Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Keywords: COVID-19; SARS-CoV-2; drug repurposing; networks; omics.

Figure 1

Snapshot of the official websites reporting SARS-CoV-2 data in real time. (A) World Health Organization (WHO) Coronavirus (COVID-19) Dashboard (https://COVID-19.who.int/ (accessed on 1 August 2023)). (B) GISAID Repository (https://gisaid.org/hcov19-variants/ (accessed on 1 August 2023)).The GISAID Initiative promotes the rapid sharing of data from all influenza viruses and the coronavirus causing COVID-19. This includes genetic sequences and related clinical and epidemiological data associated with human viruses, and geographical as well as species-specific data associated with avian and other animal viruses, to help researchers to understand how viruses evolve and spread during epidemics and pandemics.

Figure 2

Term associations from title of manuscripts published in (A) 2nd semester 2020, (B) 1st semester 2021, (C) 2nd semester 2021, (D) 1st semester 2022, (E) 2nd semester 2022 and (F) 1st semester 2023. All manuscripts were retrieved in PubMed (https://pubmed.ncbi.nlm.nih.gov (accessed on 1 May 2023)) by searching for “SARS-CoV-2” in the “Title” field. Title terms were associated using the VOSviewer software (www.vosviewer.com) (accessed on 1 August 2023) and the 100 top-ranked associations were displayed using the Cytoscape platform. Different colors show clusters of terms most correlated.

Figure 3

Number of papers published per semester from 2020 to 2023 and found in PubMed (https://pubmed.ncbi.nlm.nih.gov (accessed on 1 May 2023)) by searching for SARS-CoV-2 AND (A) Genomics, (B) Transcriptomics, (C) Proteomics, (D) Metabolomics, (E) Epigenomics or (F) Lipidomics.

Figure 4

Workflow summarizing the main steps in discovering gene targets and herbal bioactive compounds as potential drugs to treat COVID-19 patients. Starting with herbal mixtures, the authors retrieved the bioactive compounds present there and the corresponding genes that they target. These genes were matched with those interacting with SARS-CoV-2 proteins to select compound/gene combinations of interest. The further identification of potential drug targets (HUBs) was performed by the topological analysis of network models reconstructed from the omics profiles characterized by COVID-19 patient samples. Finally, the selected combinations compound/gene(HUB) were in silico validated by molecular docking/dynamics. The blue rectangles indicate the main databases used, while the red circles indicate the in silico approaches.

Figure 5

Network topology and parameters used to select hubs and candidate drug targets. (A) Scheme representing hubs, bottlenecks and shortest path. (B) Betweenness centrality and its variation in network models taken as an example. (C) Proximity distance. Red and orange nodes indicate genes belonging to disease and drug target modules, respectively. In green, the shortest paths between disease genes (S1, S2 and S3) and the drug target genes (t1, t2, t4 and t4) are shown. Node size is proportional to node degree.

Figure 6

Overlapping of drugs selected in different studies reported in https://maayanlab.cloud/covid19/ (accessed on 1 August 2023) [119]. Datasets (circles) are connected if they share at least 2 drugs. Blue rectangles indicate datasets that do not share any drugs, while black ones indicate datasets that share only one drug. Circle size is proportional to the number of drugs found. Red edges indicate studies sharing at least 5 drugs.