Abstract

   Recent political unrest has highlighted the importance of understanding
   the short- and long-term effects of gamma-radiation exposure on human
   health and survivability. In this regard, effective treatment for acute
   radiation syndrome (ARS) is a necessity in cases of nuclear disasters.
   Here, we propose 20 therapeutic targets for ARS identified using a
   systematic approach that integrates gene coexpression networks obtained
   under radiation treatment in humans and mice, drug databases,
   disease-gene association, radiation-induced differential gene
   expression, and literature mining. By selecting gene targets with
   existing drugs, we identified potential candidates for drug
   repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9,
   CBR1, ZAP70, IDH3B) were confirmed through literature to have shown
   radioprotective effect upon perturbation. This study provided a new
   perspective for the treatment of ARS using systems-level gene
   associations integrated with multiple biological information. The
   identified genes might provide high confidence drug target candidates
   for potential drug repurposing for ARS.

   Subject terms: Computational biology and bioinformatics, Functional
   clustering, Target identification, Transcriptomics

Introduction

   Given the increased capacity of countries to produce enormous
   radioactive catastrophe and the heightened tensions within the
   political climate, treatment, and prevention of Acute Radiation
   Syndrome (ARS) is paramount. ARS is an understudied disease that
   describes whole body exposure to high doses of radiation (> 0.7 Gy) in
   a short period of time^[37]1. The pathophysiology of ARS is
   characterized by nausea, vomiting, and diarrhea^[38]1. Additionally,
   exposure of (0.7–2 Gy) irradiation leads to a depletion of lymphocytes,
   granulocytes, and hepatocytes^[39]1,[40]2. The progression of the
   disease generally follows through three clinically distinct phases.
   Nausea, headaches, diarrhea, fever, altered consciousness, and fatigue
   characterize the potential outcomes of the initial—prodromal—response
   phase^[41]2. During the second, latent phase, the patient displays no
   symptoms of ARS. The third stage is the manifest illness phase, where
   the symptoms of ARS become apparent. There are currently three existing
   FDA approved treatments for ARS Neupogen, Neulasta, and Leukine^[42]3.
   However, these approved drugs are not perfect. Additional treatment
   options show some disease-mitigating properties. For instance, the
   treatment of mice by captopril, an angiotensin-converting enzyme (ACE)
   inhibitor, showed increased survival at thirty days post whole body
   irradiation^[43]4. Additional studies have shown the efficacy of
   Insulin-Like Growth factor 1 in mitigating the deleterious effects of
   radiation on mice populations^[44]5. However, these treatment methods
   only address symptoms of ARS and require strict dosing protocols to
   acquire adequate efficacy. Thus, additional treatment options are
   necessary to more effectively challenge ARS.

   However, ARS is an understudied disease, and the discovery of new
   treatments faces significant combinatorial complexity. To deal with
   this complexity new treatment options can be elucidated using systems-
   and network-based approaches that can help identify critical regulatory
   genes within the disease state. Weighted Gene Coexpression Network
   Analysis (WGCNA)^[45]6 can be used to characterize modules of
   correlated genes in gene regulatory networks. Genes in these modules
   have high levels of coexpression with a central eigen gene, which
   represents the first principal component of a given module. The central
   eigen genes have been found to be highly correlated to the most
   connected genes within a module called hubs whose expression levels are
   representative of highly correlated genes in the same module. These hub
   genes can be predictive of novel drug targets. For example, such
   analyses have been used to characterize genes and pathways associated
   with Alzheimer’s disease^[46]7, Schizophrenia ^[47]8, Amyotrophic
   Lateral Sclerosis^[48]9, diabetic kidney disease^[49]10, and to
   determine potential drug targets in pathogens^[50]11, and other complex
   human diseases^[51]12.

   To further address challenges with the discovery and development of
   treatments for complex diseases, repurposing of existing drugs to new
   diseases has become a preferred alternative to elucidating novel drugs
   due to high attrition rate and time. For instance, the FDA-approved
   radiation countermeasures Neupogen (CSF3R), Neulasta (CSF3R), and
   Leukine (CSF2RA) are repurposed drugs^[52]3. Neupogen and Neulasta are
   used in chemotherapy-induced neutropenia and Leukine is used in bone
   marrow transplant recipients^[53]13.

   In this study, we used a network-based systems analysis of five
   transcriptomics datasets obtained under radiation treatment and
   integrated them with public drug information and disease association.
   This led us to predict eight targets with repurposable drugs/compounds
   available.

Results

   The workflow used to identify drug targets for ARS is shown in
   Fig. [54]1. First, we collected ARS relevant data from public
   repositories. Second, we constructed gene coexpression networks from
   transcriptomics data across different species. We used them to identify
   important genes (hub genes). In the absence of relevant and
   high-quality human data, we aimed to identify consensus across the
   available ARS transcriptomics data for different species to maximize
   the quality of predictions. Using a consensus method increases the
   likelihood that efficacy in a mouse model would also be observed in
   human trials, which may minimize the drug development costs. Next, to
   predict potential drug targets for ARS, these hub genes were mapped
   with external databases of drugs and diseases, differentially expressed
   genes under radiation treatments from 6 h to 7 days following radiation
   of 0.5–10 Gy (different doses and time). The human samples were
   selected from pre and post-irradiation subjects. These subjects
   received total body irradiation (TBI) prior to various
   transplantations. Finally, using literature mining, we predicted the
   drug targets for ARS.

Figure 1.

   [55]Figure 1
   [56]Open in a new tab

   General workflow to construct gene coexpression network to predict
   potential drug targets.

Weighted gene coexpression network analysis revealed salient expression
networks

   We collected five transcriptomics datasets relevant to ARS that were
   obtained from mice and humans from the GEO database. Using these five
   datasets, we constructed five weighted gene coexpression networks. A
   minimum of 28 to a maximum of 169 samples was used for network
   construction (see Materials and methods). The total number of genes
   included in these datasets were between ~ 10,000 to ~ 18,000. The scale
   independence within each network was achieved using a thresholding
   power (β) parameter. Across all networks, the selected β was ranging
   from 3 to 20 (Supplementary Data [57]1). The number of modules
   (clusters of genes) identified in each network ranged from 5 to 23
   (Table [58]1). Genes in each module in a particular network have the
   highest topological overlap (i.e., robust measure than correlation).
   These genes have significant correlations among expression profiles
   across different doses of radiation or time points after radiation
   exposure.

Table 1.

   Details of constructed gene coexpression networks under radiation
   treatments.
   Species Dataset Number of samples Total number of modules
   (sub-networks) Total number of genes
   Mouse [59]GSE104121 48 5 18,167
   [60]GSE6874 28 10 11,670
   [61]GSE10640 169 22 14,835
   Human [62]GSE6874 70 8 12,845
   [63]GSE10640 60 23 10,918
   [64]Open in a new tab

Biological function analysis reveals conserved pathways across networks

   In the next step, we found the biological significance of modules
   identified in the constructed networks. Pathway enrichment analysis
   showed enrichment with biological processes in at least one module
   across five datasets (Supplementary Data [65]2). Next, we identified
   biological processes that were identified by multiple datasets. Among
   these, 23 gene ontology (GO), KEGG, and Reactome terms were enriched in
   more than one dataset (Fig. [66]2A). These terms included biological
   processes and pathways related to immune response, xenobiotic stimulus,
   chemical carcinogenesis, blood coagulation, and white blood cell
   activation (Fig. [67]2B) (P-value < 0.05 and False Discovery
   Rate < 5%). The highest fold enrichment across all the networks
   (average fold enrichment) was found in chemical carcinogenesis (~ 16
   fold), blood coagulation and hemostasis (~ 8 fold), and response to
   xenobiotic stimuli (~ 7 fold).

Figure 2.

   [68]Figure 2
   [69]Open in a new tab

   Comparison of enriched biological pathways among different networks.
   (A) Overlap of the Gene Ontology terms (Biological Processes, Molecular
   Functions, and Cellular Compartments) and pathways (KEGG pathways,
   Reactome pathways, BioCyc) in each network based on 5% False Discovery
   Rate (FDR). The venn diagram was created using
   [70]http://bioinformatics.psb.ugent.be/webtools/Venn/14 (B) Average
   fold enrichment of biological processes and pathways that were enriched
   in more than one network.

Hub genes found across multiple networks

   In the constructed networks, intramodular connectivity was used as a
   measure to identify hub genes (see Materials and methods). Because of
   the limited data availability of ARS-relevant human data, we identified
   hub genes that appeared in multiple datasets as high confidence. To
   select the top hub genes, first, we used criteria of maximum overlap of
   hub genes across the different datasets. We identified the top 5, 10,
   and 20 percent hub genes and compared them across all five datasets. In
   our analysis, the top 20% resulted in more hub genes that were
   overlapped in the maximum number of datasets (Fig. [71]3A). Among
   these, we found five hub genes (Myeloid Cell Nuclear Differentiation
   Antigen (MNDA), Syntaxin 11 (STX11), intracellular tyrosine kinase
   (IKT), Slingshot Protein Phosphatase 2 (SSH2), Keratin 9 (KRT9) )
   shared by all networks, 58 hub genes shared by four networks, and 412
   hub genes that were shared by three networks. On the contrary, in both
   the top 5% and 10% hub genes, no hub genes were shared by all networks.
   Four networks shared only seven genes in the top 10% of hub genes,
   while not a single hub gen was shared by four out of five networks in
   the top 5%. Thus, the top 20% cutoff allowed optimal comparisons
   amongst the networks.

Figure 3.

   [72]Figure 3
   [73]Open in a new tab

   Identification and analysis of hub genes. (A) top 5%, top 10%, and top
   20% hub genes identified for each dataset that are identified by five,
   four, three, and two networks. (B) GO biological processes enriched in
   2,351 hub genes with False Discovery Rate < 5%.

   Next, among the top 20% hub genes (Supplementary Data [74]3), we chose
   genes that were identified within more than one gene coexpression
   network. This analysis resulted in 2,351 hub genes identified within at
   least two coexpression networks (Supplementary Data [75]3). Enrichment
   analysis of the selected hub genes showed GO biological processes
   related to leukocyte migration, apoptosis, DNA replication, T cell
   signaling, NF-κB, and IFN-γ signaling (Fig. [76]3B).

   Furthermore, we have investigated the biological processes that are
   enriched in differentially expressed in response to high radiation dose
   (i.e., 10 Gy). Many of the biological processes and pathways related to
   commonly enriched in multiple networks (Fig. [77]2B) and in hub genes
   (Fig. [78]3B) were also enriched in genes upregulated in high radiation
   doses. We found the gene ontology terms regulation of platelet
   activation (GO:0,010,543; FDR = 0.035), blood coagulation
   (GO:0,007,596; FDR = 0.027), leukocyte differentiation (GO:0,002,521,
   FDR = 0.007), response to stimulus (GO:0,050,896; FDR = 0.0003), immune
   systems process (GO:0,002,376, FDR = 0.0042), cell–cell adhesion
   (GO:0,098,609; FDR = 0.045), intracellular signal transduction
   (GO:0,035,556; FDR = 0.045), and vesicle-mediated transport
   (GO:0,016,192; FDR = 0.003) as enriched in consistently upregulated
   genes (upregulated in more samples than downregulated). A full table of
   all enriched biological processes is provided as Supplementary Data
   [79]4.

Network analysis predicts high confidence drug targets for ARS

   To identify drug targets for potential drug repurposing, we identified
   genes that are targets of existing preclinical and FDA-approved drugs
   (Materials and methods) and then selected genes that were
   differentially expressed in response to radiation treatments.

   Among the identified 2,351 hub genes, 520 genes were mapped with
   existing preclinical and approved drugs/compounds (see Materials and
   Methods). Next, the selected hub genes were compared with
   differentially expressed genes in ARS across 32 conditions (see
   Materials and Methods). To validate our findings, first, we
   investigated already known drug targets for ARS that are part of
   identified hub genes. The gene target CSF3R of two ARS drugs Neulasta
   and Neupogen appears in our list and they were differentially expressed
   in 21 different samples (upregulated in 12 samples and downregulated in
   9 samples). Next, we investigated all the hub genes for differential
   expression. Of the 520 genes with existing drugs, 293 genes were
   differentially expressed in at least eight (25%) of 32 conditions (see
   Supplementary Data [80]5). Among these, we identified 81 genes that
   were consistently up-regulated, and 20 genes that were consistently
   down-regulated (Fig. [81]4A, Supplementary Data [82]5). We then
   analyzed these 101 differentially expressed hub genes with existing
   drugs and compounds. These genes include 75 genes involved in (based on
   Gene Ontology) metabolic processes, 59 genes in response to a stimulus,
   30 genes in immune system processes, 5 genes in cell population and
   proliferation, and 4 genes in cell signaling (Fig. [83]4B).

Figure 4.

   [84]Figure 4
   [85]Open in a new tab

   (A) Hub genes found in multiple networks with consistent regulation
   depicted with the count of samples upregulated/downregulated. (B) Count
   of genes in GO Biological processes amongst 101 consistently
   differentially regulated genes.

   Next, literature mining revealed that 20 (of 101) genes were relevant
   to the ARS (Table [86]2). Of these 20 genes, we identified eight genes
   whose response to radiation was incongruent with prognostic
   favorability (indicated with bold font in Table [87]2):
   Bromodomain-Containing Protein 4 (BRD4), NF-kB Inhibitor Alpha
   (NFKBIA)), Cyclin-Dependent Kinase Inhibitor 1A (CDKN1A), Tissue Factor
   Pathway Inhibitor (TFPI), Matrix Metallopeptidase 9 (MMP9), Carbonyl
   Reductase 1 (CBR1), Zeta Chain Of T Cell Receptor Associated Protein
   Kinase 70 (ZAP70), and Isocitrate Dehydrogenase (NAD( +)) 3 Beta
   (IDH3B). A drug and protein target map of selected targets is shown in
   Fig. [88]5A. Of 20, 11 genes were also consistently upregulated in
   response to high radiation doses (10 Gy and 12 Gy) (Fig. [89]5B).

Table 2.

   Potential drug targets identified for acute radiation syndrome.
   Gene name Protein name Gene Function^a Drug bank CMAP drug/compound
   Involvement In disease Type of regulation Literature support Predicted
   regulation for treatment ARS Status of drug
   MYD88 Innate Immune Signal Transduction Adaptor Adapter protein
   involved in the Toll-like receptor and IL-1 receptor signaling pathway
   in the innate immune response ST-2825 MYD88 deficiency Up MyD88 may
   protect against long term damage and fibrosis following radiation
   injury^[90]16 Activation Discovery and Development
   BRD4 Bromodomain-Containing Protein 4 Chromatin reader protein that
   recognizes and binds acetylated histones and plays a key role in the
   transmission of epigenetic memory across cell divisions and
   transcription regulation. Remains associated with acetylated chromatin
   throughout the entire cell cycle and provides epigenetic memory for
   postmitotic G1 gene transcription by preserving acetylated chromatin
   status and maintaining high-order chromatin structure CPI-0610 A
   chromosomal aberration involving BRD4 is found in a rare, aggressive,
   and lethal carcinoma arising in midline organs of young people Up
   Inhibition of BRD4 increases cancer cell survival following
   irradiation^[91]17 Inhibition Phase 2
   AURKA Aurora Kinase A Mitotic serine/threonine kinases that contribute
   to the regulation of cell cycle progression AT9283 Up Inhibition of
   AURKA increases radiosensitivity in Hepatocellular Carcinoma^[92]18
   Activation Phase 2
   CKB Creatine Kinase B Reversibly catalyzes the transfer of phosphate
   between ATP and various phosphogens (e.g. creatine phosphate). Creatine
   kinase isoenzymes play a central role in energy transduction in tissues
   with large, fluctuating energy demands, such as skeletal muscle, heart,
   brain and spermatozoa Creatine Up Measured Creatine levels can give a
   prognostic marker for radiation dose^[93]19 Activation Approved
   NNMT Nicotinamide N-Methyltransferase Catalyzes the N-methylation of
   nicotinamide and other pyridines to form pyridinium ions. This activity
   is important for biotransformation of many drugs and xenobiotic
   compounds Niacin Up Overexpression of NNMT protects Mesenchymal Cancer
   stem cells from radiation therapy^[94]20 Activation Approved
   PTPN1 Protein Tyrosine Phosphatase Non-receptor Type 1 Tyrosine-protein
   phosphatase which acts as a regulator of endoplasmic reticulum unfolded
   protein response Tiludronic Acid Tiludronate Up Expression of PTPN1
   improves survival of irradiated mice^[95]21 Activation Approved
   NFKBIA NF-Kappa-B Inhibitor Alpha Inhibits the activity of dimeric
   NF-kappa-B/REL complexes by trapping REL dimers in the cytoplasm
   through masking of their nuclear localization signals. On cellular
   stimulation by immune and proinflammatory responses, becomes
   phosphorylated promoting ubiquitination and degradation, enabling the
   dimeric RELA to translocate to the nucleus and activate transcription
   Acetylsalicylic acid Aspirin Ectodermal dysplasia and immunodeficiency
   2 Up Blocking NF-kB increases apoptosis and decrease in growth in
   several cancer lineages^[96]22 Inhibition Approved
   MCL1 MCL1 Apoptosis Regulator Involved in the regulation of apoptosis
   versus cell survival, and in the maintenance of viability but not of
   proliferation. Mediates its effects by interactions with a number of
   other regulators of apoptosis. Isoform 1 inhibits apoptosis. Isoform 2
   promotes apoptosis Rosmarinic-acid Up MCL1 Protects against
   radiation-induced apoptosis^[97]23 Activation Launched
   ANXA1 Annexin A1 Plays important roles in the innate immune response as
   an effector of glucocorticoid-mediated responses and regulator of the
   inflammatory process Amcinonide Amcinonide Up ANXA1 serum concentration
   following glucocorticoid treatment improves prognosis following
   radiation-induced lung injury^[98]24 Activation Launched
   RALB RAS like proto-oncogene Multifunctional GTPase involved in a
   variety of cellular processes including gene expression, cell
   migration, cell proliferation, oncogenic transformation and membrane
   trafficking BQU57 Up RALB confers radioresistance to multiple tumor
   types^[99]25 Activation Preclinical
   CBFB Core-Binding Factor Subunit Beta Forms the heterodimeric complex
   core-binding factor (CBF) with RUNX family proteins (RUNX1, RUNX2, and
   RUNX3) AL-10–49 Up Suppresses Cancer ^[100]26 Activation Preclinical
   ANXA2 Annexin A2 Calcium-regulated membrane-binding protein whose
   affinity for calcium is greatly enhanced by anionic phospholipids. It
   binds two calcium ions with high affinity. May be involved in the
   heat-stress response Fluocinolone acetonide Up Prevents
   apoptosis^[101]27 Activation Approved
   MPL MPL Proto-Oncogene, Thrombopoietin Receptor Receptor for
   thrombopoietin that acts as a primary regulator of megakaryopoiesis and
   platelet production. May represent a regulatory molecule specific for
   TPO-R-dependent immune responses Eltrombopag Avatrombopag Congenital
   amegakaryocytic thrombocytopenia Up c-MPL agonist confers complete
   survival in mice exposed to ionizing radiation^[102]28 Activation
   Launched, Launched
   CDKN1A Cyclin-Dependent Kinase Inhibitor 1A May be involved in p53/TP53
   mediated inhibition of cellular proliferation in response to DNA
   damage. Binds to and inhibits cyclin-dependent kinase activity,
   preventing phosphorylation of critical cyclin-dependent kinase
   substrates and blocking cell cycle progression GGTI-298 Up CDKN1A is
   shown to promote cell cycle arrest in G1 phase^[103]29. G1 phase cell
   cycle arrest is seen in cells with wild-type p53 gene following
   irradiation^[104]30 Inhibition Preclinical
   TFPI Tissue Factor Pathway Inhibitor Inhibits factor X (X(a)) directly
   and, in a Xa-dependent way, inhibits VIIa/tissue factor activity,
   presumably by forming a quaternary Xa/LACI/VIIa/TF complex. It
   possesses an antithrombotic action and also the ability to associate
   with lipoproteins in plasma Dalteparin Dalteparin UP TFPI inhibition
   can inhibit hemophilia^[105]31. Radiation-induced coagulopathy
   decreases clotting ability^[106]32 Inhibition Approved
   MMP9 Matrix Metallopeptidase 9 May play an essential role in local
   proteolysis of the extracellular matrix and in leukocyte migration.
   Could play a role in bone osteoclastic resorption. Cleaves KiSS1 at a
   Gly-|-Leu bond. Cleaves type IV and type V collagen into large
   C-terminal three quarter fragments and shorter N-terminal one quarter
   fragments. Degrades fibronectin but not laminin or Pz-peptide Captopril
   Intervertebral disc disease UP Captopril is an inhibitor of MMP9 and
   has shown increased survival in mice depending on dosage timing^[107]33
   Inhibition Launched
   IGF1R Insulin-Like Growth Factor 1 Receptor Receptor tyrosine kinase
   which mediates actions of insulin-like growth factor 1 (IGF1). Binds
   IGF1 with high affinity and IGF2 and insulin (INS) with a lower
   affinity. The activated IGF1R is involved in cell growth and survival
   control. IGF1R is crucial for tumor transformation and survival of
   malignant cell ATL1101 Insulin-like growth factor resistance UP
   Overexpression of IGF1R is known to confer increased radioresistance in
   chemotherapy^[108]34 Activation Investigational
   CBR1 Carbonyl Reductase 1 NADPH-dependent reductase with broad
   substrate specificity. Catalyzes the reduction of a wide variety of
   carbonyl compounds including quinones, prostaglandins, menadione, plus
   various xenobiotics Acetohexamide Down Carbonyl Reductase confers
   radioprotectivity in head and neck squamous cell carcinoma^[109]35
   Activation Launched
   ZAP70 Zeta Chain of T Cell Receptor Associated Protein Kinase 70
   Tyrosine kinase that plays an essential role in regulation of the
   adaptive immune response. Regulates motility, adhesion and cytokine
   expression of mature T-cells, as well as thymocyte development.
   Contributes also to the development and activation of primary
   B-lymphocytes Staurosporine Immunodeficiency 48 Down Zap70 Deficiency
   leads to a loss of T cells^[110]36 Radiation induces
   lymphopenia^[111]37 Activation Preclinical
   IDH3B Isocitrate Dehydrogenase (NAD(+)) 3 Beta Plays a structural role
   to facilitate the assembly and ensure the full activity of the enzyme
   catalyzing the decarboxylation of isocitrate (ICT) into
   alpha-ketoglutarate NADH Coenzyme i Retinitis Pigmentosa Down
   Isocitrate Dehydrogenase generates NADH. NADH is radioprotective for
   mouse intestine^[112]38 Activation Approved, Phase 2
   [113]Open in a new tab

   ^aThe gene functions were obtained from the Uniprot database^[114]39.

Figure 5.

   [115]Figure 5
   [116]Open in a new tab

   Drug-target network of potential targets and gene expression. (A)
   drug-target network. PPI = protein–protein associations obtained using
   STRING database.The network was created using Cytoscape version
   3.7.2^[117]15. (B) Fold changes of genes showing consistent
   differential expression of mice in high radiation doses (i.e., 10 Gy
   and 12 Gy) from [118]GSE10640, [119]GSE6874, and [120]GSE104121.
   Experiment 1 and 2 were from BLK mice 24 h post-irradiation with 10 Gy,
   Experiment 3–6 were from BalbC (3,5,6) and BLK (4) 6hrs
   post-irradiation with 10 Gy, and Experiment 7 was performed on BalbC
   mice 6hrs post-irradiation with 12 Gy.

Discussion

   The onset of Acute Radiation Syndrome (ARS) upon exposure to high doses
   of radiation in a short period is a burgeoning problem as access to
   highly radioactive substances becomes ubiquitous. Thus, the importance
   of characterizing and treating this disease is an emerging endeavor. As
   such, we created gene correlation networks to help identify hub genes
   across multiple transcriptomic data for ARS to identify potential
   therapeutic options. Our transcriptomics-based gene networks were
   enriched with biological processes, including immune response and
   immune system process that were supported by another study^[121]40.
   While this study provided an overview of potential treatments for ARS
   there were significant limitations. The use of multiple species,
   multiple time points and multiple radiation doses means our predictions
   are a holistic screening of potential treatments for ARS. Within the
   immune response, pathways related to Blood Coagulation and platelet
   activation, signaling and aggregation were enriched processes in the
   networks. It has been shown previously that coagulopathy and lower
   platelet counts are physiologically-relevant symptoms of acute
   radiation^[122]32. Identification of genes that have been independently
   shown as relevant to ARS provides partial validation to the constructed
   networks. Evidence was found for eight hub genes identified as
   potential targets (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70,
   IDH3B), that altering their expression may induce radioprotective
   effects. These eight genes and associated drugs that have the potential
   for repurposing are discussed below.

Upregulated genes

   Bromodomain-Containing Protein 4 (BRD4): BRD4 Inhibition was shown to
   condense chromatin structure and decrease the DNA damage signaling
   pathway resulting in less DNA repair. Further, inhibition of BRD4
   demonstrated a relaxation of the chromatin structure and more repair
   signalling^[123]17. Thus, inhibition of BRD4 using CPI-0610, a phase
   two clinical trial molecule, could result in a better prognosis for ARS
   patients. NF-kB inhibitor alpha (NFKBIA): NF-kB inhibition has been
   shown to increase apoptosis through the loss of regulation of the JNK
   pathway^[124]22. Additionally, increased apoptotic activity is induced
   by irradiation^[125]41. Thus inhibition of NFKBIA by Acetylsalicylic
   acid could increase NF-kB activity and confer an antiapoptotic state
   for the irradiated cells. Cyclin-dependent kinase inhibitor alpha
   (CDKN1A): It has been shown that active and overexpressed CDKN1A
   increases cell cycle arrest in the G1 phase^[126]29. Arrest in this
   phase of the cell cycle was seen in irradiated cells, and cell cycle
   arrest is a known promoter of apoptosis^[127]30. A preclinical compound
   GGTI-298 is under investigation that can inhibit CDKN1A. Tissue Factor
   Pathway inhibitor (TFPI): It has been shown that inhibition of TFPI
   reduces hemophilia bleeding through activating coagulation
   pathways^[128]31. Since hemophilia is an indication of
   Radiation-Induced Disseminated Intravascular Coagulopathy (RDIC) that
   fresh frozen plasma, platelets or cryoprecipitated antihemophilic
   factor (AHF) ^[129]30. It is possible that inhibition of TFPI or its
   combination with human derived products such as AHF may improve the
   patients’ outcome by mitigating the effects of hemophilia in ARS.
   FDA-approved drug, dalteparin, is an inhibitor available for TFPI.
   Matrix Metallopeptidase 9 (MMP9): has been suggested to increase
   fibrosis following damage^[130]42; fibrosis is increased in cells that
   receive ionizing radiation^[131]43. Additionally, captopril is an
   FDA-approved and known inhibitor of MMP9^[132]13 that improves the
   survival of mice when exposed to high doses of radiation^[133]33. This
   protection was highly dependent on the timing of the treatment and
   suggests further time based inquiries of the data may elucidate time
   dependent physiological manifestations of ARS.

Downregulated genes

   Carbonyl Reductase 1 (CBR1) encodes a protein that reduces the level of
   reactive oxygen species, especially lipid aldehydes. Further, it has
   been shown that CBR1 confers radioprotectivity to head and neck
   squamous cell carcinomas^[134]35. Thus increasing the activity level of
   CBR1 may decrease the oxidative stress induced by radiation.
   FDA-approved acetohexamide currently used to treat type II diabetes can
   be used as an activator of CBR1. Zeta Chain of T Cell Receptor
   Associated Protein Kinase 70 (ZAP70) regulates T-cell signaling via
   interaction with cytoplasmic tyrosine kinases^[135]44. Loss-of-function
   in ZAP70 has been shown to lead to a systemic loss of functional T
   cells^[136]36 in mice post radiation^[137]37. Thus, we hypothesize that
   upregulating ZAP70 may lead to regaining functional T cells following
   radiation. Staurosporine is an experimental drug known to modulate the
   ZAP70 function and could be further explored as a potential repurposing
   option for ARS. Finally, Isocitrate Dehydrogenase (NAD( +)) 3 Beta
   (IDH3B) facilitates the transfer of Hydrogen from Isocitrate to NAD(+);
   exogenous NADH has been shown to protect mice intestines after
   radiation injury^[138]38. Thus, we hypothesize that the increase of
   IDH3B can lead to higher levels of NADH in the system and thus improve
   the prognosis following radiation injury. The compound coenzyme I (in
   Phase 2 of clinical trials) is indeed known to interact with IDH3B and
   could be explored for repurposing in ARS.

   In addition to the above-discussed genes, all other reported genes
   elucidate a physiological state that is induced under various radiation
   regiments. We predicted the possible activation of 12 other genes that
   have drugs approved by the FDA or in preclinical and clinical phases.
   Based on published literature, the activation of these genes may have
   radioprotective properties, and thus the development of corresponding
   activators may be useful. These include MYD88 (Myeloid Differentiation
   Primary Response Protein), which is found as upregulated in response to
   radiation and has been found to protect against radiation
   injuries^[139]16. The upregulation of Aurora Kinase A (AURKA) may also
   lead to protective properties against radiation as its inhibition has
   been found to increase radiosensitivity in hepatocellular
   carcinoma^[140]18. Similarly, activation of Protein Tyrosine
   Phosphatase Non-receptor Type 1 (PTPN1) has been found to increase
   survival in mice after irradiation. Finally, Nicotinamide
   N-Methyltransferase (NNMT) has been found to protect cancer cells from
   radiation.

   Further delineation based on timing and dose could further specify
   which treatment and response best suit the target. Additional studies
   can be conducted to determine the efficacy of modulating these genes
   individually or in combination. Additionally, the data was garnered
   primarily from mice which will heavily influence the predicted
   treatments. Finally, while this modulation can confer protection
   against acute radiation syndrome, some of these genes can also provide
   radioprotective properties to tumor cells and hence may not represent a
   long-term treatment option for patients with ARS.

Conclusions

   This study uses systems-level gene associations integrated with
   multiple biological information levels and provides a new perspective
   for the treatment of Acute Radiation Syndrome (ARS). The study
   identified eight genes with existing drugs and relevance to ARS, which
   may serve as high confidence drug target candidates for drug
   repurposing, potentially providing treatment of ARS.

Dataset collection

   Gene expression datasets [141]GSE104121^[142]45, [143]GSE10640^[144]46,
   and [145]GSE6874^[146]47 related to ARS were collected from the GEO
   database^[147]48. These datasets were generated from studies using
   humans and mice. In [148]GSE104121, peripheral whole blood samples were
   collected from 48 mice following 1–12 Gy total body irradiation (TBI),
   at 6 h, 16 h, 24 h, and 48 h after the treatment. [149]GSE10640 was
   generated using human and mouse samples. Human data were collected
   prior to and following the total body irradiation (TBI) of patients
   with 1.5–2 Gy. From this dataset we used 70 human samples (36 Pre
   Irradiation 34 post irradiation). These samples came from 6 h after TBI
   of 1.5–2.0 Gy in preparation for transplantation. The mouse data were
   collected following 6 h, 24 h, and 7 days of TBI with 0.5, 2, and
   10 Gy. [150]GSE6874 consists of two datasets [151]GSE6871 (human) and
   [152]GSE6873 (mouse). Human data were collected from 60 samples (33
   pre-irradiation and 27 post-irradiation) pre- and post-irradiated (with
   1.5–2 Gy TBI) patients. Mouse data were collected following TBI varied
   from 0.5 Gy to 10 Gy.

Differential gene expression analysis

   Differential gene expression analysis was conducted for each dataset
   using limma^[153]49 and affy^[154]50 R/Bioconductor packages. The
   comparison was made between healthy samples and the various conditions,
   including radiation dose and time post-irradiation. Additionally,
   within [155]GSE10640, there were multiple species of mice used (Balbc
   and Blk); hence, differential expression was performed between healthy
   Balbc vs. Irradiated and similarly for the Blk mice. A total of 32
   comparisons for differential expression analysis were performed across
   all five datasets (Supplementary Data [156]5). Two-fold change was
   considered as a significant differential expression.

Weighted gene coexpression network construction

   Prior to the construction of the network, datasets were quantile
   normalized using the limma package in R. Next, genes were filtered
   based on the coefficient of variation (CV) to remove spurious
   correlations among genes. A total of five networks were constructed
   consisting of two human (one from [157]GSE10640 one from [158]GSE6874),
   and three mice networks ([159]GSE10640, [160]GSE6874, [161]GSE104121).

Pearson’s correlation coefficient between genes within each dataset

   Assuming the biological networks show scale-free topology, the absolute
   correlation between genes was raised with a soft thresholding power
   (β) (Supplementary Data [162]1). All networks were fitted using an R^2
   cutoff of 0.80 ^[163]6. The clustering of genes was performed based on
   the dissimilarities of genes using topological overlap measure (TOM).
   Average Linkage Hierarchical clustering was used for module detection
   in each network^[164]6.

Hub genes identification

   Hub genes were identified in each correlation network characterized by
   the genes with the highest intramodular connectivity value^[165]6. This
   value determines the level of module membership a gene has; thus, a
   higher value of intramodular connectivity corresponds to more
   significant connectivity in the module. Genes among the top 20 percent
   were selected to ensure the highest confidence in the genes selected
   while simultaneously not introducing false positives by lowering the
   threshold.

Identification of known drugs and correlation with diseases

   To investigate if a hub gene is an existing drug target, we used the
   repurposing tool^[166]51 in the ConnectivityMap^[167]52 and DrugBank
   databases^[168]13. Additionally, we utilized UniProt ^[169]39 and OMIM
   databases^[170]53 to map genes to associated diseases.

Functional enrichment analysis

   Gene Ontology (GO) biological processes^[171]54, KEGG pathways^[172]55,
   and Reactome pathways^[173]56 terms were utilized for enrichment
   analysis.

   Enrichment of genes within each module in the networks was performed
   using the anRichment^[174]57 and anRichmentMethods packages in R.
   Pathways with Benjamini–Hochberg (FDR) value < 0.05 were considered to
   be enriched.

   A functional analysis of hub genes was performed using DAVID^[175]58,
   STRING^[176]59 and UniProt databases. In the DAVID database and STRING
   database, biological processes and pathways with FDR < 0.05 were
   considered as enriched.

Supplementary Information

   [177]Supplementary Information 1.^ (191.1KB, xlsx)
   [178]Supplementary Information 2.^ (629KB, docx)

Acknowledgements