Integrative Systems Biology Research Articles

Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension.

Integrative multiomics can elucidate pulmonary arterial hypertension (PAH) pathobiology, but procuring human PAH lung samples is rare. We leveraged transcriptomic profiling and deep phenotyping of the largest multicenter PAH lung biobank to date (96 disease and 52 control) by integration with clinicopathologic data, genome-wide association studies, Bayesian regulatory networks, single-cell transcriptomics, and pharmacotranscriptomics. We identified 2 potentially protective gene network modules associated with vascular cells, and we validated ASPN, coding for asporin, as a key hub gene that is upregulated as a compensatory response to counteract PAH. We found that asporin is upregulated in lungs and plasma of multiple independent PAH cohorts and correlates with reduced PAH severity. We show that asporin inhibits proliferation and transforming growth factor-β/phosphorylated SMAD2/3 signaling in pulmonary artery smooth muscle cells from PAH lungs. We demonstrate in Sugen-hypoxia rats that ASPN knockdown exacerbated PAH and recombinant asporin attenuated PAH. Our integrative systems biology approach to dissect the PAH lung transcriptome uncovered asporin as a novel protective target with therapeutic potential in PAH.

Dissecting the lung transcriptome of pulmonary fibrosis-associated pulmonary hypertension.

Integrative multiomics can help elucidate the pathophysiology of pulmonary fibrosis (PF)-associated pulmonary hypertension (PH) (PF-PH). Weighted gene coexpression network analysis (WGCNA) was performed on a transcriptomic dataset of explanted lung tissue from 116 patients with PF. Patients were stratified by pulmonary vascular resistance (PVR), and differential gene expression analysis was conducted. Gene modules were correlated with hemodynamics at the time of transplantation and tested for enrichment in the lung transcriptomics signature of an independent pulmonary arterial hypertension (PAH) cohort. We found 1,250 differentially expressed genes between high and low PVR groups. WGCNA identified that black and yellowgreen modules negatively correlated with PVR, whereas the tan and darkgrey modules are positively correlated with PVR in PF-PH. In addition, the tan module showed the strongest enrichment for an independent PAH gene signature, suggesting shared gene expression patterns between PAH and PF-PH. Pharmacotranscriptomic analysis using the Connectivity Map implicated the tan and darkgrey modules as potentially pathogenic in PF-PH, given their combined module signature demonstrated a high negative connectivity score for treprostinil, a medication used in the treatment of PF-PH, and a high positive connectivity score for bone morphogenetic protein (BMP) loss of function. Pathway enrichment analysis revealed that inflammatory pathways and oxidative phosphorylation were downregulated, whereas epithelial-mesenchymal transition was upregulated in modules associated with increased PVR. Our integrative systems biology approach to the lung transcriptome of PF with and without PH identified several PH-associated coexpression modules and gene targets with shared molecular features with PAH warranting further investigation to uncover potential new therapies for PF-PH.NEW & NOTEWORTHY An integrative systems biology approach that included transcriptomic analysis of explanted lung tissue from patients with pulmonary fibrosis (PF) with and without pulmonary hypertension (PH) undergoing lung transplantation, combined with hemodynamic correlation and pharmacotranscriptomics, identified modules of genes associated with pulmonary vascular disease severity. Comparison with an independent pulmonary arterial hypertension (PAH) dataset identified shared gene expression patterns between PAH and PF-PH.

Folylpolyglutamate synthetase inactivation in relapsed ALL induces a druggable folate metabolic vulnerability

AimsThe antifolate methotrexate (MTX) is an anchor drug used in acute lymphoblastic leukemia (ALL) with poorly understood chemoresistance mechanisms in relapse. Herein we find decreased folate polyglutamylation network activities and inactivating FPGS mutations, both of which could induce MTX resistance and folate metabolic vulnerability in relapsed ALL. MethodsWe utilized integrated systems biology analysis of transcriptomic and genomic data from relapse ALL cohorts to infer hidden ALL relapse drivers and related genetic alternations during clonal evolution. The drug sensitivity assay was used to determine the impact of relapse-specific FPGS mutations on sensitivity to different antifolates and chemotherapeutics in ALL cells. We used liquid chromatography-mass spectrometry (LC-MS) to quantify MTX and folate polyglutamate levels in folylpoly-γ-glutamate synthetase (FPGS) mutant ALL cells. Enzymatic activity and protein degradation assays were also conducted to characterize the catalytic properties and protein stabilities of FPGS mutants. An ALL cell line-derived mouse leukemia xenograft model was used to evaluate the in vivo impact of FPGS inactivation on leukemogenesis and sensitivity to the polyglutamatable antifolate MTX as well as non-polyglutamatble lipophilic antifolate trimetrexate (TMQ). ResultsWe found a significant decrease in folate polyglutamylation network activities during ALL relapse using RNA-seq data. Supported by functional evidence, we identified multifactorial mechanisms of FPGS inactivation in relapsed ALL, including its decreased network activity and gene expression, focal gene deletion, impaired catalytic activity, and increased protein degradation. These deleterious FPGS alterations induce MTX resistance and inevitably cause marked intracellular folate shrinkage, which could be efficiently targeted by a polyglutamylation-independent lipophilic antifolate TMQ in vitro and in vivo. ConclusionsMTX resistance in relapsed ALL relies on FPGS inactivation, which inevitably induces a folate metabolic vulnerability, allowing for an efficacious antifolate ALL treatment strategy that is based upon TMQ, thereby surmounting chemoresistance in relapsed ALL.

Discovery of a Therapeutic Agent for Glioblastoma Using a Systems Biology-Based Drug Repositioning Approach.

Glioblastoma (GBM), a highly malignant tumour of the central nervous system, presents with a dire prognosis and low survival rates. The heterogeneous and recurrent nature of GBM renders current treatments relatively ineffective. In our study, we utilized an integrative systems biology approach to uncover the molecular mechanisms driving GBM progression and identify viable therapeutic drug targets for developing more effective GBM treatment strategies. Our integrative analysis revealed an elevated expression of CHST2 in GBM tumours, designating it as an unfavourable prognostic gene in GBM, as supported by data from two independent GBM cohorts. Further, we pinpointed WZ-4002 as a potential drug candidate to modulate CHST2 through computational drug repositioning. WZ-4002 directly targeted EGFR (ERBB1) and ERBB2, affecting their dimerization and influencing the activity of adjacent genes, including CHST2. We validated our findings by treating U-138 MG cells with WZ-4002, observing a decrease in CHST2 protein levels and a reduction in cell viability. In summary, our research suggests that the WZ-4002 drug candidate may effectively modulate CHST2 and adjacent genes, offering a promising avenue for developing efficient treatment strategies for GBM patients.

Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta-Analysis Using Systems Biology and Machine Learning Approaches.

The vaginal microbiome has a substantial role in the occurrence of preterm birth (PTB), which contributes substantially to neonatal mortality worldwide. However, current bioinformatics approaches mostly concentrate on the taxonomic classification and functional profiling of the microbiome, limiting their abilities to elucidate the complex factors that contribute to PTB. A total of 3757 vaginal microbiome 16S rRNA samples were obtained from five publicly available datasets. The sampleswere divided into two categories based on pregnancy outcome: preterm birth (PTB) (N = 966) and term birth (N = 2791). Additionally, the samples were further categorized based on the participants' race and trimester. The 16S rRNA reads were subjected to taxonomic classification and functional profiling using the Parallel-META 3 software in Ubuntu environment. The obtained abundances were analyzed using an integrated systems biology and machine learning approach to determine the key microbes, pathways, and genes that contribute to PTB. The resulting features were further subjected tostatistical analysis to identify the top nine features with the greatest effect sizes. We identified nine significant features, namely Shuttleworthia, Megasphaera, Sneathia, proximal tubule bicarbonate reclamation pathway, systemic lupus erythematosus pathway, transcription machinery pathway, lepA gene, pepX gene, and rpoD gene. Their abundance variations were observed through the trimesters. Vaginal infections caused by Shuttleworthia, Megasphaera, and Sneathia and altered small metabolite biosynthesis pathways such as lipopolysaccharide folate and retinal may increase the susceptibility to PTB. The identified organisms, genes, pathways, and their networks may be specifically targeted for thetreatment of bacterial infections that increasePTB risk.

Integrated systems biology analysis of acute lymphoblastic leukemia: unveiling molecular signatures and drug repurposing opportunities.

Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by aberrant proliferation and accumulation of lymphoid precursor cells within the bone marrow. The tyrosine kinase inhibitor (TKI), imatinib mesylate, has played a significant role in the treatment of Philadelphia chromosome-positive ALL (Ph + ALL). However, the achievement of durable and sustained therapeutic success remains a challenge due to the development of TKI resistance during the clinical course.The primary objective of this investigation is to propose a novel and efficacious treatment approach through drug repositioning, targeting ALL and its Ph + subtype by identifying and addressing differentially expressed genes (DEGs). This study involves a comprehensive analysis of transcriptome datasets pertaining to ALL and Ph + ALL in order to identify DEGs associated with the progression of these diseases to identify possible repurposable drugs that target identified hub proteins.The outcomes of this research have unveiled 698 disease-related DEGs for ALL and 100 for Ph + ALL. Furthermore, a subset of drugs, specifically glipizide for Ph + ALL, and maytansine and isoprenaline for ALL, have been identified as potential candidates for therapeutic intervention. Subsequently, cytotoxicity assessments were performed to confirm the in vitro cytotoxic effects of these selected drugs on both ALL and Ph + ALL cell lines.In conclusion, this study offers a promising avenue for the management of ALL and Ph + ALL through drug repurposed drugs. Further investigations are necessary to elucidate the mechanisms underlying cell death, and clinical trials are recommended to validate the promising results obtained through drug repositioning strategies.

Elucidating a genomic signature associated with behavioral and executive function after moderate to severe pediatric TBI: a systems biology informed approach

Introduction: There is significant unexplained variability in behavioral and executive functioning after pediatric traumatic brain injury (TBI). Prior research indicates that there are likely genetic contributions; however, current research is limited. The purpose of this study is to use a systems biology informed approach to characterize the genomic signature related to behavioral and executive functioning ∼12 months after moderate through severe TBI in children.Methods: Participants were from two prospective cohorts of children with severe TBI (Cohort #1) and moderate-severe TBI and an orthopedic injury (OI) group (Cohort #2). Participants included 196 children (n = 72 and n = 124 total from each respective cohort), ranging in age between 0–17 years at the time of injury. In total, 86 children had severe TBI, 49 had moderate TBI, and 61 had an OI. Global behavioral functioning assessed via the Child Behavior Checklist and executive function assessed via the Behavioral Rating Inventory of Executive Function at ∼ 12 months post injury served as outcomes. To test for a genomic signature, we compared the number of nominally significant (p < 0.05) polymorphisms associated with the outcomes in our systems biology identified genes to a set 10,000 permutations using control genes (e.g., not implicated by systems biology). We used the ToppFun application from Toppgene Suite to identify enriched biologic processes likely to be associated with behavioral and executive function outcomes.Results: At 12 months post injury, injury type (TBI vs OI) by polymorphism interaction was significantly enriched in systems biology selected genes for behavioral and executive function outcomes, suggesting these genes form a genomic signature. Effect sizes of the associations from our genes of interest ranged from .2–.5 for the top 5% of variants. Systems biology analysis of the variants associated with the top 5% effect sizes indicated enrichment in several specific biologic processes and systems.Discussion: Findings indicate that a genomic signature may explain heterogeneity of behavioral and executive outcomes after moderate and severe TBI. This work provides the foundation for constructing genomic signatures and integrating systems biology and genetic information into future recovery, prognostic, and treatment algorithms.

Abstract 4490: Targeting NQO1 with Episesaminol: A new therapeutic option identified for colorectal cancer using integrated systems biology and structural biology-based approach

Abstract Introduction: Colon and/or rectal cancer together constitute colorectal cancer (CRC), the 2nd most lethal and the 3rd common cancer worldwide. Drug resistance to modern chemotherapies results in decreased therapeutic efficacy and CRC treatment failure. Herein, multi-layered strategies are used for a better identification of the newer therapeutics. Methodology: Different publicly available datasets were used for the identification of the differentially expressed genes (DEGs) between normal and CRC tissues. miRNAs, lncRNAs and small molecules interacting with those DEGs were mined from various databases. A complex network of interacting mRNA-miRNAs-lncRNAs-small molecules was created using the Cytoscape. Hub RNAs, significant modules and cliques were identified using Cytohubba and MClique plugins. The mRNAs from the top 30 cliques were analyzed by expression, oncoprint, survival and gene ontology. Polyphenols from the PhenolExplorer database were also assessed for their druggability and toxicity properties to finally obtain druggable polyphenols (DPs). A two-step molecular docking was done using PyRx for mRNAs from top 30 cliques and DPs and the top ten complexes were docked again using AutoDock. Molecular dynamics simulation (MDS) is performed for the best fit protein-proposed DP vs protein-known drug using GROMACS. Results: 1915 DEGs, 2609 miRNAs, 1411 lncRNAs and 8790 small molecules (other than selected DPs) interactions were considered for generating a comprehensive interaction network. The 27 mRNAs from the top 30 cliques were subjected to an array of analyses. NCAPD2, CDK4 and FASN were found to be involved in different stages of CRC. IRF2BPL, CMPK1, CCNF and UST were found as survival biomarkers, whereas FAT4 was found to be the only genetically altered mRNA. Functional enrichment supported the analysis revealing significant CRC-associated gene ontology as well as pathways. 56 DPs when docked with the DEGs, the two-step docking revealed NQO1-Episesaminol to be the best target-drug complex. Results of docking were comparable when MDS was performed for NOQ1•Episesaminol Vs NQO1•Doxorubicin (approved drug). Conclusion: In the current interaction network and MDS-based integrated analysis, significant druggable CRC biomarkers and their newer therapeutic solutions were identified. MDS reveals NQO1•Episesaminol as a potential target•drug combination for CRC. Citation Format: Sohini Chakraborty, Gaurav Sharma, Sricheta Karmakar, Prarthana Chatterjee, Saptarshi Sinha, Pradipta Ghosh, Satarupa Banerjee. Targeting NQO1 with Episesaminol: A new therapeutic option identified for colorectal cancer using integrated systems biology and structural biology-based approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4490.

Abstract 450: GCN2 protects cancer cells from hypertranslation and metabolic crisis

Abstract It is widely assumed that cancer cells rely on high levels of protein synthesis to support growth and proliferation. However, protein synthesis, including ribosome biogenesis, is a highly resource- and energy-demanding process that needs to be coordinated with protein degradation and metabolic programs. GCN2 is an evolutionarily conserved kinase that is primarily activated by uncharged tRNAs when amino acid levels drop and activates the Integrated Stress Response (ISR), which encompasses a global reduction in translation and increased amino acid uptake and generation. GCN2 has been shown to support cancer cell survival under conditions of nutrient scarcity. Here, we used an integrated systems biology approach to delineate the multifaceted role of GCN2 in cancer cells that do not suffer from nutrient shortages. We carried out a broad array of multiomic analyses (RNA-seq, TMT-labelling based proteomics, Ribo-seq, LC-MS metabolomics) of melanoma cells in which GCN2 was inhibited (GCN2iB) or genetically depleted (constitutive and inducible shRNA) in cell culture and in vivo in murine xenografts, supplemented by puromycinylation-based quantification of global protein synthesis. GCN2 inhibition in A375 cells triggered a transcriptome and proteome response that was dominated by the induction of processes linked to protein and ribosome synthesis while suppressing metabolic pathways such as glycolysis and the proteasome. We confirmed these observations in vivo via RNA-seq of tumor samples from mice xenografted with A375 cells in which GCN2 was knocked down by induced shRNA expression and from xenografted mice that were treated orally with GCN2iB. Ribo-seq further demonstrated that, upon GCN2 inhibition, A375 cells expand their translational machinery. Notably, we identified 50 ribosomal genes that were translated at higher rate in GCN2-inhibited cells and whose expression was regulated exclusively at the translational level. GCN2 inhibition/depletion also resulted in an approximately 1.8-fold increase in global protein synthesis and altered the druggable proteome. Moreover, metabolite profiling of GCN2iB-treated cells revealed a loss of metabolic homeostasis including substantial alterations in glycolysis and both amino acid and lipid metabolism. In addition, we observed a significant decrease in ATP levels despite increased translation of mitochondrial ETC complex proteins. Proteomic and translatomic analyses revealed a failure of cells to increase proteasomal subunits and to maintain the production of ECM proteins. Last, but not least, we did not observe a role of GCN2 in preventing or resolving ribosome collisions. Taken together, our findings reveal GCN2 as a master regulator of translation in cancer cells that do not suffer from nutrient scarcity that keeps protein synthesis in check to maintain metabolic homeostasis. Citation Format: Monica Roman-Trufero, Istvan T. Kleijn, Kevin Blighe, Paula Saavedra-Garcia, Jinglin Zhou, Abigail Gaffar, Marilena Christoforou, Mariia Yuneva, Audrey M. Michel, Glenn R. Masson, Vahid Shahrezaei, Holger W. Auner. GCN2 protects cancer cells from hypertranslation and metabolic crisis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 450.

Abstract 6220: TRIP13 augments pancreatic cancer progression- An integrated systems biology study

Abstract Background- Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer related deaths in the United States due to the lack of understanding of the complexity of disease, early diagnostic methods, and extremely poor survival. Despite great advancements in biomedical research, there are very limited modalities available for the early detection of PanCa and its treatment. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority research in the field. Our group has identified Thyroid Receptor Interacting Protein -13 (TRIP13) as an oncogenic protein which contributes to early events of PanCa. Methodology- High dimensional omics and structural elucidation of TRIP13 was conducted using publicly available databases like Consurf, Phosphosite, GTEx and GEPIA2. Molecular biology techniques were applied to cross validate all our bioinformatics data. qPCR, immunoblotting and IHC analysis was performed in progressive PDAC cell lines and human tissues to decipher the expression level of TRIP13 in various pathological staging including functional enrichment analysis using Linkdomics. Results- The structural elucidation analysis revealed that most of the functionally exposed residues, phosphorylation and ubiquitylation sites situated at AAA domain of TRIP13. As compared to normal, TRIP13 is significantly over expressed in pancreatic tumor samples. Pathological staging 2 and 1 (early stages) showed relatively higher expression of TRIP13 as compared advanced stage. Of the total 7 isoforms of TRIP13, only one isoform (TRIP13-001) has shown the over expression in pancreatic tumor samples and shown association with poor survival. In correlation studies, TRIP13 has shown positive association with other pancreatic cancer biomarkers (CEACAM5, S100A4, MUC1, MSLN, CA125). The functional enrichment analyses suggest that TRIP13 is involved in important patho-physiological pathways like DNA repair, viral carcinogenesis, cellular senescence, and cell cycle. Our wet lab experiments also support our computational biology observation. Conclusion- This integrated computational biology study suggests a potential role of TRIP13 in pancreatic cancer progression via modulation of important oncological pathways. Wet lab experimentations also validated the involvement of TRIP13 in pancreatic cancer progression. TRIP13 expression may be associated with patient prognosis. Thus, its inhibitory small molecules can be useful for boosting pancreatic cancer therapies in clinical settings. Key words- TRIP13, Pancreatic cancer, Early events of cancer, Integrative Biology, Transcriptomics, Computational biology Citation Format: Swati Dhasmana, Anupam Dhasmana, Stella Rios, Iris A. Perez, Sheema Khan, Farrukh Afaq, Upender Manne, Murali M. Yallapu, Subhash C. Chauhan. TRIP13 augments pancreatic cancer progression- An integrated systems biology study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6220.

Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives.

This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.

Integrative systems biology of wheat susceptibility to Fusarium graminearum uncovers a conserved gene regulatory network and identifies master regulators targeted by fungal core effectors

BackgroundPlant diseases are driven by an intricate set of defense mechanisms counterbalanced by the expression of host susceptibility factors promoted through the action of pathogen effectors. In spite of their central role in the establishment of the pathology, the primary components of plant susceptibility are still poorly understood and challenging to trace especially in plant-fungal interactions such as in Fusarium head blight (FHB) of bread wheat. Designing a system-level transcriptomics approach, we leveraged the analysis of wheat responses from a susceptible cultivar facing Fusarium graminearum strains of different aggressiveness and examined their constancy in four other wheat cultivars also developing FHB.ResultsIn this study, we describe unexpected differential expression of a conserved set of transcription factors and an original subset of master regulators were evidenced using a regulation network approach. The dual-integration with the expression data of pathogen effector genes combined with database mining, demonstrated robust connections with the plant molecular regulators and identified relevant candidate genes involved in plant susceptibility, mostly able to suppress plant defense mechanisms. Furthermore, taking advantage of wheat cultivars of contrasting susceptibility levels, a refined list of 142 conserved susceptibility gene candidates was proposed to be necessary host’s determinants for the establishment of a compatible interaction.ConclusionsOur findings emphasized major FHB determinants potentially controlling a set of conserved responses associated with susceptibility in bread wheat. They provide new clues for improving FHB control in wheat and also could conceivably leverage further original researches dealing with a broader spectrum of plant pathogens.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Abstract IA019: Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit

Abstract Breast tumors arise and progress via processes that involve intrinsic deregulation of epithelial cells and that also alter the composition and function of associated stromal and immune cells. Together, these tumor-intrinsic and microenvironmental changes enable malignant epithelial cells in the tumor to acquire key cancer hallmarks, including proliferation, migration, immune evasion and further evolution. The resulting collection of cancer and stromal cells comprise a complex, adaptive tumor ecosystem. Long-term therapeutic success will require that drugs attack malignant cells, favorably activate the immune system, and create a tumor microenvironment that does not support tumor (re)growth. Creating a process for treatment selection will require an integrated systems biology approach to develop, test and refine a robust computational framework that takes into account the states, signaling relationships, and spatial organization that determine the emergent properties inherent in complex interacting systems. Toward this goal, we have developed a coordinated experimental-computational approach designed to enable identification of effective treatment strategies. To examine therapeutic responses of diverse aspects of the tumor ecosystem, we have deployed a novel drug delivery microdevice that enables rapid, high-throughput assessment of the effects of multiple therapies on tumor cells and the surrounding microenvironment. When coupled with multiplex tissue imaging, this platform provides a comprehensive assessment of the state and spatial organization of the tumor ecosystem as it adapts to therapy. Initial studies demonstrated that many drugs designed to target malignant epithelial cells strongly impact stromal and immune cells, providing new insights into the importance of considering multiple aspects of the tumor ecosystem when designing effective therapeutic strategies. The resultant data are being used to guide development of a multiscale computational model that considers the functional relationships between different tumor components and can be used to predict the effects of therapies on diverse aspects of tumor ecosystems. In total, our approach has the potential to transform cancer treatments through rational identification of effective therapeutic strategies that lead to long-term durable control of tumors. Citation Format: Laura M. Heiser. Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA019.

Network pharmacology and molecular docking reveals the therapeutic mechanism of Musa acuminata against obsessive-compulsive disorder

Network pharmacology integrates systems biology and bioinformatics to explore the intricate relationships among drugs, targets, diseases, and other molecular docking used to explore the effect of Musa acuminata against obsessive-compulsive disorder (OCD). OCD is a behavioral and mental neurological condition. The occurrence of patients with OCD has increased in recent times due to both genetic and environmental factors. Genetic factors involve variations in certain genes that give instructions for proteins that react to or transport the neurotransmitter known as serotonin which has been associated with an increased risk of OCD. Thus, the present study aims to establish the therapeutic properties of M. acuminata, that is, banana against environment-induced OCD. The active components of M. acuminata were screened through IMPPAT 2.0, NPASS, and HMDB databases. Corresponding gene targets of active ingredients were found by using STITCH and comparative toxicogenomics database databases. Similarly, the genes involved in OCD were screened with the help of the Gene Cards and DisGeNET database. Utilizing the Gene Cards and DisGeNET database, 2,966 gene targets of OCD were obtained. The network interaction map of gene targets was created using STRING and the CytoHubba plugin of Cytoscape, and the top interacting targets were then filtered away. Finally, the prophylactic activity of active ingredients of M. acuminata against OCD was studied through a molecular docking strategy. This research utilized a network pharmacology-integrated molecular docking approach to study the therapeutic properties of M. acuminata against OCD.

Transcription Analysis of the THBS2 Gene through Regulation by Potential Noncoding Diagnostic Biomarkers and Oncogenes of Gastric Cancer in the ECM-Receptor Interaction Signaling Pathway: Integrated System Biology and Experimental Investigation.

Gastric cancer (GC) is the second most frequent cause of cancer-related death worldwide and the fourth most common malignancy. Despite significant improvements in patient survival over the past few decades, the prognosis for patients with GC remains dismal because of the high recurrence rate. In this comprehensive system biology and experimental investigation, we aimed to find new novel diagnostic biomarkers of GC through a regulatory RNA interaction network. Gene expression, coexpression, and survival analyses were performed using microarray and RNAseq datasets (analyzed by RStudio, GEPIA2, and ENCORI). RNA interaction analysis was performed using miRWalk and ENCORI online databases. Gene set enrichment analysis (GSEA) was performed to find related signaling pathways of up- and downregulated genes in the microarray dataset. Gene ontology and pathway enrichment analysis were performed by the enrichr database. Protein interaction analysis was performed by STRING online database. Validation of expression and coexpression analyses was performed using a qRT-PCR experiment. Based on bioinformatics analyses, THBS2 (FC: 7.14, FDR < 0.0001) has a significantly high expression in GC samples. lncRNAs BAIAP2-AS1, TSIX, and LINC01215 have RNA interaction with THBS2. BAIAP2-AS1 (FC: 1.44, FDR: 0.018), TSIX (FC: 1.34, FDR: 0.038), and LINC01215 (FC: 1.19, FDR: 0.046) have significant upregulation in GC samples. THBS2 has a significant role in the regulation of the ECM-receptor signaling pathway. miR-4677-5p has a significant RNA interaction with THBS2. The expression level of THBS2, BAIAP2-AS1, TSIX, and LINC01215 has a nonsignificant negative correlation with the survival rate of GC patients (HR: 0.28, logrank p: 0.28). qRT-PCR experiment validates mentioned bioinformatics expression analyses. BAIAP2-AS1 (AUC: 0.7136, p value: 0.0096), TSIX (AUC: 0.7456, p value: 0.0029), and LINC01215 (AUC: 0.7872, p value: 0.0005) could be acceptable diagnostic biomarkers of GC. BAIAP2-AS1, lncRNA LINC01215, lncRNA TSIX, and miR-4677-5p might modulate the ECM-receptor signaling pathway via regulation of THBS2 expression level, as the high-expressed noncoding RNAs in GC. Furthermore, mentioned lncRNAs could be considered potential diagnostic biomarkers of GC.

PROSPECTIVE MULTI-OMIC ANALYSIS OF HUMAN LONGEVITY COHORTS IDENTIFIES ANALYTE NETWORKS ASSOCIATED WITH LONGEVITY

Abstract Serum based biomarkers of longevity have long been sought to explain the mechanisms of healthy aging and longevity. Using a 1:3 case cohort design, the Longevity Consortium has produced untargeted mass spectrometry based proteomic and metabolomic datasets from serum of four cohorts with longevity status, defined as those that reached the age corresponding to the 98th percentile of survival using sex specific and birth cohort specific survival percentiles. The cohorts are the Osteoporotic Fractures in Men study, the Study of Osteoporotic Fractures, the Health, Aging, and Body Composition Study, and the Cardiovascular Health Study. In this study, we integrate metabolomics and proteomics using machine learning and system biology approaches to construct multi-omic signatures predictive of longevity and healthy aging. We identify networks enriched for biomarkers previously shown to be associated with longevity such as apolipoproteins, along with novel associations, and we further compare with our findings in a mouse omics LC dataset of molecular changes induced by life-extending interventions. We show substantial differences between male and female longevity networks. The study highlights the effectiveness of using integrative systems biology methods to capture the heterogeneity of underlying molecular aging phenotypes, in order to generate a robust signature of longevity. The identified biomarker signatures may have significant implications for the development of personalized interventions aimed at promoting healthy aging and preventing age-related diseases.

Editorial: Integrative systems biology and big data for agricultural improvement and understanding

Editorial: Integrative systems biology and big data for agricultural improvement and understanding

Integrated systems biology approach identifies gene targets for endothelial dysfunction.

Endothelial dysfunction (ED) is critical in the development and progression of cardiovascular (CV) disorders, yet effective therapeutic targets for ED remain elusive due to limited understanding of its underlying molecular mechanisms. To address this gap, we employed a systems biology approach to identify potential targets for ED. Our study combined multi omics data integration, with siRNA screening, high content imaging and network analysis to prioritise key ED genes and identify a pro- and anti-ED network. We found 26 genes that, upon silencing, exacerbated the ED phenotypes tested, and network propagation identified a pro-ED network enriched in functions associated with inflammatory responses. Conversely, 31 genes ameliorated ED phenotypes, pointing to potential ED targets, and the respective anti-ED network was enriched in hypoxia, angiogenesis and cancer-related processes. An independent screen with 17 drugs found general agreement with the trends from our siRNA screen and further highlighted DUSP1, IL6 and CCL2 as potential candidates for targeting ED. Overall, our results demonstrate the potential of integrated system biology approaches in discovering disease-specific candidate drug targets for endothelial dysfunction.