In the actual backgroundontext 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]}[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].

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]}[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]}[13,14,15,16], besides the traditional data-dependent acquisition (DDA) ones ^{[17][18][19][20][21]}[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][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][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.

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

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]}[97,98,99,100]. 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.