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. 2023 Mar 1;8(10):9555-9568.
doi: 10.1021/acsomega.3c00020. eCollection 2023 Mar 14.

Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome

Prithvi Singh  1 , Mohd Mohsin  2 , Armiya Sultan  2 , Prakash Jha  3 , Mohd Mabood Khan  4 , Mansoor Ali Syed  2 , Madhu Chopra  3 , Mohammad Serajuddin  4 , Arshad Husain Rahmani  5 , Saleh A Almatroodi  5 , Faris Alrumaihi  5 , Ravins Dohare  1
Affiliations

Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome

Prithvi Singh et al. ACS Omega. .

Abstract

Despite all epidemiological, clinical, and experimental research efforts, therapeutic concepts in sepsis and sepsis-induced multi-organ dysfunction syndrome (MODS) remain limited and unsatisfactory. Currently, gene expression data sets are widely utilized to discover new biomarkers and therapeutic targets in diseases. In the present study, we analyzed MODS expression profiles (comprising 13 sepsis and 8 control samples) retrieved from NCBI-GEO and found 359 differentially expressed genes (DEGs), among which 170 were downregulated and 189 were upregulated. Next, we employed the weighted gene co-expression network analysis (WGCNA) to establish a MODS-associated gene co-expression network (weighted) and identified representative module genes having an elevated correlation with age. Based on the results, a turquoise module was picked as our hub module. Further, we constructed the PPI network comprising 35 hub module DEGs. The DEGs involved in the highest-confidence PPI network were utilized for collecting pathway and gene ontology (GO) terms using various libraries. Nucleotide di- and triphosphate biosynthesis and interconversion was the most significant pathway. Also, 3 DEGs within our PPI network were involved in the top 5 significantly enriched ontology terms, with hypercortisolism being the most significant term. PRKAR1A was the overlapping gene between top 5 significant pathways and GO terms, respectively. PRKAR1A was considered as a therapeutic target in MODS, and 2992 ligands were screened for binding with PRKAR1A. Among these ligands, 3 molecules based on CDOCKER score (molecular dynamics simulated-based score, which allows us to rank the binding poses according to their quality and to identify the best pose for each system) and crucial interaction with human PRKAR1A coding protein and protein kinase-cyclic nucleotide binding domains (PKA RI alpha CNB-B domain) via active site binding residues, viz. Val283, Val302, Gln304, Val315, Ile327, Ala336, Ala337, Val339, Tyr373, and Asn374, were considered as lead molecules.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
(A) MA plot demonstrating the disparity within expression values (mean) and fold change (log2) of 15,014 MODS genes. Red-, gray-, and blue-colored points signify upregulated (189), nonsignificant (14,655), and downregulated (170) genes, respectively. (B) Heatmap of top 10 down- and upregulated MODS-specific DEGs. Rows correspond to the normalized expression value of DEGs, and columns correspond to samples. The colored annotation bars representing the sample type (light magenta for controls and yellow for sepsis), gender type (dark magenta for females and dark blue for males), and age are positioned at the top of the heatmap.
Figure 2
Figure 2
(A) Dendrogram of 359 MODS-associated DEGs clustered on the basis of disTOM and three communities (obtained using dynamic tree cut algorithm). (B) 3D MDS plot with every colored point signifying a gene fitting to a particular community of the corresponding color. (C) TOM plot of the WGCN signifying TOM among brown, blue, and turquoise community genes. The plot’s top and left side panels represent hierarchically clustered gene dendrograms and module assignments. Dark-colored blocks along the diagonal represent communities. (D) Bar plot demonstrates the GS values distribution and error bars across blue, turquoise, and brown modules.
Figure 3
Figure 3
Scatterplot showing significantly (p < 0.05) high correlation of GS for age with MM across (A) brown and (B) turquoise modules. Expression heatmap of (C) brown and (D) turquoise community genes, wherein the columns and rows relate to samples and genes. The red- and green-colored bands in the heatmaps signify higher and lower expression levels, respectively. Also, the corresponding ME expression levels (y-axis) across the samples (x-axis) are represented at the base panel of each module heatmap as bar plots.
Figure 4
Figure 4
PPI network of hub module comprising 35 nodes and 22 linking edges comparable to a STRING interaction score > 0.9. The yellow- and magenta-colored nodes signify up- and downregulated proteins.
Figure 5
Figure 5
Three-dimensional plot of different conformations of drugs sorted based on the consensus-LibDock score. The red, green, teal, and blue colors represent 0, 1, 2, and 3 consensus scores.
Figure 6
Figure 6
Structure-based virtual screening of small-molecule inhibitors. 2D and 3D schematic representation of the intermolecular interaction of predicted binding modes of A. S1591, B. S5267, and C. S7953.
Figure 7
Figure 7
Time-dependent structural dynamics of protein–ligand (PL) complexes: (A) RMSD, (B) RMSF, (C) radius of gyration, (D) intermolecular H-bond between protein and protein, and (E) intermolecular H-bond between protein and ligand.
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References

    1. Singer M.; Deutschman C. S.; Seymour C. W.; Shankar-Hari M.; Annane D.; Bauer M.; Bellomo R.; Bernard G. R.; Chiche J.-D.; Coopersmith C. M.; Hotchkiss R. S.; Levy M. M.; Marshall J. C.; Martin G. S.; Opal S. M.; Rubenfeld G. D.; van der Poll T.; Vincent J.-L.; Angus D. C. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, J. Am. Med. Assoc. 2016, 315, 801.10.1001/jama.2016.0287. - DOI - PMC - PubMed
    1. Hotchkiss R. S.; Moldawer L. L.; Opal S. M.; Reinhart K.; Turnbull I. R.; Vincent J.-L. Sepsis and septic shock. Nat. Rev. Dis. Prim. 2016, 2, 16045.10.1038/nrdp.2016.45. - DOI - PMC - PubMed
    1. Rudd K. E.; Johnson S. C.; Agesa K. M.; Shackelford K. A.; Tsoi D.; Kievlan D. R.; Colombara D. V.; Ikuta K. S.; Kissoon N.; Finfer S.; Fleischmann-Struzek C.; Machado F. R.; Reinhart K. K.; Rowan K.; Seymour C. W.; Watson R. S.; West T. E.; Marinho F.; Hay S. I.; Lozano R.; Lopez A. D.; Angus D. C.; Murray C. J. L.; Naghavi M. Global, Regional, and National Sepsis Incidence and Mortality, 1990-2017: Analysis for the Global Burden of Disease Study. Lancet 2020, 395, 200–211. 10.1016/S0140-6736(19)32989-7. - DOI - PMC - PubMed
    1. Jarczak D.; Kluge S.; Nierhaus A. Sepsis-Pathophysiology and Therapeutic Concepts. Front. Med. 2021, 8, 628302.10.3389/fmed.2021.628302. - DOI - PMC - PubMed
    1. Schultz M. J.; Dunser M. W.; Dunser A. M.; Dondorp N. K. J.; Adhikari S.; Iyer A.; Kwizera Y.; Lubell A.; Papali L.; Pisani B. D.; Riviello D. C.; Angus L. C.; Azevedo T.; Baker J. V.; Diaz E.; Festic R.; Haniffa R.; Jawa S. T.; Jacob N.; Kissoon R.; Lodha I.; Martin-Loeches G.; Lundeg D.; Misango M.; Mer S.; Mohanty S.; Murthy N.; Musa J.; Nakibuuka A.; Serpa Neto M.; Nguyen Thi Hoang B.; Nguyen Thien R.; Pattnaik J.; Phua J.; Preller P.; Povoa S.; Ranjit D.; Talmor J.; Thevanayagam C. L.; Thwaites C. L. Current Challenges in the Management of Sepsis in ICUs in Resource-Poor Settings and Suggestions for the Future. Intensive Care Med. 2017, 43, 612–624. 10.1007/s00134-017-4750-z. - DOI - PubMed