Analysis of the potential relationship between COVID-19 and Behcet's disease using transcriptome data.

To investigate the potential role of COVID-19 in relation to Behcet's disease (BD) and to search for relevant biomarkers. We used a bioinformatics approach to download transcriptomic data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and PBMCs of BD patients, screened the common differential genes between COVID-19 and BD, performed gene ontology (GO) and pathway analysis, and constructed the protein-protein interaction (PPI) network, screened the hub genes and performed co-expression analysis. In addition, we constructed the genes-transcription factors (TFs)-miRNAs network, the genes-diseases network and the genes-drugs network to gain insight into the interactions between the 2 diseases. We used the RNA-seq dataset from the GEO database (GSE152418, GSE198533). We used cross-analysis to obtain 461 up-regulated common differential genes and 509 down-regulated common differential genes, mapped the PPI network, and used Cytohubba to identify the 15 most strongly associated genes as hub genes (ACTB, BRCA1, RHOA, CCNB1, ASPM, CCNA2, TOP2A, PCNA, AURKA, KIF20A, MAD2L1, MCM4, BUB1, RFC4, and CENPE). We screened for statistically significant hub genes and found that ACTB was in low expression of both BD and COVID-19, and ASPM, CCNA2, CCNB1, and CENPE were in low expression of BD and high expression of COVID-19. GO analysis and pathway analysis was then performed to obtain common pathways and biological response processes, which suggested a common association between BD and COVID-19. The genes-TFs-miRNAs network, genes-diseases network and genes-drugs network also play important roles in the interaction between the 2 diseases. Interaction between COVID-19 and BD exists. ACTB, ASPM, CCNA2, CCNB1, and CENPE as potential biomarkers for 2 diseases.

Comparative spatial transcriptomic profiling of severe acute respiratory syndrome coronavirus 2 Delta and Omicron variants infections in the lungs of cynomolgus macaques.

Recently emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants are generally less pathogenic than previous strains. However, elucidating the molecular basis for pulmonary immune response alterations is challenging owing to the virus's heterogeneous distribution within complex tissue structure. Here, we revealed the spatial transcriptomic profiles of pulmonary microstructures at the SARS-CoV-2 infection site in the nine cynomolgus macaques upon inoculation with the Delta and Omicron variants. Delta- and Omicron-infected lungs had upregulation of genes involved in inflammation, cytokine response, complement, cell damage, proliferation, and differentiation pathways. Depending on the tissue microstructures (alveoli, bronchioles, and blood vessels), there were differences in the types of significantly upregulated genes in each pathway. Notably, a limited number of genes involved in cytokine and cell damage response were differentially expressed between bronchioles of the Delta- and Omicron-infection groups. These results indicated that despite a significant antigenic shift in SARS-CoV-2, the host immune response mechanisms induced by the variants were relatively consistent, with limited transcriptional alterations observed only in large airways. This study may aid in understanding the pathogenesis of SARS-CoV-2 and developing a clinical strategy for addressing immune dysregulation by identifying potential transcriptional biomarkers within pulmonary microstructures during infection with emerging variants.

Single-cell transcriptomic atlas of lung microvascular regeneration after targeted endothelial cell ablation.

We sought to define the mechanism underlying lung microvascular regeneration in a model of severe acute lung injury (ALI) induced by selective lung endothelial cell ablation. Intratracheal instillation of DT in transgenic mice expressing human diphtheria toxin (DT) receptor targeted to ECs resulted in ablation of >70% of lung ECs, producing severe ALI with near complete resolution by 7 days. Using single-cell RNA sequencing, eight distinct endothelial clusters were resolved, including alveolar aerocytes (aCap) ECs expressing apelin at baseline and general capillary (gCap) ECs expressing the apelin receptor. At 3 days post-injury, a novel gCap EC population emerged characterized by de novo expression of apelin, together with the stem cell marker, protein C receptor. These stem-like cells transitioned at 5 days to proliferative endothelial progenitor-like cells, expressing apelin receptor together with the pro-proliferative transcription factor, Foxm1, and were responsible for the rapid replenishment of all depleted EC populations by 7 days post-injury. Treatment with an apelin receptor antagonist prevented ALI resolution and resulted in excessive mortality, consistent with a central role for apelin signaling in EC regeneration and microvascular repair. The lung has a remarkable capacity for microvasculature EC regeneration which is orchestrated by newly emergent apelin-expressing gCap endothelial stem-like cells that give rise to highly proliferative, apelin receptor-positive endothelial progenitors responsible for the regeneration of the lung microvasculature.

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Identification of a sub-group of critically ill patients with high risk of intensive care unit-acquired infections and poor clinical course using a transcriptomic score.

BACKGROUND: The development of stratification tools based on the assessment of circulating mRNA of genes involved in the immune response is constrained by the heterogeneity of septic patients. The aim of this study is to develop a transcriptomic score based on a pragmatic combination of immune-related genes detected with a prototype multiplex PCR tool. METHODS: As training cohort, we used the gene expression dataset obtained from 176 critically ill patients enrolled in the REALISM study (NCT02638779) with various etiologies and still hospitalized in intensive care unit (ICU) at day 5-7. Based on the performances of each gene taken independently to identify patients developing ICU-acquired infections (ICU-AI) after day 5-7, we built an unweighted score assuming the independence of each gene. We then determined the performances of this score to identify a subgroup of patients at high risk to develop ICU-AI, and both longer ICU length of stay and mortality of this high-risk group were assessed. Finally, we validated the effectiveness of this score in a retrospective cohort of 257 septic patients. RESULTS: This transcriptomic score (TScore) enabled the identification of a high-risk group of patients (49%) with an increased rate of ICU-AI when compared to the low-risk group (49% vs. 4%, respectively), with longer ICU length of stay (13 days [95% CI 8-30] vs. 7 days [95% CI 6-9], p < 0.001) and higher ICU mortality (15% vs. 2%). High-risk patients exhibited biological features of immune suppression with low monocytic HLA-DR levels, higher immature neutrophils rates and higher IL10 concentrations. Using the TScore, we identified 160 high-risk patients (62%) in the validation cohort, with 30% of ICU-AI (vs. 18% in the low-risk group, p = 0.06), and significantly higher mortality and longer ICU length of stay. CONCLUSIONS: The transcriptomic score provides a useful and reliable companion diagnostic tool to further develop immune modulating drugs in sepsis in the context of personalized medicine.

Integrative Analysis of Metabolomic and Transcriptomic Data Reveals the Mechanism of Color Formation in Corms of Pinellia ternata.

Pinellia ternata (Thunb.) Breit. (P. ternata) is a very important plant that is commonly used in traditional Chinese medicine. Its corms can be used as medicine and function to alleviate cough, headache, and phlegm. The epidermis of P. ternata corms is often light yellow to yellow in color; however, within the range of P. ternata found in JingZhou City in Hubei Province, China, there is a form of P. ternata in which the epidermis of the corm is red. We found that the total flavonoid content of red P. ternata corms is significantly higher than that of yellow P. ternata corms. The objective of this study was to understand the molecular mechanisms behind the difference in epidermal color between the two forms of P. ternata. The results showed that a high content of anthocyanidin was responsible for the red epidermal color in P. ternata, and 15 metabolites, including cyanidin-3-O-rutinoside-5-O-glucoside, cyanidin-3-O-glucoside, and cyanidin-3-O-rutinoside, were screened as potential color markers in P. ternata through metabolomic analysis. Based on an analysis of the transcriptome, seven genes, including PtCHS1, PtCHS2, PtCHI1, PtDFR5, PtANS, PtUPD-GT2, and PtUPD-GT3, were found to have important effects on the biosynthesis of anthocyanins in the P. ternata corm epidermis. Furthermore, two transcription factors (TFs), bHLH1 and bHLH2, may have regulatory functions in the biosynthesis of anthocyanins in red P. ternata corms. Using an integrative analysis of the metabolomic and transcriptomic data, we identified five genes, PtCHI, PtDFR2, PtUPD-GT1, PtUPD-GT2, and PtUPD-GT3, that may play important roles in the presence of the red epidermis color in P. ternata corms.

The specialized pro-resolving lipid mediator Protectin D1 affects macrophages differentiation and activity in Adult-onset Still's disease and COVID-19, two hyperinflammatory diseases sharing similar transcriptomic profiles.

Introduction: COVID-19 and autoinflammatory diseases, such as Adult-onset Still's Disease (AOSD), are characterized by hyperinflammation, in which it is observed massive production and uncontrolled secretion of pro-inflammatory cytokines. The specialized pro-resolving lipid mediators (SPMs) family is one the most important processes counteracting hyperinflammation inducing tissue repair and homeostasis restoration. Among SPMs, Protectin D1 (PD1) is able to exert antiviral features, at least in animal models. The aim of this study was to compare the transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with AOSD and COVID-19 and to evaluate the role of PD1 on those diseases, especially in modulating macrophages polarization. Methods: This study enrolled patients with AOSD, COVID-19, and healthy donors HDs, undergoing clinical assessment and blood sample collection. Next-generation deep sequencing was performed to identify differences in PBMCs transcripts profiles. Plasma levels of PD1 were assessed by commercial ELISA kits. Monocyte-derived macrophages were polarized into M1 and M2 phenotypes. We analyzed the effect of PD1 on macrophages differentiation. At 10 days, macrophages were analyzed for surface expression of subtypes markers by flow cytometry. Cytokines production was measured in supernatants by Bio-Plex Assays. Results: In the transcriptomes from AOSD patients and COVID-19 patients, genes involved in inflammation, lipid catabolism, and monocytes activation were specifically dysregulated in AOSD and COVID-19 patients when compared to HDs. Patients affected by COVID-19, hospitalized in intensive care unit (ICU), showed higher levels of PD1 when compared to not-ICU hospitalized patients and HDs (ICU COVID-19 vs not-ICU COVID-19, p= 0.02; HDs vs ICU COVID-19, p= 0.0006). PD1 levels were increased in AOSD patients with SS ≥1 compared to patients with SS=0 (p=0.028) and HDs (p=0.048). In vitro treatment with PD1 of monocytes-derived macrophages from AOSD and COVID-19 patients induced a significant increase of M2 polarization vs control (p<0.05). Furthermore, a significant release of IL-10 and MIP-1ß from M2 macrophages was observed when compared to controls (p<0.05). Discussion: PD1 is able to induce pro-resolutory programs in both AOSD and COVID-19 increasing M2 polarization and inducing their activity. In particular, PD1-treated M2 macrophages from AOSD and COVID-19 patients increased the production of IL-10 and enhanced homeostatic restoration through MIP-1ß production.

Single-cell sequencing of PBMC characterizes the altered transcriptomic landscape of classical monocytes in BNT162b2-induced myocarditis.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been the most dangerous threat to public health worldwide for the last few years, which led to the development of the novel mRNA vaccine (BNT162b2). However, BNT162b2 vaccination is known to be associated with myocarditis. Here, as an attempt to determine the pathogenesis of the disease and to develop biomarkers to determine whether subjects likely proceed to myocarditis after vaccination, we conducted a time series analysis of peripheral blood mononuclear cells of a patient with BNT162b2-induced myocarditis. Single-cell RNA sequence analysis identified monocytes as the cell clusters with the most dynamic changes. To identify distinct gene expression signatures, we compared monocytes of BNT162b2-induced myocarditis with monocytes under various conditions, including SARS-CoV-2 infection, BNT162b2 vaccination, and Kawasaki disease, a disease similar to myocarditis. Representative changes in the transcriptomic profile of classical monocytes include the upregulation of genes related to fatty acid metabolism and downregulation of transcription factor AP-1 activity. This study provides, for the first time, the importance of classical monocytes in the pathogenesis of myocarditis following BNT162b2 vaccination and presents the possibility that vaccination affects monocytes, further inducing their differentiation and infiltration into the heart.

Comparison of disinfectants-induced gene expression profile: Potential adverse effects.

Despite their importance in combating the spread of the COVID-19 pandemic, adverse effects of disinfectants on human health, especially the respiratory system, have been of continuing concern to researchers. Considering that bronchi are the main target of sprayed disinfectants, we here treated the seven major active ingredients in disinfectant products accepted by the US EPA to human bronchial epithelial cells and determined the subtoxic levels. Then, we performed microarray analysis using total RNA obtained at the subtoxic level and designed a network representing disinfectant-induced cellular response using the KEGG pathway analysis technique. Polyhexamethylguanidine phosphate, a lung fibrosis inducer, was used as a reference material to verify the relationship between cell death and pathology. The derived results reveal potential adverse effects along with the need for an effective application strategy for each chemical.

A Counterintuitive Neutrophil-Mediated Pattern in COVID-19 Patients Revealed through Transcriptomics Analysis.

The COVID-19 pandemic has persisted for almost three years. However, the mechanisms linked to the SARS-CoV-2 effect on tissues and disease severity have not been fully elucidated. Since the onset of the pandemic, a plethora of high-throughput data related to the host transcriptional response to SARS-CoV-2 infections has been generated. To this end, the aim of this study was to assess the effect of SARS-CoV-2 infections on circulating and organ tissue immune responses. We profited from the publicly accessible gene expression data of the blood and soft tissues by employing an integrated computational methodology, including bioinformatics, machine learning, and natural language processing in the relevant transcriptomics data. COVID-19 pathophysiology and severity have mainly been associated with macrophage-elicited responses and a characteristic "cytokine storm". Our counterintuitive findings suggested that the COVID-19 pathogenesis could also be mediated through neutrophil abundance and an exacerbated suppression of the immune system, leading eventually to uncontrolled viral dissemination and host cytotoxicity. The findings of this study elucidated new physiological functions of neutrophils, as well as tentative pathways to be explored in asymptomatic-, ethnicity- and locality-, or staging-associated studies.

Predicting activatory and inhibitory drug-target interactions based on structural compound representations and genetically perturbed transcriptomes.

A computational approach to identifying drug-target interactions (DTIs) is a credible strategy for accelerating drug development and understanding the mechanisms of action of small molecules. However, current methods to predict DTIs have mainly focused on identifying simple interactions, requiring further experiments to understand mechanism of drug. Here, we propose AI-DTI, a novel method that predicts activatory and inhibitory DTIs by combining the mol2vec and genetically perturbed transcriptomes. We trained the model on large-scale DTIs with MoA and found that our model outperformed a previous model that predicted activatory and inhibitory DTIs. Data augmentation of target feature vectors enabled the model to predict DTIs for a wide druggable targets. Our method achieved substantial performance in an independent dataset where the target was unseen in the training set and a high-throughput screening dataset where positive and negative samples were explicitly defined. Also, our method successfully rediscovered approximately half of the DTIs for drugs used in the treatment of COVID-19. These results indicate that AI-DTI is a practically useful tool for guiding drug discovery processes and generating plausible hypotheses that can reveal unknown mechanisms of drug action.

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

Transcriptomics unravels molecular changes associated with cilia and COVID-19 in chronic rhinosinusitis with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common upper respiratory tract complication where the pathogenesis is largely unknown. Herein, we investigated the transcriptome profile in nasal mucosa biopsies of CRSwNP patients and healthy individuals. We further integrated the transcriptomics data with genes located in chromosomal regions containing genome-wide significant gene variants for COVID-19. Among the most significantly upregulated genes in polyp mucosa were CCL18, CLEC4G, CCL13 and SLC9A3. Pathways involving "Ciliated epithelial cells" were the most differentially expressed molecular pathways when polyp mucosa and non-polyp mucosa from the same patient was compared. Natural killer T-cell (NKT) and viral pathways were the most statistically significant pathways in the mucosa of CRSwNP patients compared with those of healthy control individuals. Upregulated genes in polyp mucosa, located within the genome-wide associated regions of COVID-19, included LZTFL1, CCR9, SLC6A20, IFNAR1, IFNAR2 and IL10RB. Interestingly, the second most over-expressed gene in our study, CLEC4G, has been shown to bind directly to SARS-CoV-2 spike's N-terminal domain and mediate its entry and infection. Our results on altered expression of genes related to cilia and viruses point to the de-regulation of viral defenses in CRSwNP patients, and may give clues to future intervention strategies.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace.

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells.

Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.

Analysing transcriptomic signatures and identifying potential genes for the protective effect of inactivated COVID-19 vaccines.

Inactivated vaccines are one of the most effective strategies for controlling the coronavirus disease 2019 (COVID-19) pandemic. However, the response genes for the protective effect of inactivated vaccines are still unclear. Herein, we analysed the neutralization antibody responses elicited by vaccine serum and carried out transcriptome sequencing of RNAs isolated from the PBMCs of 29 medical staff receiving two doses of the CoronaVac vaccine. The results showed that SARS-CoV-2 neutralization antibody titers varied considerably among individuals, and revealed that many innate immune pathways were activated after vaccination. Furthermore, the blue module revealed that NRAS, YWHAB, SMARCA5, PPP1CC and CDC5L may be correlated with the protective effect of the inactivated vaccine. Additionally, MAPK1, CDC42, PPP2CA, EP300, YWHAZ and NRAS were demonstrated as the hub genes having a significant association with vaccines. These findings provide a basis for understanding the molecular mechanism of the host immune response induced by inactivated vaccines.

Transcriptomic and antiviral analyses of PoIFN-Delta5 against porcine enteric viruses in porcine intestinal epithelial cells.

The interferon-delta family was first reported in domestic pigs and belongs to the type I interferon (IFN-I) family. The enteric viruses could cause diarrhea in newborn piglets with high morbidity and mortality. We researched the function of the porcine IFN-delta (PoIFN-δ) family in the porcine intestinal epithelial cells (IPEC-J2) cells infected with porcine epidemic diarrhea virus (PEDV). Our study found that all PoIFN-δs shared a typical IFN-I signature and could be divided into five branches in the phylogenic tree. Different strains of PEDV could induce typical IFN transitorily, and the virulent strain AH2012/12 had the strongest induction of porcine IFN-δ and IFN-alpha (PoIFN-α) in the early stage of infection. In addition, it was found that PoIFN-δ5/6/9/11 and PoIFN-δ1/2 were highly expressed in the intestine. PoIFN-δ5 had a better antiviral effect on PEDV compared to PoIFN-δ1 due to its higher induction of ISGs. PoIFN-δ1 and PoIFN-δ5 also activated JAK-STAT and IRS signaling. For other enteric viruses, transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV), and porcine rotavirus (PoRV), PoIFN-δ1 and PoIFN-δ5 both showed an excellent antiviral effect. Transcriptome analyses uncovered the differences in host responses to PoIFN-α and PoIFN-δ5 and revealed thousands of differentially expressed genes were mainly enriched in the inflammatory response, antigen processing and presentation, and other immune-related pathways. PoIFN-δ5 would be a potential antiviral drug, especially against porcine enteric viruses. These studies were the first to report the antiviral function against porcine enteric viruses and broaden the new acquaintances of this type of interferon though not novelly discovered.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Long term exposure of cigarette smoke condensate (CSC) mediates transcriptomic changes in normal human lung epithelial Beas-2b cells and protection by garlic compounds.

Chronic cigarette smoke condensate (CSC) exposure is one of the preventable risk factors in the CS-induced lung cancer. However, understanding the mechanism of cellular transformation induced by CS in the lung remains limited. We investigated the effect of long term exposure of CSC in human normal lung epithelial Beas-2b cells, and chemopreventive mechanism of organosulphur garlic compounds, diallyl sulphide (DAS) and diallyl disulphide (DADS) using Next Generation Sequencing (NGS) transcriptomic analysis. CSC regulated 1077 genes and of these 36 genes are modulated by DAS while 101 genes by DADS. DAS modulated genes like IL1RL1 (interleukin-1 receptor like-1), HSPA-6 (heat shock protein family A, member 6) while DADS demonstrating ADTRP (Androgen-Dependent TFPI Regulating Protein), ANGPT4 (Angiopoietin 4), GFI1 (Growth Factor-Independent 1 Transcriptional Repressor), TBX2 (T-Box Transcription Factor 2), with some common genes like NEURL-1 (Neuralized E3-Ubiquitin Protein Ligase 1), suggesting differential effects between these two garlic compounds. They regulate genes by influencing pathways including HIF-1alpha, STAT-3 and matrix metalloproteases, contributing to the chemoprotective ability of organosulfur garlic compounds against CSC-induced cellular transformation. Taken together, we demonstrated CSC induced global gene expression changes pertaining to cellular transformation which potentially can be delayed with dietary chemopreventive phytochemicals like DS and DADS influencing alterations at the transcriptomic level.