Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.
{"title":"Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy.","authors":"Diya Sanjeev, Mejo George, Levin John, Athira Perunelly Gopalakrishnan, Pahal Priyanka, Spoorthi Mendon, Tanuja Yandigeri, Mahammad Nisar, Muhammad Nisar, Saptami Kanekar, Rex Devasahayam Arokia Balaya, Rajesh Raju","doi":"10.1089/omi.2023.0244","DOIUrl":"10.1089/omi.2023.0244","url":null,"abstract":"<p><p>Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140143889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01Epub Date: 2024-03-13DOI: 10.1089/omi.2024.0001
Gnanasekar Pranavathiyani, Archana Pan
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.
{"title":"Prediction of Essential Proteins of <i>Klebsiella pneumoniae</i> using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.","authors":"Gnanasekar Pranavathiyani, Archana Pan","doi":"10.1089/omi.2024.0001","DOIUrl":"10.1089/omi.2024.0001","url":null,"abstract":"<p><p><i>Klebsiella pneumoniae</i> 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 <i>K. pneumoniae</i> 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 <i>K. pneumoniae</i> 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 <i>K. pneumoniae</i>. 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.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140120227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-grade gliomas (HGGs) are extremely aggressive primary brain tumors with high mortality rates. Despite notable progress achieved by clinical research and biomarkers emerging from proteomics studies, efficacious drugs and therapeutic targets are limited. This study used targeted proteomics, in silico molecular docking, and simulation-based drug repurposing to identify potential drug candidates for HGGs. Importantly, we performed multiple reaction monitoring (MRM) on differentially expressed proteins with putative roles in the development and progression of HGGs based on our previous work and the published literature. Furthermore, in silico molecular docking-based drug repurposing was performed with a customized library of FDA-approved drugs to identify multitarget-directed ligands. The top drug candidates such as Pazopanib, Icotinib, Entrectinib, Regorafenib, and Cabozantinib were explored for their drug-likeness properties using the SwissADME. Pazopanib exhibited binding affinities with a maximum number of proteins and was considered for molecular dynamic simulations and cell toxicity assays. HGG cell lines showed enhanced cytotoxicity and cell proliferation inhibition with Pazopanib and Temozolomide combinatorial treatment compared to Temozolomide alone. To the best of our knowledge, this is the first study combining MRM with molecular docking and simulation-based drug repurposing to identify potential drug candidates for HGG. While the present study identified five multitarget-directed potential drug candidates, future clinical studies in larger cohorts are crucial to evaluate the efficacy of these molecular candidates. The research strategy and methodology used in the present study offer new avenues for innovation in drug discovery and development which may prove useful, particularly for cancers with low cure rates.
{"title":"Multitarget Potential Drug Candidates for High-Grade Gliomas Identified by Multiple Reaction Monitoring Coupled with <i>In Silico</i> Drug Repurposing.","authors":"Ayushi Verma, Rushda Patel, Atharva Mahale, Rujuta Vijay Thorat, Soumya Lipsa Rath, Epari Sridhar, Aliasgar Moiyadi, Sanjeeva Srivastava","doi":"10.1089/omi.2023.0256","DOIUrl":"10.1089/omi.2023.0256","url":null,"abstract":"<p><p>High-grade gliomas (HGGs) are extremely aggressive primary brain tumors with high mortality rates. Despite notable progress achieved by clinical research and biomarkers emerging from proteomics studies, efficacious drugs and therapeutic targets are limited. This study used targeted proteomics, <i>in silico</i> molecular docking, and simulation-based drug repurposing to identify potential drug candidates for HGGs. Importantly, we performed multiple reaction monitoring (MRM) on differentially expressed proteins with putative roles in the development and progression of HGGs based on our previous work and the published literature. Furthermore, <i>in silico</i> molecular docking-based drug repurposing was performed with a customized library of FDA-approved drugs to identify multitarget-directed ligands. The top drug candidates such as Pazopanib, Icotinib, Entrectinib, Regorafenib, and Cabozantinib were explored for their drug-likeness properties using the SwissADME. Pazopanib exhibited binding affinities with a maximum number of proteins and was considered for molecular dynamic simulations and cell toxicity assays. HGG cell lines showed enhanced cytotoxicity and cell proliferation inhibition with Pazopanib and Temozolomide combinatorial treatment compared to Temozolomide alone. To the best of our knowledge, this is the first study combining MRM with molecular docking and simulation-based drug repurposing to identify potential drug candidates for HGG. While the present study identified five multitarget-directed potential drug candidates, future clinical studies in larger cohorts are crucial to evaluate the efficacy of these molecular candidates. The research strategy and methodology used in the present study offer new avenues for innovation in drug discovery and development which may prove useful, particularly for cancers with low cure rates.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139697971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01Epub Date: 2024-01-25DOI: 10.1089/omi.2023.0173
Smrita Singh, K T Shreya Parthasarathi, Mohd Younis Bhat, Champaka Gopal, Jyoti Sharma, Akhilesh Pandey
Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (BAZ1B), WNK lysine-deficient protein kinase 1 (WNK1), and myosin light-chain kinase (MLCK) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (AAK1), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (HDGF), heat shock protein 90 (HSP90), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.
{"title":"Profiling Kinase Activities for Precision Oncology in Diffuse Gastric Cancer.","authors":"Smrita Singh, K T Shreya Parthasarathi, Mohd Younis Bhat, Champaka Gopal, Jyoti Sharma, Akhilesh Pandey","doi":"10.1089/omi.2023.0173","DOIUrl":"10.1089/omi.2023.0173","url":null,"abstract":"<p><p>Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (<i>BAZ1B</i>), <i>WNK</i> lysine-deficient protein kinase 1 (<i>WNK1</i>), and myosin light-chain kinase (<i>MLCK</i>) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (<i>AAK1</i>), to be hypophosphorylated. <i>LMNA</i> hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (<i>HDGF</i>), heat shock protein 90 (<i>HSP90</i>), and <i>FTH1</i> as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
卵巢癌是女性癌症死亡的主要原因。早期诊断和精准/个性化医疗对于降低卵巢癌的死亡率和发病率至关重要,而新的分子靶点也能加速药物的发现。我们在此报告一种基于差异共表达分析的综合系统生物学和机器学习(ML)方法,以确定浆液性卵巢癌的候选系统生物标志物(即基因模块)。因此,对四个独立的转录组数据集进行了独立的统计分析,并确定了常见的差异表达基因(DEGs)。利用这些 DEGs,揭示了共表达基因对。随后,重建了共表达基因对之间的差异共表达网络,从而确定了差异共表达基因模块。根据既定标准,"SOV-模块 "被确定为重要模块,由 19 个基因组成。利用独立的数据集,采用主成分分析(PCA)和 ML 技术评估了 SOV 模块的诊断能力。主成分分析的灵敏度和特异度分别为96.7%和100%,ML分析显示,在本研究样本中,区分表型的准确率高达100%。我们使用生存分析和 ML 分析评估了 SOV 模块的预后能力。我们发现,SOV 模块在预后方面的表现非常显著(p 值 = 1.36 × 10-4),使用 ML 技术区分存活和死亡的准确率为 63%。总之,所报告的候选基因组系统生物标志物为浆液性卵巢癌诊断和预后的个性化医疗提供了希望,值得进一步开展实验和临床转化研究。
{"title":"A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.","authors":"Medi Kori, Talip Yasir Demirtas, Betul Comertpay, Kazim Yalcin Arga, Raghu Sinha, Esra Gov","doi":"10.1089/omi.2023.0273","DOIUrl":"10.1089/omi.2023.0273","url":null,"abstract":"<p><p>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 (<i>p</i>-value = 1.36 × 10<sup>-4</sup>) 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.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139697970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01Epub Date: 2024-02-06DOI: 10.1089/omi.2023.0277
Aruldoss Immanuel, Ragothaman M Yennamalli, Venkatasubramanian Ulaganathan
Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.
{"title":"Targeting the Bottlenecks in Levan Biosynthesis Pathway in <i>Bacillus subtilis</i> and Strain Optimization by Computational Modeling and Omics Integration.","authors":"Aruldoss Immanuel, Ragothaman M Yennamalli, Venkatasubramanian Ulaganathan","doi":"10.1089/omi.2023.0277","DOIUrl":"10.1089/omi.2023.0277","url":null,"abstract":"<p><p>Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the <i>Bacillus subtilis</i> model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in <i>B. subtilis</i>. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered <i>B. subtilis</i> strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr<sup>-1</sup> at 20 mmol gDw<sup>-1</sup> hr<sup>-1</sup> of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in <i>B. subtilis</i>. Importantly, we found that the <i>pgk</i> and <i>ctaD</i> genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01Epub Date: 2024-01-29DOI: 10.1089/omi.2024.0003
Vural Özdemir
Climate emergency is a planetary health and systems science challenge because human health, nonhuman animal health, and the health of the planetary ecosystems are coproduced and interdependent. Yet, we live in a time when climate emergency is tackled by platitudes and weak reforms instead of structural and systems changes, and with tools of the very same systems and metanarratives, for example, infinite growth at all costs, that are causing climate change in the first place. Seeking solutions to problems from within the knowledge frames and metanarratives that are causing the problems reproduces the same problems across time and geographies. This article examines and underlines the importance of an epistemological gaze on knowledge economy, an epistemological X-ray, as another solution in the toolbox of decolonial and other social justice struggles in an era of climate emergency. Epistemology questions and excavates the metanarratives embedded in knowledge forms that are popular, dominant, and hegemonic as well as knowledges that are silent, omitted, or erased. In this sense, epistemology does not take the "archives" of data and knowledge for granted but asks questions such as who, when, how, and with what and whose funding the archive was built, and what is included and left out? Epistemological choices made by innovators, funders, and knowledge actors often remain opaque in knowledge economies. Epistemology research is crucial for science and innovations to be responsive to planetary society and climate emergency and mindful of the social, political, neocolonial, and historical contexts of science and technology in the 21st century.
气候紧急情况是对地球健康和系统科学的挑战,因为人类健康、非人类动物健康和地球生态系统的健康是共同产生和相互依存的。然而,在我们所处的时代,应对气候紧急情况的方法是陈词滥调和软弱无力的改革,而不是结构性和系统性的变革,所使用的工具也正是导致气候变化的系统和元叙事,例如不惜一切代价的无限增长。从造成问题的知识框架和元叙事中寻求问题的解决方案,会在不同的时间和地域重现同样的问题。本文探讨并强调了对知识经济的认识论凝视--认识论 X 光--的重要性,它是气候紧急时代非殖民主义和其他社会正义斗争工具箱中的另一种解决方案。认识论质疑并挖掘流行的、主导的和霸权的知识形式中蕴含的元叙事,以及沉默的、遗漏的或被抹杀的知识。从这个意义上说,认识论并不把数据和知识的 "档案 "视为理所当然,而是要提出这样的问题:档案是由谁、何时、以何种方式、在何种资助下建立的?在知识经济时代,创新者、资助者和知识参与者做出的认识论选择往往是不透明的。认识论研究对于科学和创新顺应地球社会和气候紧急状况、关注 21 世纪科学技术的社会、政治、新殖民主义和历史背景至关重要。
{"title":"Technological Encounters in a Knowledge Economy: An Epistemic X-Ray.","authors":"Vural Özdemir","doi":"10.1089/omi.2024.0003","DOIUrl":"10.1089/omi.2024.0003","url":null,"abstract":"<p><p>Climate emergency is a planetary health and systems science challenge because human health, nonhuman animal health, and the health of the planetary ecosystems are coproduced and interdependent. Yet, we live in a time when climate emergency is tackled by platitudes and weak reforms instead of structural and systems changes, and with tools of the very same systems and metanarratives, for example, infinite growth at all costs, that are causing climate change in the first place. Seeking solutions to problems from within the knowledge frames and metanarratives that are causing the problems reproduces the same problems across time and geographies. This article examines and underlines the importance of an epistemological gaze on knowledge economy, an epistemological X-ray, as another solution in the toolbox of decolonial and other social justice struggles in an era of climate emergency. Epistemology questions and excavates the metanarratives embedded in knowledge forms that are popular, dominant, and hegemonic as well as knowledges that are silent, omitted, or erased. In this sense, epistemology does not take the \"archives\" of data and knowledge for granted but asks questions such as who, when, how, and with what and whose funding the archive was built, and what is included and left out? Epistemological choices made by innovators, funders, and knowledge actors often remain opaque in knowledge economies. Epistemology research is crucial for science and innovations to be responsive to planetary society and climate emergency and mindful of the social, political, neocolonial, and historical contexts of science and technology in the 21st century.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Checkpoint kinase 1 (CHK1), a serine/threonine kinase, plays a crucial role in cell cycle arrest and is a promising therapeutic target for drug development against cancers. CHK1 coordinates cell cycle checkpoints in response to DNA damage, facilitating repair of single-strand breaks, and maintains the genome integrity in response to replication stress. In this study, we employed an integrated computational and experimental approach to drug discovery and repurposing, aiming to identify a potent CHK1 inhibitor among existing drugs. An e-pharmacophore model was developed based on the three-dimensional crystal structure of the CHK1 protein in complex with CCT245737. This model, characterized by seven key molecular features, guided the screening of a library of drugs through molecular docking. The top 10% of scored ligands were further examined, with procaterol emerging as the leading candidate. Procaterol demonstrated interaction patterns with the CHK1 active site similar to CHK1 inhibitor (CCT245737), as shown by molecular dynamics analysis. Subsequent in vitro assays, including cell proliferation, colony formation, and cell cycle analysis, were conducted on gastric adenocarcinoma cells treated with procaterol, both as a monotherapy and in combination with cisplatin. Procaterol, in synergy with cisplatin, significantly inhibited cell growth, suggesting a potentiated therapeutic effect. Thus, we propose the combined application of cisplatin and procaterol as a novel potential therapeutic strategy against human gastric cancer. The findings also highlight the relevance of CHK1 kinase as a drug target for enhancing the sensitivity of cytotoxic agents in cancer.
{"title":"Cisplatin and Procaterol Combination in Gastric Cancer? Targeting Checkpoint Kinase 1 for Cancer Drug Discovery and Repurposing by an Integrated Computational and Experimental Approach.","authors":"Suchitha Giridhara Prema, Jaikanth Chandrasekaran, Saptami Kanekar, Mejo George, Thottethodi Subrahmanya Keshava Prasad, Rajesh Raju, Shobha Dagamajalu, Rex Devasahayam Arokia Balaya","doi":"10.1089/omi.2023.0163","DOIUrl":"10.1089/omi.2023.0163","url":null,"abstract":"<p><p>Checkpoint kinase 1 (CHK1), a serine/threonine kinase, plays a crucial role in cell cycle arrest and is a promising therapeutic target for drug development against cancers. CHK1 coordinates cell cycle checkpoints in response to DNA damage, facilitating repair of single-strand breaks, and maintains the genome integrity in response to replication stress. In this study, we employed an integrated computational and experimental approach to drug discovery and repurposing, aiming to identify a potent CHK1 inhibitor among existing drugs. An e-pharmacophore model was developed based on the three-dimensional crystal structure of the CHK1 protein in complex with CCT245737. This model, characterized by seven key molecular features, guided the screening of a library of drugs through molecular docking. The top 10% of scored ligands were further examined, with procaterol emerging as the leading candidate. Procaterol demonstrated interaction patterns with the CHK1 active site similar to CHK1 inhibitor (CCT245737), as shown by molecular dynamics analysis. Subsequent <i>in vitro</i> assays, including cell proliferation, colony formation, and cell cycle analysis, were conducted on gastric adenocarcinoma cells treated with procaterol, both as a monotherapy and in combination with cisplatin. Procaterol, in synergy with cisplatin, significantly inhibited cell growth, suggesting a potentiated therapeutic effect. Thus, we propose the combined application of cisplatin and procaterol as a novel potential therapeutic strategy against human gastric cancer. The findings also highlight the relevance of CHK1 kinase as a drug target for enhancing the sensitivity of cytotoxic agents in cancer.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139403907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Host-virus Protein-Protein Interactions (PPIs) play pivotal roles in biological processes crucial for viral pathogenesis and by extension, inform antiviral drug discovery and therapeutics innovations. Despite efforts to develop the Epstein-Barr virus (EBV)-host PPI network, there remain significant knowledge gaps and a limited number of interacting human proteins deciphered. Furthermore, understanding the dynamics of the EBV-host PPI network in the distinct lytic and latent viral stages remains elusive. In this study, we report a comprehensive map of the EBV-human protein interactions, encompassing 1752 human and 61 EBV proteins by integrating data from the public repository HPIDB (v3.0) as well as curated high-throughput proteomic data from the literature. To address the stage-specific nature of EBV infection, we generated two detailed subset networks representing the latent and lytic stages, comprising 747 and 481 human proteins, respectively. Functional and pathway enrichment analysis of these subsets uncovered the profound impact of EBV proteins on cancer. The identification of highly connected proteins and the characterization of intrinsically disordered and cancer-related proteins provide valuable insights into potential therapeutic targets. Moreover, the exploration of drug-protein interactions revealed notable associations between hub proteins and anticancer drugs, offering novel perspectives for controlling EBV pathogenesis. This study represents, to the best of our knowledge, the first comprehensive investigation of the two distinct stages of EBV infection using high-throughput datasets. This makes a contribution to our understanding of EBV-host interactions and provides a foundation for future drug discovery and therapeutic interventions.
{"title":"Epstein-Barr Virus: Human Interactome Reveals New Molecular Insights into Viral Pathogenesis for Potential Therapeutics and Antiviral Drug Discovery.","authors":"Deepak Krishnan, Sreeranjini Babu, Rajesh Raju, Mohanan Valiya Veettil, Thottethodi Subramanya Keshava Prasad, Chandran S Abhinand","doi":"10.1089/omi.2023.0241","DOIUrl":"10.1089/omi.2023.0241","url":null,"abstract":"<p><p>Host-virus Protein-Protein Interactions (PPIs) play pivotal roles in biological processes crucial for viral pathogenesis and by extension, inform antiviral drug discovery and therapeutics innovations. Despite efforts to develop the Epstein-Barr virus (EBV)-host PPI network, there remain significant knowledge gaps and a limited number of interacting human proteins deciphered. Furthermore, understanding the dynamics of the EBV-host PPI network in the distinct lytic and latent viral stages remains elusive. In this study, we report a comprehensive map of the EBV-human protein interactions, encompassing 1752 human and 61 EBV proteins by integrating data from the public repository HPIDB (v3.0) as well as curated high-throughput proteomic data from the literature. To address the stage-specific nature of EBV infection, we generated two detailed subset networks representing the latent and lytic stages, comprising 747 and 481 human proteins, respectively. Functional and pathway enrichment analysis of these subsets uncovered the profound impact of EBV proteins on cancer. The identification of highly connected proteins and the characterization of intrinsically disordered and cancer-related proteins provide valuable insights into potential therapeutic targets. Moreover, the exploration of drug-protein interactions revealed notable associations between hub proteins and anticancer drugs, offering novel perspectives for controlling EBV pathogenesis. This study represents, to the best of our knowledge, the first comprehensive investigation of the two distinct stages of EBV infection using high-throughput datasets. This makes a contribution to our understanding of EBV-host interactions and provides a foundation for future drug discovery and therapeutic interventions.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01Epub Date: 2023-12-27DOI: 10.1089/omi.2023.0276
Vural Özdemir
Predictive, Personalized, Preventive, and Participatory (P4) Medicine is embedded in the precision medicine conceptual framework to achieve the overarching goal of "the right drug, for the right patient, at the right dose, and at the right time." Science cultures and political determinants of health have normative and instrumental impacts on P4 medicine. Yet, since the age of Enlightenment in the 17th century, science and economics have been disarticulated from politics along the lines of classical liberalism, and with an ahistorical approach that continues into the 21st century. The consequence of this liberal disarticulation is that science is falsely and narrowly understood as an invariably technocratic and objective field. In the aftermath of the Covid-19 pandemic, it is clearer that political determinants of health are the causes-of-causes for disease and health. I propose that we need P5 medicine with a fifth P, political determinants of planetary health. The new "P" can engage not only with instrumental aspects of P4 medicine research and clinical implementation but also with the structural factors that are an integral part of the politics of the P4 medicine. For example, the living legacies of colonialism contribute to the unequal relationships in trade, labor, provision, and production of materials among nation-states and between the Global South and the Global North and shape the class struggles in contemporary society, science, and medicine. A decolonial politics of care in which the political determinants of planetary health are taken seriously is therefore crucial and relevant to building a robust, ethical, responsible, and just P5 medicine in the 21st century.
{"title":"Taking Political Determinants of Planetary Health Seriously: Expanding from P4 to P5 Medicine.","authors":"Vural Özdemir","doi":"10.1089/omi.2023.0276","DOIUrl":"10.1089/omi.2023.0276","url":null,"abstract":"<p><p>Predictive, Personalized, Preventive, and Participatory (P4) Medicine is embedded in the precision medicine conceptual framework to achieve the overarching goal of \"the right drug, for the right patient, at the right dose, and at the right time.\" Science cultures and political determinants of health have normative and instrumental impacts on P4 medicine. Yet, since the age of Enlightenment in the 17th century, science and economics have been disarticulated from politics along the lines of classical liberalism, and with an ahistorical approach that continues into the 21st century. The consequence of this liberal disarticulation is that science is falsely and narrowly understood as an invariably technocratic and objective field. In the aftermath of the Covid-19 pandemic, it is clearer that political determinants of health are the causes-of-causes for disease and health. I propose that we need P5 medicine with a fifth P, political determinants of planetary health. The new \"P\" can engage not only with instrumental aspects of P4 medicine research and clinical implementation but also with the structural factors that are an integral part of the politics of the P4 medicine. For example, the living legacies of colonialism contribute to the unequal relationships in trade, labor, provision, and production of materials among nation-states and between the Global South and the Global North and shape the class struggles in contemporary society, science, and medicine. A decolonial politics of care in which the political determinants of planetary health are taken seriously is therefore crucial and relevant to building a robust, ethical, responsible, and just P5 medicine in the 21st century.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139049104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}