor X
F12 P00748 Coagulation factor XII
F13B P05160 Coagulation factor XIII B chain
FGA P02671 Fibrinogen alpha chain
FGG P02679 Fibrinogen gamma chain
GSN P06396 Gelsolin
HRG P04196 Histidine-rich glycoprotein
HSPA8 P11142 Heat shock cognate 71 kDa protein
ITIH4 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4
KLKB1 P03952 Plasma kallikrein
KNG1 P01042 Kininogen-1
LGALS3BP Q08380 Galectin-3-binding protein
LRG1 P02750 Leucine-rich alpha-2-glycoprotein
MPO P05164 Myeloperoxidase
ORM1 P02763 Alpha-1-acid glycoprotein 1
PIGR P01833 Polymeric immunoglobulin receptor
PLG P00747 Plasminogen
PRG4 Q92954 Proteoglycan 4
PROS1 P07225 Vitamin K-dependent protein S
SERPINA1 P01009 Alpha-1-antitrypsin
SERPINA3 P01011 Alpha-1-antichymotrypsin
SERPINA10 Q9UK55 Protein Z-dependent protease inhibitor
SERPINC1 P01008 Antithrombin-III
SERPINF2 P08697 Alpha-2-antiplasmin
TF P02787 Transferrin
TTR P02766 Transthyretin
VIM P08670 Vimentin
CCL2 P13500 C-C motif chemokine 2 Cluster 2
CCL7 P80098 C-C motif chemokine 7
CCL8 P80075 C-C motif chemokine 8
CD14 P08571 Monocyte differentiation antigen CD14
CCL23 P55773 C-C motif chemokine 23
CD274 Q9NZQ7 Programmed cell death 1 ligand 1
CHI3L1 P36222 Chitinase-3-like protein 1
CXCL10 P02778 C-X-C motif chemokine 10
CXCL11 O14625 C-X-C motif chemokine 11
DEFA1 P59665 Neutrophil defensin 1
HGF P14210 Hepatocyte growth factor
IL-10 P22301 Interleukin-10
IL-18R1 Q13478 Interleukin-18 receptor 1
IL-6 P08887 Interleukin-6 receptor subunit alpha
LBP P18428 Lipopolysaccharide-binding protein
LCN2 P80188 Neutrophil gelatinase-associated lipocalin
LGALS9 O00182 Galectin-9
S100A11 P31949 Protein S100-A11
S100A12 P80511 Protein S100-A12
S100A8 P05109 Protein S100-A8
S100A9 P06702 Protein S100-A9
SAA1 P0DJI8 Serum amyloid A-1 protein
TGFB1 P01137 Transforming growth factor beta-1 proprotein
TNF P01375 Tumor necrosis factor
VEGFA P15692 Vascular endothelial growth factor A
APOA1 P02647 Apolipoprotein A1 Cluster 3
APOA2 P02652 Apolipoprotein A2
APOC1 P02654 Apolipoprotein C1
APOC3 P02656 Apolipoprotein C3
APOD P05090 Apolipoprotein D
APOL1 O14791 Apolipoprotein L1
APOM O95445 Apolipoprotein M
C8A P07357 Complement component C8 alpha chain
CETP P11597 Cholesteryl ester transfer protein
CFHR5 Q9BXR6 Complement factor H-related protein 5
FGB P02675 Fibrinogen beta chain
IGFALS P35858 Insulin-like growth factor-binding protein complex acid labile subunit
ITIH3 Q06033 Inter-alpha-trypsin inhibitor heavy chain H3
PI16 Q6UXB8 Peptidase inhibitor 16
SAA2 P0DJI9 Serum amyloid A-2 protein
SAA4 P35542 Serum amyloid A-4 protein
SCARB2 Q14108 Lysosome membrane protein 2
Proteins were ordered alphabetically for each cluster according to the protein symbol.
As for proteins, herein have also summarized the common findings obtained analyzing metabolomics studies in terms of metabolites and metabolic pathways highlighted in plasma and serum from COVID-19 patients (Figure 3). In particular, herein have focused the analysis on small molecules, since metabolomics software only recognize well-annotated HMDB (Human Metabolome Database) compounds, and lipids may be not correctly mapped. Indeed, the enrichment analysis of the metabolites associated with COVID-19 was performed using MetaboAnalyst 5.0 software [53][54][55][56].
Figure 3. The common metabolites and metabolic pathways obtained from the conclusion of plasma and serum metabolomics studies are summarized. A specific signature of the metabolome of COVID-19 patients was obtained representing the most enriched pathways constructed through an over-representation analysis (ORA) within MetaboAnalyst 5.0 and represented as bubble plot. The bubble plot takes into account the statistical significance (–log p-value) of each metabolite set identified through the ORA analysis. Where the size of each bubble refers to the number of times the metabolite set is cited here by each scientist, the color instead refers to the number of metabolites detected over the total number of metabolites within that pathway (occupancy). On the right, the metabolites common to all the concluded papers that have generated the pathway enrichment are grouped.

3. Concluding Remarks

During the current COVID-19 pandemic, unprecedented efforts have been made by the scientific community to dissect the molecular bases of SARS-CoV-2 infection, spreading, and pathogenicity. Omics scientists deserved particular merits for performing several proteomics- and metabolomics-based investigations, but also integrative multiomics analyses, using samples derived from patients. Thus, the application of COVIDomics strategies has certainly empowered the knowledge relative to COVID-19 in terms of biomarkers of disease and specific physiological mechanisms disturbed upon SARS-CoV-2 infection. The study of coronavirus structure, replication, and infection, together with the virus-host interaction and the host response, have remarkably provided us with efficient tools to diagnose the disease and ameliorate the fate of COVID-19 patients. In general, a huge improvement in patients’ outcomes is correlated to the development of vaccines as prophylactic therapeutic approaches, despite it remaining not currently possible to foresee the prognosis of infected patients by a single analysis or through the detection of a single and specific marker. Nonetheless, in this scenario, the use of omics sciences and their integration have provided us with relevant proteomics and metabolomics signatures, as a point of junction between SARS-CoV-2 infection/pathobiology with the subsequent clinical outcome of affected patients. Thus, COVIDomics is contributing to a better comprehension of the molecular intricacy of COVID-19, in the view of identifying new molecular actors involved in the pathogenesis of the disease to be used for efficient therapeutic approaches in combatting the COVID-19 pandemic.

References

  1. Costanzo, M.; De Giglio, M.A.R.; Roviello, G.N. Anti-Coronavirus Vaccines: Past Investigations on SARS-CoV-1 and MERS-CoV, the Approved Vaccines from BioNTech/Pfizer, Moderna, Oxford/AstraZeneca and others under Development Against SARS-CoV- 2 Infection. Curr. Med. Chem. 2022, 29, 4–18.
  2. De Giglio, M.A.R.; Roviello, G.N. SARS-CoV-2: Recent Reports on Antiviral Therapies Based on Lopinavir/Ritonavir, Darunavir/Umifenovir, Hydroxychloroquine, Remdesivir, Favipiravir and other Drugs for the Treatment of the New Coronavirus. Curr. Med. Chem. 2020, 27, 4536–4541.
  3. Rehman, S.U.; Rehman, S.U.; Yoo, H.H. COVID-19 challenges and its therapeutics. Biomed. Pharmacother. 2021, 142, 112015.
  4. Singh, T.U.; Parida, S.; Lingaraju, M.C.; Kesavan, M.; Kumar, D.; Singh, R.K. Drug repurposing approach to fight COVID-19. Pharmacol. Rep. 2020, 72, 1479–1508.
  5. Borbone, N.; Piccialli, G.; Roviello, G.N.; Oliviero, G. Nucleoside Analogs and Nucleoside Precursors as Drugs in the Fight against SARS-CoV-2 and Other Coronaviruses. Molecules 2021, 26, 986.
  6. Xu, Z.; Shi, L.; Wang, Y.; Zhang, J.; Huang, L.; Zhang, C.; Liu, S.; Zhao, P.; Liu, H.; Zhu, L.; et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir. Med. 2020, 8, 420–422.
  7. Nalbandian, A.; Sehgal, K.; Gupta, A.; Madhavan, M.V.; McGroder, C.; Stevens, J.S.; Cook, J.R.; Nordvig, A.S.; Shalev, D.; Sehrawat, T.S.; et al. Post-acute COVID-19 syndrome. Nat. Med. 2021, 27, 601–615.
  8. Marshall, J.C.; Murthy, S.; Diaz, J.; Adhikari, N.K.; Angus, D.C.; Arabi, Y.M.; Baillie, K.; Bauer, M.; Berry, S.; Blackwood, B.; et al. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect. Dis. 2020, 20, e192–e197.
  9. Zhang, Y.-Z.; Holmes, E.C. A Genomic Perspective on the Origin and Emergence of SARS-CoV-2. Cell 2020, 181, 223–227.
  10. Naqvi, A.A.T.; Fatima, K.; Mohammad, T.; Fatima, U.; Singh, I.K.; Singh, A.; Atif, S.M.; Hariprasad, G.; Hasan, G.M.; Hassan, I. Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach. Biochim. Biophys. Acta (BBA) Mol. Basis Dis. 2020, 1866, 165878.
  11. Bell, L.C.; Meydan, C.; Kim, J.; Foox, J.; Butler, D.; Mason, C.E.; Shapira, S.D.; Noursadeghi, M.; Pollara, G. Transcriptional response modules characterize IL-1β and IL-6 activity in COVID-19. iScience 2021, 24, 101896.
  12. Fagone, P.; Ciurleo, R.; Lombardo, S.D.; Iacobello, C.; Palermo, C.I.; Shoenfeld, Y.; Bendtzen, K.; Bramanti, P.; Nicoletti, F. Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies. Autoimmun. Rev. 2020, 19, 102571.
  13. Cho, K.-C.; Clark, D.J.; Schnaubelt, M.; Teo, G.C.; Leprevost, F.D.V.; Bocik, W.; Boja, E.S.; Hiltke, T.; Nesvizhskii, A.I.; Zhang, H. Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry. Anal. Chem. 2020, 92, 4217–4225.
  14. Kawashima, Y.; Watanabe, E.; Umeyama, T.; Nakajima, D.; Hattori, M.; Honda, K.; Ohara, O. Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis. Int. J. Mol. Sci. 2019, 20, 5932.
  15. Nakajima, D.; Ohara, O.; Kawashima, Y. Data-Independent Acquisition Mass Spectrometry-Based Deep Proteome Analysis for Hydrophobic Proteins from Dried Blood Spots Enriched by Sodium Carbonate Precipitation. In Clinical Proteomics; Humana: New Yor, NY, USA, 2022; pp. 39–52. ISBN 978-1-0716-1935-3.
  16. Costanzo, M.; Caterino, M.; Cevenini, A.; Kollipara, L.; Shevchuk, O.; Nguyen, C.D.L.; Sickmann, A.; Ruoppolo, M. Data Independent Acquisition Mass Spectrometry for Proteomic Advances into Isolated Methylmalonic Acidemia. In NATO Science for Peace and Security Series A: Chemistry and Biology; Springer: Dordrecht, The Netherlands, 2020; pp. 221–223.
  17. Barigazzi, E.; Santorelli, L.; Morello, W.; Raimondo, F.; Crapella, B.; Ghio, L.; Tamburello, C.; Montini, G.; Pitto, M. New Insight into Idiopathic Nephrotic Syndrome: Strategy Based on Urinary Exosomes. In NATO Science for Peace and Security Series A: Chemistry and Biology; Springer: Dordrecht, The Netherlands, 2020; pp. 217–218.
  18. Santorelli, L.; Stella, M.; Chinello, C.; Capitoli, G.; Piga, I.; Smith, A.; Grasso, A.; Grasso, M.; Bovo, G.; Magni, F. Does the Urinary Proteome Reflect ccRCC Stage and Grade Progression? Diagnostics 2021, 11, 2369.
  19. Kong, S.W.; Hernandez-Ferrer, C. Assessment of coverage for endogenous metabolites and exogenous chemical compounds using an untargeted metabolomics platform. Pac. Symp. Biocomput. 2020, 25, 587–598.
  20. Manganelli, V.; Salvatori, I.; Costanzo, M.; Capozzi, A.; Caissutti, D.; Caterino, M.; Valle, C.; Ferri, A.; Sorice, M.; Ruoppolo, M.; et al. Overexpression of Neuroglobin Promotes Energy Metabolism and Autophagy Induction in Human Neuroblastoma SH-SY5Y Cells. Cells 2021, 10, 3394.
  21. Costanzo, M.; Caterino, M.; Cevenini, A.; Jung, V.; Chhuon, C.; Lipecka, J.; Fedele, R.; Guerrera, I.C.; Ruoppolo, M. Dataset of a comparative proteomics experiment in a methylmalonyl-CoA mutase knockout HEK 293 cell model. Data Brief 2020, 33, 106453.
  22. Gordon, D.E.; Jang, G.M.; Bouhaddou, M.; Xu, J.; Obernier, K.; White, K.M.; O’Meara, M.J.; Rezelj, V.V.; Guo, J.Z.; Swaney, D.L.; et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 2020, 583, 459–468.
  23. Giacco, A.; Paoli, G.D.; Senese, R.; Cioffi, F.; Silvestri, E.; Moreno, M.; Ruoppolo, M.; Caterino, M.; Costanzo, M.; Lombardi, A.; et al. The saturation degree of fatty acids and their derived acylcarnitines determines the direct effect of metabolically active thyroid hormones on insulin sensitivity in skeletal muscle cells. FASEB J. 2018, 33, 1811–1823.
  24. Ruoppolo, M.; Caterino, M.; Albano, L.; Pecce, R.; Di Girolamo, M.G.; Crisci, D.; Costanzo, M.; Milella, L.; Franconi, F.; Campesi, I. Targeted metabolomic profiling in rat tissues reveals sex differences. Sci. Rep. 2018, 8, 4663.
  25. De Pasquale, V.; Caterino, M.; Costanzo, M.; Fedele, R.; Ruoppolo, M.; Pavone, L.M. Targeted Metabolomic Analysis of a Mucopolysaccharidosis IIIB Mouse Model Reveals an Imbalance of Branched-Chain Amino Acid and Fatty Acid Metabolism. Int. J. Mol. Sci. 2020, 21, 4211.
  26. Heiles, S. Advanced tandem mass spectrometry in metabolomics and lipidomics—methods and applications. Anal. Bioanal. Chem. 2021, 413, 5927–5948.
  27. Rampler, E.; El Abiead, Y.; Schoeny, H.; Rusz, M.; Hildebrand, F.; Fitz, V.; Koellensperger, G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics—Standardization, Coverage, and Throughput. Anal. Chem. 2020, 93, 519–545.
  28. Abu-Farha, M.; Thanaraj, T.A.; Qaddoumi, M.G.; Hashem, A.; Abubaker, J.; Al-Mulla, F. The Role of Lipid Metabolism in COVID-19 Virus Infection and as a Drug Target. Int. J. Mol. Sci. 2020, 21, 3544.
  29. Galbraith, M.D.; Kinning, K.T.; Sullivan, K.D.; Baxter, R.; Araya, P.; Jordan, K.R.; Russell, S.; Smith, K.P.; Granrath, R.E.; Shaw, J.R.; et al. Seroconversion stages COVID19 into distinct pathophysiological states. eLife 2021, 10.
  30. Li, C.-X.; Gao, J.; Zhang, Z.; Chen, L.; Li, X.; Zhou, M.; Wheelock, Å.M. Multiomics integration-based molecular characterizations of COVID-19. Brief. Bioinform. 2021, 23, bbab485.
  31. Patel, H.; Ashton, N.J.; Dobson, R.J.B.; Andersson, L.-M.; Yilmaz, A.; Blennow, K.; Gisslen, M.; Zetterberg, H. Proteomic blood profiling in mild, severe and critical COVID-19 patients. Sci. Rep. 2021, 11, 6357.
  32. Haljasmägi, L.; Salumets, A.; Rumm, A.P.; Jürgenson, M.; Krassohhina, E.; Remm, A.; Sein, H.; Kareinen, L.; Vapalahti, O.; Sironen, T.; et al. Longitudinal proteomic profiling reveals increased early inflammation and sustained apoptosis proteins in severe COVID-19. Sci. Rep. 2020, 10, 20533.
  33. Zhong, W.; Altay, O.; Arif, M.; Edfors, F.; Doganay, L.; Mardinoglu, A.; Uhlen, M.; Fagerberg, L. Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms. eBioMedicine 2021, 74, 103723.
  34. Bauer, W.; Weber, M.; Diehl-Wiesenecker, E.; Galtung, N.; Prpic, M.; Somasundaram, R.; Tauber, R.; Schwenk, J.M.; Micke, P.; Kappert, K. Plasma Proteome Fingerprints Reveal Distinctiveness and Clinical Outcome of SARS-CoV-2 Infection. Viruses 2021, 13, 2456.
  35. Shu, T.; Ning, W.; Wu, D.; Xu, J.; Han, Q.; Huang, M.; Zou, X.; Yang, Q.; Yuan, Y.; Bie, Y.; et al. Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19. Immunity 2020, 53, 1108–1122.e5.
  36. Park, J.; Kim, H.; Kim, S.Y.; Kim, Y.; Lee, J.-S.; Dan, K.; Seong, M.-W.; Han, D. In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients. Sci. Rep. 2020, 10, 22418.
  37. Messner, C.B.; Demichev, V.; Wendisch, D.; Michalick, L.; White, M.; Freiwald, A.; Textoris-Taube, K.; Vernardis, S.I.; Egger, A.-S.; Kreidl, M.; et al. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection. Cell Syst. 2020, 11, 11–24.
  38. Demichev, V.; Tober-Lau, P.; Lemke, O.; Nazarenko, T.; Thibeault, C.; Whitwell, H.; Röhl, A.; Freiwald, A.; Szyrwiel, L.; Ludwig, D.; et al. A time-resolved proteomic and prognostic map of COVID-19. Cell Syst. 2021, 12, 780–794.e7.
  39. Su, Y.; Chen, D.; Yuan, D.; Lausted, C.; Choi, J.; Dai, C.L.; Voillet, V.; Duvvuri, V.R.; Scherler, K.; Troisch, P.; et al. Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19. Cell 2020, 183, 1479–1495.e20.
  40. Chen, Y.; Zheng, Y.; Yu, Y.; Wang, Y.; Huang, Q.; Qian, F.; Sun, L.; Song, Z.; Chen, Z.; Feng, J.; et al. Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage. EMBO J. 2020, 39, e105896.
  41. Krishnan, S.; Nordqvist, H.; Ambikan, A.T.; Gupta, S.; Sperk, M.; Svensson-Akusjärvi, S.; Mikaeloff, F.; Benfeitas, R.; Saccon, E.; Ponnan, S.M.; et al. Metabolic Perturbation Associated With COVID-19 Disease Severity and SARS-CoV-2 Replication. Mol. Cell. Proteom. 2021, 20, 100159.
  42. Suvarna, K.; Salkar, A.; Palanivel, V.; Bankar, R.; Banerjee, N.; Pai, M.G.J.; Srivastava, A.; Singh, A.; Khatri, H.; Agrawal, S.; et al. A Multi-omics Longitudinal Study Reveals Alteration of the Leukocyte Activation Pathway in COVID-19 Patients. J. Proteome Res. 2021, 20, 4667–4680.
  43. Wu, P.; Chen, D.; Ding, W.; Wu, P.; Hou, H.; Bai, Y.; Zhou, Y.; Li, K.; Xiang, S.; Liu, P.; et al. The trans-omics landscape of COVID-19. Nat. Commun. 2021, 12, 4543.
  44. Li, Y.; Hou, G.; Zhou, H.; Wang, Y.; Tun, H.M.; Zhu, A.; Zhao, J.; Xiao, F.; Lin, S.; Liu, D.; et al. Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery. Signal Transduct. Target. Ther. 2021, 6, 155.
  45. Wang, C.; Li, X.; Ning, W.; Gong, S.; Yang, F.; Fang, C.; Gong, Y.; Wu, D.; Huang, M.; Gou, Y.; et al. Multi-omic profiling of plasma reveals molecular alterations in children with COVID-19. Theranostics 2021, 11, 8008–8026.
  46. Hou, X.; Zhang, X.; Wu, X.; Lu, M.; Wang, D.; Xu, M.; Wang, H.; Liang, T.; Dai, J.; Duan, H.; et al. Serum Protein Profiling Reveals a Landscape of Inflammation and Immune Signaling in Early-stage COVID-19 Infection. Mol. Cell. Proteom. 2020, 19, 1749–1759.
  47. Chen, Y.; Yao, H.; Zhang, N.; Wu, J.; Gao, S.; Guo, J.; Lu, X.; Cheng, L.; Luo, R.; Liang, X.; et al. Proteomic Analysis Identifies Prolonged Disturbances in Pathways Related to Cholesterol Metabolism and Myocardium Function in the COVID-19 Recovery Stage. J. Proteome Res. 2021, 20, 3463–3474.
  48. Kimura, Y.; Nakai, Y.; Shin, J.; Hara, M.; Takeda, Y.; Kubo, S.; Jeremiah, S.S.; Ino, Y.; Akiyama, T.; Moriyama, K.; et al. Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach. Sci. Rep. 2021, 11, 20638.
  49. Lee, J.; Han, D.; Kim, S.Y.; Hong, K.H.; Jang, M.; Kim, M.J.; Kim, Y.; Park, J.H.; Cho, S.I.; Park, W.B.; et al. Longitudinal proteomic profiling provides insights into host response and proteome dynamics in COVID-19 progression. Proteomics 2021, 21, 2000278.
  50. D’Alessandro, A.; Thomas, T.; Dzieciatkowska, M.; Hill, R.C.; Francis, R.O.; Hudson, K.E.; Zimring, J.C.; Hod, E.A.; Spitalnik, S.L.; Hansen, K.C. Serum Proteomics in COVID-19 Patients: Altered Coagulation and Complement Status as a Function of IL-6 Level. J. Proteome Res. 2020, 19, 4417–4427.
  51. Shen, B.; Yi, X.; Sun, Y.; Bi, X.; Du, J.; Zhang, C.; Quan, S.; Zhang, F.; Sun, R.; Qian, L.; et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell 2020, 182, 59–72.e15.
  52. Yang, J.; Chen, C.; Chen, W.; Huang, L.; Fu, Z.; Ye, K.; Lv, L.; Nong, Z.; Zhou, X.; Lu, W.; et al. Proteomics and metabonomics analyses of COVID-19 complications in patients with pulmonary fibrosis. Sci. Rep. 2021, 11, 14601.
  53. Caterino, M.; Ruoppolo, M.; Villani, G.R.D.; Marchese, E.; Costanzo, M.; Sotgiu, G.; Dore, S.; Franconi, F.; Campesi, I. Influence of Sex on Urinary Organic Acids: A Cross-Sectional Study in Children. Int. J. Mol. Sci. 2020, 21, 582.
  54. Melo, M.G.; Remacle, N.; Cudré-Cung, H.-P.; Roux, C.; Poms, M.; Cudalbu, C.; Barroso, M.; Gersting, S.W.; Feichtinger, R.G.; Mayr, J.A.; et al. The first knock-in rat model for glutaric aciduria type I allows further insights into pathophysiology in brain and periphery. Mol. Genet. Metab. 2021, 133, 157–181.
  55. Costanzo, M.; Fiocchetti, M.; Ascenzi, P.; Marino, M.; Caterino, M.; Ruoppolo, M. Proteomic and Bioinformatic Investigation of Altered Pathways in Neuroglobin-Deficient Breast Cancer Cells. Molecules 2021, 26, 2397.
  56. Costanzo, M.; Caterino, M.; Salvatori, I.; Manganelli, V.; Ferri, A.; Misasi, R.; Ruoppolo, M. Proteome data of neuroblastoma cells overexpressing Neuroglobin. Data Brief 2022, 41, 107843.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Biochemistry & Molecular Biology
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Michele Costanzo
View Times: 525
Entry Collection: COVID-19
Revisions: 2 times (View History)
Update Date: 14 Mar 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback