201
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Chen C, Padi M. Flexible modeling of regulatory networks improves transcription factor activity estimation. NPJ Syst Biol Appl 2024; 10:58. [PMID: 38806476 PMCID: PMC11133322 DOI: 10.1038/s41540-024-00386-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024] Open
Abstract
Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.
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Affiliation(s)
- Chen Chen
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, AZ, USA
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA
| | - Megha Padi
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA.
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
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202
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Chujan S, Vajeethaveesin N, Satayavivad J, Kitkumthorn N. Identification of Molecular Mechanisms of Ameloblastoma and Drug Repositioning by Integration of Bioinformatics Analysis and Molecular Docking Simulation. Bioinform Biol Insights 2024; 18:11779322241256459. [PMID: 38812739 PMCID: PMC11135093 DOI: 10.1177/11779322241256459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 05/02/2024] [Indexed: 05/31/2024] Open
Abstract
Background Ameloblastoma (AM) is a benign tumor locally originated from odontogenic epithelium that is commonly found in the jaw. This tumor makes aggressive invasions and has a high recurrence rate. This study aimed to investigate the differentially expressed genes (DEGs), biological function alterations, disease targets, and existing drugs for AM using bioinformatics analysis. Methods The data set of AM was retrieved from the GEO database (GSE132474) and identified the DEGs using bioinformatics analysis. The biological alteration analysis was applied to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein-protein interaction (PPI) network analysis and hub gene identification were screened through NetworkAnalyst. The transcription factor-protein network was constructed via OmicsNet. We also identified candidate compounds from L1000CDS2 database. The target of AM and candidate compounds were verified using docking simulation. Results Totally, 611 DEGs were identified. The biological function enrichment analysis revealed glycosaminoglycan and GABA (γ-aminobutyric acid) signaling were most significantly up-regulated and down-regulated in AM, respectively. Subsequently, hub genes and transcription factors were screened via the network and showed FOS protein was found in both networks. Furthermore, we evaluated FOS protein to be a therapeutic target in AMs. Candidate compounds were screened and verified using docking simulation. Tanespimycin showed the greatest affinity binding value to bind FOS protein. Conclusions This study presented the underlying molecular mechanisms of disease pathogenesis, biological alteration, and important pathways of AMs and provided a candidate compound, Tanespimycin, targeting FOS protein for the treatment of AMs.
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Affiliation(s)
- Suthipong Chujan
- Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok, Thailand
- Center of Excellence on Environmental Health and Toxicology (EHT), Office of the Permanent Secretary (OPS), Ministry of Higher Education, Science, Research and Innovation (MHESI), Bangkok, Thailand
| | | | - Jutamaad Satayavivad
- Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok, Thailand
- Center of Excellence on Environmental Health and Toxicology (EHT), Office of the Permanent Secretary (OPS), Ministry of Higher Education, Science, Research and Innovation (MHESI), Bangkok, Thailand
| | - Nakarin Kitkumthorn
- Department of Oral Biology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand
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203
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Cheng N, Wang L, Liu Y, Song B, Ding C. HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction. J Chem Inf Model 2024; 64:4334-4347. [PMID: 38709204 PMCID: PMC11135324 DOI: 10.1021/acs.jcim.4c00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/07/2024]
Abstract
Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.
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Affiliation(s)
- Ning Cheng
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
| | - Li Wang
- Degree
Programs in Systems and information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - Yiping Liu
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Bosheng Song
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changsong Ding
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
- Big
Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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204
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Jardim LL, Schieber TA, Santana MP, Cerqueira MH, Lorenzato CS, Franco VKB, Zuccherato LW, da Silva Santos BA, Chaves DG, Ravetti MG, Rezende SM. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network. J Thromb Haemost 2024:S1538-7836(24)00303-9. [PMID: 38810700 DOI: 10.1016/j.jtha.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/02/2024] [Accepted: 05/12/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. OBJECTIVES To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.
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Affiliation(s)
- Letícia Lemos Jardim
- Instituto René Rachou (Fiocruz Minas), Belo Horizonte, Minas Gerais, Brazil; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Tiago A Schieber
- Faculdade de Ciências Econômicas, School of Economics, Universidade Federal de Minas Gerais, Brazil
| | | | | | | | | | | | | | | | - Martín Gomez Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Suely Meireles Rezende
- Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
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205
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Balzerani F, Blasco T, Pérez-Burillo S, Valcarcel LV, Hassoun S, Planes FJ. Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota. NPJ Syst Biol Appl 2024; 10:56. [PMID: 38802371 PMCID: PMC11130242 DOI: 10.1038/s41540-024-00381-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.
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Affiliation(s)
- Francesco Balzerani
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Telmo Blasco
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Sergio Pérez-Burillo
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcarcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA.
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, 02155, USA.
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
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206
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Rivera-Galindo MA, Aguirre-Garrido F, Garza-Ramos U, Villavicencio-Pulido JG, Fernández Perrino FJ, López-Pérez M. Relevance of the Adjuvant Effect between Cellular Homeostasis and Resistance to Antibiotics in Gram-Negative Bacteria with Pathogenic Capacity: A Study of Klebsiella pneumoniae. Antibiotics (Basel) 2024; 13:490. [PMID: 38927157 PMCID: PMC11200652 DOI: 10.3390/antibiotics13060490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Antibiotic resistance has become a global issue. The most significant risk is the acquisition of these mechanisms by pathogenic bacteria, which can have a severe clinical impact and pose a public health risk. This problem assumes that bacterial fitness is a constant phenomenon and should be approached from an evolutionary perspective to develop the most appropriate and effective strategies to contain the emergence of strains with pathogenic potential. Resistance mechanisms can be understood as adaptive processes to stressful conditions. This review examines the relevance of homeostatic regulatory mechanisms in antimicrobial resistance mechanisms. We focus on the interactions in the cellular physiology of pathogenic bacteria, particularly Gram-negative bacteria, and specifically Klebsiella pneumoniae. From a clinical research perspective, understanding these interactions is crucial for comprehensively understanding the phenomenon of resistance and developing more effective drugs and treatments to limit or attenuate bacterial sepsis, since the most conserved adjuvant phenomena in bacterial physiology has turned out to be more optimized and, therefore, more susceptible to alterations due to pharmacological action.
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Affiliation(s)
- Mildred Azucena Rivera-Galindo
- Doctorado en Ciencias Biológicas y de la Salud Universidad Autónoma Metropolitana, Ciudad de México, México Universidad Autónoma Metropolitana-Unidad Xochimilco Calz, del Hueso 1100, Coapa, Villa Quietud, Coyoacán CP 04960, Mexico;
| | - Félix Aguirre-Garrido
- Environmental Sciences Department, Division of Biological and Health Sciences, Autonomous Metropolitan University (Lerma Unit), Av. de las Garzas N◦ 10, Col. El Panteón, Lerma de Villada CP 52005, Mexico; (F.A.-G.); (J.G.V.-P.)
| | - Ulises Garza-Ramos
- Centro de Investigación Sobre Enfermedades Infecciosas (CISEI), Instituto Nacional de Salud Pública (INSP), Cuernavaca CP 62100, Mexico;
| | - José Geiser Villavicencio-Pulido
- Environmental Sciences Department, Division of Biological and Health Sciences, Autonomous Metropolitan University (Lerma Unit), Av. de las Garzas N◦ 10, Col. El Panteón, Lerma de Villada CP 52005, Mexico; (F.A.-G.); (J.G.V.-P.)
| | - Francisco José Fernández Perrino
- Department of Biotechnology, Division of Biological and Health Sciences, Universidad Autónoma Metropolitana-Unidad Iztapalapa, Av. San Rafael Atlixco 186, Leyes de Reforma, México City CP 09340, Mexico;
| | - Marcos López-Pérez
- Environmental Sciences Department, Division of Biological and Health Sciences, Autonomous Metropolitan University (Lerma Unit), Av. de las Garzas N◦ 10, Col. El Panteón, Lerma de Villada CP 52005, Mexico; (F.A.-G.); (J.G.V.-P.)
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207
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Ohtsuki S. Insulin receptor at the blood-brain barrier: Transport and signaling. VITAMINS AND HORMONES 2024; 126:113-124. [PMID: 39029970 DOI: 10.1016/bs.vh.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2024]
Abstract
The blood-brain barrier (BBB) is a unique system of the brain microvasculature that limits the exchange between the blood and the brain. Brain microvascular endothelial cells form the BBB as part of the neurovascular unit and express insulin receptors. The insulin receptor at the BBB has been studied in two different functional aspects. These functions include (1) the supplying of blood insulin to the brain and (2) the modulation of BBB function via insulin signaling. The first function involves drug delivery to the brain, while the second function is related to the association between central nervous system diseases and type 2 diabetes through insulin resistance. This chapter summarizes recent progress in research on the function of insulin receptors at the BBB.
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Affiliation(s)
- Sumio Ohtsuki
- Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
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208
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Zaunseder E, Mütze U, Okun JG, Hoffmann GF, Kölker S, Heuveline V, Thiele I. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab 2024:S1550-4131(24)00182-7. [PMID: 38834070 DOI: 10.1016/j.cmet.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/13/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.
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Affiliation(s)
- Elaine Zaunseder
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Ulrike Mütze
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Vincent Heuveline
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; Digital Metabolic Twin Centre, University of Galway, Ireland; Ryan Institute, University of Galway, Galway, Ireland; APC Microbiome Ireland, Cork, Ireland.
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209
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Kuraz Abebe B, Wang J, Guo J, Wang H, Li A, Zan L. A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle. Gene 2024; 908:148295. [PMID: 38387707 DOI: 10.1016/j.gene.2024.148295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/23/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.
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Affiliation(s)
- Belete Kuraz Abebe
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; Department of Animal Science, Werabe University, P.O. Box 46, Werabe, Ethiopia
| | - Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Juntao Guo
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Hongbao Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Anning Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China.
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210
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Marinaro G, Bruno L, Pirillo N, Coluccio ML, Nanni M, Malara N, Battista E, Bruno G, De Angelis F, Cancedda L, Di Mascolo D, Gentile F. The role of elasticity on adhesion and clustering of neurons on soft surfaces. Commun Biol 2024; 7:617. [PMID: 38778159 PMCID: PMC11111731 DOI: 10.1038/s42003-024-06329-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 05/14/2024] [Indexed: 05/25/2024] Open
Abstract
The question of whether material stiffness enhances cell adhesion and clustering is still open to debate. Results from the literature are seemingly contradictory, with some reports illustrating that adhesion increases with surface stiffness and others suggesting that the performance of a system of cells is curbed by high values of elasticity. To address the role of elasticity as a regulator in neuronal cell adhesion and clustering, we investigated the topological characteristics of networks of neurons on polydimethylsiloxane (PDMS) surfaces - with values of elasticity (E) varying in the 0.55-2.65 MPa range. Results illustrate that, as elasticity increases, the number of neurons adhering on the surface decreases. Notably, the small-world coefficient - a topological measure of networks - also decreases. Numerical simulations and functional multi-calcium imaging experiments further indicated that the activity of neuronal cells on soft surfaces improves for decreasing E. Experimental findings are supported by a mathematical model, that explains adhesion and clustering of cells on soft materials as a function of few parameters - including the Young's modulus and roughness of the material. Overall, results indicate that - in the considered elasticity interval - increasing the compliance of a material improves adhesion, improves clustering, and enhances communication of neurons.
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Affiliation(s)
- Giovanni Marinaro
- Center for Interdisciplinary Research on Medicines (CIRM), University of Liège, Quartier Hôpital, 4000, Liège, Belgium
| | - Luigi Bruno
- Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036, Rende, Italy
| | - Noemi Pirillo
- Nanotechnology Research Center, Department of Experimental and Clinical Medicine, University of "Magna Graecia" of Catanzaro, 88100, Catanzaro, Italy
| | - Maria Laura Coluccio
- Nanotechnology Research Center, Department of Experimental and Clinical Medicine, University of "Magna Graecia" of Catanzaro, 88100, Catanzaro, Italy
| | - Marina Nanni
- Department of Neuroscience and Brain Technologies, Italian Institute of Technology, Via Morego 30, 16163, Genoa, Italy
| | - Natalia Malara
- Department of Health Science, University of "Magna Graecia" of Catanzaro, 88100, Catanzaro, Italy
| | - Edmondo Battista
- Department of Innovative Technologies in Medicine & Dentistry, University "G. d'Annunzio" Chieti-Pescara, 66100, Chieti, Italy
| | - Giulia Bruno
- Plasmon Nanotechnologies, Italian Institute of Technology, Via Morego 30, 16163, Genoa, Italy
| | - Francesco De Angelis
- Plasmon Nanotechnologies, Italian Institute of Technology, Via Morego 30, 16163, Genoa, Italy
| | - Laura Cancedda
- Department of Neuroscience and Brain Technologies, Italian Institute of Technology, Via Morego 30, 16163, Genoa, Italy
| | - Daniele Di Mascolo
- Laboratory of Nanotechnology for Precision Medicine, Italian Institute of Technology, 16163, Genoa, Italy.
- Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126, Bari, Italy.
| | - Francesco Gentile
- Nanotechnology Research Center, Department of Experimental and Clinical Medicine, University of "Magna Graecia" of Catanzaro, 88100, Catanzaro, Italy.
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211
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Fang Y, Li X, Tian R. Unlocking Glioblastoma Vulnerabilities with CRISPR-Based Genetic Screening. Int J Mol Sci 2024; 25:5702. [PMID: 38891890 PMCID: PMC11171782 DOI: 10.3390/ijms25115702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover the genetic determinants of GBM progression and responsiveness to various therapies. Furthermore, we discuss the ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.
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Affiliation(s)
- Yitong Fang
- Department of Medical Neuroscience, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; (Y.F.); (X.L.)
- Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xing Li
- Department of Medical Neuroscience, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; (Y.F.); (X.L.)
- Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen 518055, China
| | - Ruilin Tian
- Department of Medical Neuroscience, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China; (Y.F.); (X.L.)
- Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen 518055, China
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Seo S, Tandon A, Lee KW, Lee JH, Park SH. Information Density Enhancement Using Lossy Compression in DNA Data Storage. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403071. [PMID: 38779945 DOI: 10.1002/adma.202403071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/06/2024] [Indexed: 05/25/2024]
Abstract
This study develops two deoxyribonucleic acid (DNA) lossy compression models, Models A and B, to encode grayscale images into DNA sequences, enhance information density, and enable high-fidelity image recovery. These models, distinguished by their handling of pixel domains and interpolation methods, offer a novel approach to data storage for DNA. Model A processes pixels in overlapped domains using linear interpolation (LI), whereas Model B uses non-overlapped domains with nearest-neighbor interpolation (NNI). Through a comparative analysis with Joint Photographic Experts Group (JPEG) compression, the DNA lossy compression models demonstrate competitive advantages in terms of information density and image quality restoration. The application of these models to the Modified National Institute of Standards and Technology (MNIST) dataset reveals their efficiency and the recognizability of decompressed images, which is validated by convolutional neural network (CNN) performance. In particular, Model B2, a version of Model B, emerges as an effective method for balancing high information density (surpassing over 20 times the typical densities of two bits per nucleotide) with reasonably good image quality. These findings highlight the potential of DNA-based data storage systems for high-density and efficient compression, indicating a promising future for biological data storage solutions.
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Affiliation(s)
- Seongjun Seo
- Department of Physics and Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Anshula Tandon
- Department of Physics and Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Keun Woo Lee
- DNASTech, Industry-Academic Cooperation Center, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Jee-Hyong Lee
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Sung Ha Park
- Department of Physics and Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea
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213
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Sha Z, Freda PJ, Bhandary P, Ghosh A, Matsumoto N, Moore JH, Hu T. Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis. RESEARCH SQUARE 2024:rs.3.rs-4392123. [PMID: 38826481 PMCID: PMC11142370 DOI: 10.21203/rs.3.rs-4392123/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system. Results Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis. Conclusions These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.
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Affiliation(s)
- Zhendong Sha
- School of Computing, Queen’s University, 557 Goodwin Hall, 21-25 Union St, Kingston, Ontario, K7L 2N8, Canada
| | - Philip J. Freda
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Priyanka Bhandary
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Attri Ghosh
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Nicholas Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, U.S.A
| | - Ting Hu
- School of Computing, Queen’s University, 557 Goodwin Hall, 21-25 Union St, Kingston, Ontario, K7L 2N8, Canada
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214
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Chen H, Fu X, Wu X, Zhao J, Qiu F, Wang Z, Wang Z, Chen X, Xie D, Huang J, Fan J, Yang X, Song Y, Li J, He D, Xiao G, Lu A, Liang C. Gut microbial metabolite targets HDAC3-FOXK1-interferon axis in fibroblast-like synoviocytes to ameliorate rheumatoid arthritis. Bone Res 2024; 12:31. [PMID: 38782893 PMCID: PMC11116389 DOI: 10.1038/s41413-024-00336-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/18/2024] [Accepted: 04/07/2024] [Indexed: 05/25/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease. Early studies hold an opinion that gut microbiota is environmentally acquired and associated with RA susceptibility. However, accumulating evidence demonstrates that genetics also shape the gut microbiota. It is known that some strains of inbred laboratory mice are highly susceptible to collagen-induced arthritis (CIA), while the others are resistant to CIA. Here, we show that transplantation of fecal microbiota of CIA-resistant C57BL/6J mice to CIA-susceptible DBA/1J mice confer CIA resistance in DBA/1J mice. C57BL/6J mice and healthy human individuals have enriched B. fragilis than DBA/1J mice and RA patients. Transplantation of B. fragilis prevents CIA in DBA/1J mice. We identify that B. fragilis mainly produces propionate and C57BL/6J mice and healthy human individuals have higher level of propionate. Fibroblast-like synoviocytes (FLSs) in RA are activated to undergo tumor-like transformation. Propionate disrupts HDAC3-FOXK1 interaction to increase acetylation of FOXK1, resulting in reduced FOXK1 stability, blocked interferon signaling and deactivation of RA-FLSs. We treat CIA mice with propionate and show that propionate attenuates CIA. Moreover, a combination of propionate with anti-TNF etanercept synergistically relieves CIA. These results suggest that B. fragilis or propionate could be an alternative or complementary approach to the current therapies.
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Affiliation(s)
- Hongzhen Chen
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
| | - Xuekun Fu
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China
| | - Xiaohao Wu
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Junyi Zhao
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
| | - Fang Qiu
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China
| | - Zhenghong Wang
- Institute of Plant and Food Science, Department of Biology, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Zhuqian Wang
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China
| | - Xinxin Chen
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
| | - Duoli Xie
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China
| | - Jie Huang
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China
| | - Junyu Fan
- Department of Rheumatology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu Yang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yi Song
- Institute of Plant and Food Science, Department of Biology, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jie Li
- Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, China
| | - Dongyi He
- Department of Rheumatology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guozhi Xiao
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China.
| | - Aiping Lu
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China.
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, 510006, China.
- Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| | - Chao Liang
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Shenzhen, 518055, China.
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, 999077, China.
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 100850, Beijing, China.
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Arora P, Sharma A, Trivedi R, Sharma P, Padhy S, Shah S, Dutta SK, Manda K, Rana P. Lipidomic Analysis Reveals Systemic Alterations in Servicemen Exposed to Repeated Occupational Low-Level Blast Waves. Mil Med 2024:usae268. [PMID: 38776149 DOI: 10.1093/milmed/usae268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/02/2024] [Accepted: 05/06/2024] [Indexed: 05/24/2024] Open
Abstract
INTRODUCTION Occupational exposure to blast is a prevalent risk experienced by military personnel. While low-level exposure may not manifest immediate signs of illness, prolonged and repetitive exposure may result in neurophysiological dysfunction. Such repeated exposure to occupational blasts has been linked to structural and functional modifications in the brain, adversely affecting the performance of servicemen in the field. These neurological changes can give rise to symptoms resembling concussion and contribute to the development of post-traumatic stress disorder. MATERIALS AND METHODS To understand long-term effects of blast exposure, the study was conducted to assess memory function, serum circulatory protein and lipid biomarkers, and associated concussive symptomology in servicemen. Concussion-like symptoms were assessed using the Rivermead Post-Concussion Symptoms Questionnaire (RPSQ) along with memory function using PGI memory scale. The serum protein biomarkers were quantified using a sandwich ELISA assay, and the serum lipid profile was measured using liquid chromatography-mass spectrometer. RESULTS The findings revealed that repeated low-level blast exposure resulted in impaired memory function, accompanied by elevated levels of serum neurofilament light chain (neuroaxonal injury) and C-reactive protein. Furthermore, alterations in the lipid profile were observed, with an increase in lipid species associated with immune activation. These changes collectively point to systemic inflammation, neuronal injury, and memory dysfunction as pathological characteristics of repeated low-level blast exposure. CONCLUSION The results of our preliminary investigation offer valuable insights for further large-scale study and provide a guiding principle that necessitates a suitable mitigation approach to safeguard the health of personnel against blast overpressure.
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Affiliation(s)
- Palkin Arora
- Radiological, Nuclear and Imaging Sciences (RNAIS), Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi 110054, India
| | - Apoorva Sharma
- Radiological, Nuclear and Imaging Sciences (RNAIS), Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi 110054, India
| | - Richa Trivedi
- Radiological, Nuclear and Imaging Sciences (RNAIS), Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi 110054, India
| | - Priyanka Sharma
- Radiological, Nuclear and Imaging Sciences (RNAIS), Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi 110054, India
| | - Sankarsan Padhy
- RADAR and Sensor Wing, Proof and Experimental Establishment (PXE), DRDO, Chandipur, Balasore, Odisha 756025, India
| | - Shahnawaj Shah
- RADAR and Sensor Wing, Proof and Experimental Establishment (PXE), DRDO, Chandipur, Balasore, Odisha 756025, India
| | - Suman K Dutta
- Military Wing, Proof and Experimental Establishment (PXE), DRDO, Chandipur, Balasore, Odisha 756025, India
| | - Kailash Manda
- Radiological, Nuclear and Imaging Sciences (RNAIS), Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi 110054, India
| | - Poonam Rana
- Radiological, Nuclear and Imaging Sciences (RNAIS), Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi 110054, India
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216
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Barutcu AR, Black MB, Samuel R, Slattery S, McMullen PD, Nong A. Integrating gene expression and splicing dynamics across dose-response oxidative modulators. Front Genet 2024; 15:1389095. [PMID: 38846964 PMCID: PMC11155298 DOI: 10.3389/fgene.2024.1389095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/06/2024] [Indexed: 06/09/2024] Open
Abstract
Toxicological risk assessment increasingly utilizes transcriptomics to derive point of departure (POD) and modes of action (MOA) for chemicals. One essential biological process that allows a single gene to generate several different RNA isoforms is called alternative splicing. To comprehensively assess the role of splicing dysregulation in toxicological evaluation and elucidate its potential as a complementary endpoint, we performed RNA-seq on A549 cells treated with five oxidative stress modulators across a wide dose range. Differential gene expression (DGE) showed limited pathway enrichment except at high concentrations. However, alternative splicing analysis revealed variable intron retention events affecting diverse pathways for all chemicals in the absence of significant expression changes. For instance, diazinon elicited negligible gene expression changes but progressive increase in the number of intron retention events, suggesting splicing alterations precede expression responses. Benchmark dose modeling of intron retention data highlighted relevant pathways overlooked by expression analysis. Systematic integration of splicing datasets should be a useful addition to the toxicogenomic toolkit. Combining both modalities paint a more complete picture of transcriptomic dose-responses. Overall, evaluating intron retention dynamics afforded by toxicogenomics may provide biomarkers that can enhance chemical risk assessment and regulatory decision making. This work highlights splicing-aware toxicogenomics as a possible additional tool for examining cellular responses.
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217
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Ruiz-Arenas C, Marín-Goñi I, Wang L, Ochoa I, Pérez-Jurado L, Hernaez M. NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders. Nucleic Acids Res 2024; 52:e44. [PMID: 38597610 PMCID: PMC11109970 DOI: 10.1093/nar/gkae197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/23/2024] [Accepted: 03/12/2024] [Indexed: 04/11/2024] Open
Abstract
Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.
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Affiliation(s)
- Carlos Ruiz-Arenas
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
- Department MELIS, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Irene Marín-Goñi
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Idoia Ochoa
- Department of Electrical and Electronics Engineering, Tecnun, University of Navarra, Donostia, Spain
- Institute for Data Science and Artificial Inteligence (DATAI), University of Navarra, Pamplona 31008, Spain
| | - Luis A Pérez-Jurado
- Department MELIS, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
- Genetics Service, Hospital del Mar & Hospital del Mar Research Institute (IMIM), Barcelona, Spain
| | - Mikel Hernaez
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
- Institute for Data Science and Artificial Inteligence (DATAI), University of Navarra, Pamplona 31008, Spain
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218
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Chen Y, Mao R, Xu J, Huang Y, Xu J, Cui S, Zhu Z, Ji X, Huang S, Huang Y, Huang HY, Yen SC, Lin YCD, Huang HD. A Causal Regulation Modeling Algorithm for Temporal Events with Application to Escherichia coli's Aerobic to Anaerobic Transition. Int J Mol Sci 2024; 25:5654. [PMID: 38891842 PMCID: PMC11171773 DOI: 10.3390/ijms25115654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli's (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism's metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli's AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.
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Affiliation(s)
- Yigang Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Runbo Mao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Jiatong Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Jingyi Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Shidong Cui
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Zihao Zhu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Xiang Ji
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Shenghan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yanzhe Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Shih-Chung Yen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yang-Chi-Duang Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
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219
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Bumbaca B, Birtwistle MR, Gallo JM. Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions. RESEARCH SQUARE 2024:rs.3.rs-4391296. [PMID: 38826227 PMCID: PMC11142360 DOI: 10.21203/rs.3.rs-4391296/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte- like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.
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Affiliation(s)
- Brandon Bumbaca
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson SC, USA
- Department of Bioengineering, Clemson University, Clemson SC, USA
| | - James M Gallo
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
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220
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Huan JM, Wang XJ, Li Y, Zhang SJ, Hu YL, Li YL. The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data. BioData Min 2024; 17:13. [PMID: 38773619 PMCID: PMC11110203 DOI: 10.1186/s13040-024-00365-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 05/17/2024] [Indexed: 05/24/2024] Open
Abstract
A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.
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Affiliation(s)
- Jia-Ming Huan
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xiao-Jie Wang
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuan Li
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Shi-Jun Zhang
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuan-Long Hu
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yun-Lun Li
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
- Department of Cardiovascular, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
- Precision Diagnosis and Treatment of Cardiovascular Diseases with Traditional Chinese Medicine Shandong Engineering Research Center, Jinan, 250355, China.
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221
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Bayazid AB, Lim BO. Therapeutic Effects of Plant Anthocyanin against Alzheimer's Disease and Modulate Gut Health, Short-Chain Fatty Acids. Nutrients 2024; 16:1554. [PMID: 38892488 PMCID: PMC11173718 DOI: 10.3390/nu16111554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/13/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and neurogenerative disease (NDD), and it is also one of the leading causes of death worldwide. The number of AD patients is over 55 million according to 2020 Alzheimer's Disease International (ADI), and the number is increasing drastically without any effective cure. In this review, we discuss and analyze the potential role of anthocyanins (ACNs) against AD while understanding the molecular mechanisms. ACNs have been reported as having neuroprotective effects by mitigating cognitive impairments, apoptotic markers, neuroinflammation, aberrant amyloidogenesis, and tauopathy. Taken together, ACNs could be an important therapeutic agent for combating or delaying the onset of AD.
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Affiliation(s)
- Al Borhan Bayazid
- Medicinal Biosciences, Department of Applied Biological Sciences, Graduate School, BK21 Program, Konkuk University, Chungju 27478, Republic of Korea
| | - Beong Ou Lim
- Medicinal Biosciences, Department of Applied Biological Sciences, Graduate School, BK21 Program, Konkuk University, Chungju 27478, Republic of Korea
- Human Bioscience Corporate R&D Center, Human Bioscience Corp., 268 Chungwondaero, Chungju 27478, Republic of Korea
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222
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Kim Y, Han Y, Hopper C, Lee J, Joo JI, Gong JR, Lee CK, Jang SH, Kang J, Kim T, Cho KH. A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations. CELL REPORTS METHODS 2024; 4:100773. [PMID: 38744288 PMCID: PMC11133856 DOI: 10.1016/j.crmeth.2024.100773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 03/19/2024] [Accepted: 04/19/2024] [Indexed: 05/16/2024]
Abstract
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
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Affiliation(s)
- Yunseong Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Younghyun Han
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Corbin Hopper
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jae Il Joo
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeong-Ryeol Gong
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Chun-Kyung Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seong-Hoon Jang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Junsoo Kang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Taeyoung Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
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223
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Liu W, Zhang J, Qiao G, Bian J, Dong B, Li Y. HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies. BMC Bioinformatics 2024; 25:196. [PMID: 38769492 DOI: 10.1186/s12859-024-05806-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.
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Affiliation(s)
- Wuyong Liu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China
| | - Jingyu Zhang
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Guanyu Qiao
- Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Jilong Bian
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China
| | - Benzhi Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China
| | - Yang Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China.
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224
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Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl 2024; 10:53. [PMID: 38760412 PMCID: PMC11101644 DOI: 10.1038/s41540-024-00378-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.
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Affiliation(s)
| | - Trivadi Sundaram Ganesan
- Department of Medical Oncology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Anbumathi Palanisamy
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, India.
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225
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Nguyen V, Li Y, Lu T. Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae. ACS Synth Biol 2024; 13:1442-1453. [PMID: 38657170 PMCID: PMC11103795 DOI: 10.1021/acssynbio.3c00542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.
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Affiliation(s)
- Viviana Nguyen
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Yifei Li
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Ting Lu
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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226
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Mishra S, Deewan A, Zhao H, Rao CV. Nitrogen starvation causes lipid remodeling in Rhodotorula toruloides. Microb Cell Fact 2024; 23:141. [PMID: 38760782 PMCID: PMC11102182 DOI: 10.1186/s12934-024-02414-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/30/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND The oleaginous yeast Rhodotorula toruloides is a promising chassis organism for the biomanufacturing of value-added bioproducts. It can accumulate lipids at a high fraction of biomass. However, metabolic engineering efforts in this organism have progressed at a slower pace than those in more extensively studied yeasts. Few studies have investigated the lipid accumulation phenotype exhibited by R. toruloides under nitrogen limitation conditions. Consequently, there have been only a few studies exploiting the lipid metabolism for higher product titers. RESULTS We performed a multi-omic investigation of the lipid accumulation phenotype under nitrogen limitation. Specifically, we performed comparative transcriptomic and lipidomic analysis of the oleaginous yeast under nitrogen-sufficient and nitrogen deficient conditions. Clustering analysis of transcriptomic data was used to identify the growth phase where nitrogen-deficient cultures diverged from the baseline conditions. Independently, lipidomic data was used to identify that lipid fractions shifted from mostly phospholipids to mostly storage lipids under the nitrogen-deficient phenotype. Through an integrative lens of transcriptomic and lipidomic analysis, we discovered that R. toruloides undergoes lipid remodeling during nitrogen limitation, wherein the pool of phospholipids gets remodeled to mostly storage lipids. We identify specific mRNAs and pathways that are strongly correlated with an increase in lipid levels, thus identifying putative targets for engineering greater lipid accumulation in R. toruloides. One surprising pathway identified was related to inositol phosphate metabolism, suggesting further inquiry into its role in lipid accumulation. CONCLUSIONS Integrative analysis identified the specific biosynthetic pathways that are differentially regulated during lipid remodeling. This insight into the mechanisms of lipid accumulation can lead to the success of future metabolic engineering strategies for overproduction of oleochemicals.
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Affiliation(s)
- Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, DOE Center for Advanced Bioenergy and Bioproducts Innovation, Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Anshu Deewan
- Department of Chemical and Biomolecular Engineering, DOE Center for Advanced Bioenergy and Bioproducts Innovation, Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, DOE Center for Advanced Bioenergy and Bioproducts Innovation, Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Christopher V Rao
- Department of Chemical and Biomolecular Engineering, DOE Center for Advanced Bioenergy and Bioproducts Innovation, Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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227
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Tian Z, Liu L, Wu L, Yang Z, Zhang Y, Du L, Zhang D. Enhancement of vitamin B 6 production driven by omics analysis combined with fermentation optimization. Microb Cell Fact 2024; 23:137. [PMID: 38750497 PMCID: PMC11095007 DOI: 10.1186/s12934-024-02405-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/24/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Microbial engineering aims to enhance the ability of bacteria to produce valuable products, including vitamin B6 for various applications. Numerous microorganisms naturally produce vitamin B6, yet the metabolic pathways involved are rigorously controlled. This regulation by the accumulation of vitamin B6 poses a challenge in constructing an efficient cell factory. RESULTS In this study, we conducted transcriptome and metabolome analyses to investigate the effects of the accumulation of pyridoxine, which is the major commercial form of vitamin B6, on cellular processes in Escherichia coli. Our omics analysis revealed associations between pyridoxine and amino acids, as well as the tricarboxylic acid (TCA) cycle. Based on these findings, we identified potential targets for fermentation optimization, including succinate, amino acids, and the carbon-to-nitrogen (C/N) ratio. Through targeted modifications, we achieved pyridoxine titers of approximately 514 mg/L in shake flasks and 1.95 g/L in fed-batch fermentation. CONCLUSION Our results provide insights into pyridoxine biosynthesis within the cellular metabolic network for the first time. Our comprehensive analysis revealed that the fermentation process resulted in a remarkable final yield of 1.95 g/L pyridoxine, the highest reported yield to date. This work lays a foundation for the green industrial production of vitamin B6 in the future.
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Affiliation(s)
- Zhizhong Tian
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Linxia Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Lijuan Wu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Zixuan Yang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Yahui Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Liping Du
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China.
| | - Dawei Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China.
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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228
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Taefehshokr N, Lac A, Vrieze AM, Dickson BH, Guo PN, Jung C, Blythe EN, Fink C, Aktar A, Dikeakos JD, Dekaban GA, Heit B. SARS-CoV-2 NSP5 antagonizes MHC II expression by subverting histone deacetylase 2. J Cell Sci 2024; 137:jcs262172. [PMID: 38682259 PMCID: PMC11166459 DOI: 10.1242/jcs.262172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024] Open
Abstract
SARS-CoV-2 interferes with antigen presentation by downregulating major histocompatibility complex (MHC) II on antigen-presenting cells, but the mechanism mediating this process is unelucidated. Herein, analysis of protein and gene expression in human antigen-presenting cells reveals that MHC II is downregulated by the SARS-CoV-2 main protease, NSP5. This suppression of MHC II expression occurs via decreased expression of the MHC II regulatory protein CIITA. CIITA downregulation is independent of the proteolytic activity of NSP5, and rather, NSP5 delivers HDAC2 to the transcription factor IRF3 at an IRF-binding site within the CIITA promoter. Here, HDAC2 deacetylates and inactivates the CIITA promoter. This loss of CIITA expression prevents further expression of MHC II, with this suppression alleviated by ectopic expression of CIITA or knockdown of HDAC2. These results identify a mechanism by which SARS-CoV-2 limits MHC II expression, thereby delaying or weakening the subsequent adaptive immune response.
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Affiliation(s)
- Nima Taefehshokr
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Alex Lac
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Angela M. Vrieze
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Brandon H. Dickson
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Peter N. Guo
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Catherine Jung
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Eoin N. Blythe
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
- Robarts Research Institute, London, Ontario, CanadaN6A 3K7
| | - Corby Fink
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
- Robarts Research Institute, London, Ontario, CanadaN6A 3K7
| | - Amena Aktar
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
| | - Jimmy D. Dikeakos
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
- Robarts Research Institute, London, Ontario, CanadaN6A 3K7
| | - Gregory A. Dekaban
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
- Robarts Research Institute, London, Ontario, CanadaN6A 3K7
| | - Bryan Heit
- Department of Microbiology and Immunology, and the Western Infection, Immunity and Inflammation Centre, The University of Western Ontario, London, Ontario, CanadaN6A 5C1
- Robarts Research Institute, London, Ontario, CanadaN6A 3K7
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Zinati Y, Takiddeen A, Emad A. GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks. Nat Commun 2024; 15:4055. [PMID: 38744843 DOI: 10.1038/s41467-024-48516-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 05/01/2024] [Indexed: 05/16/2024] Open
Abstract
We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.
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Affiliation(s)
- Yazdan Zinati
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
| | - Abdulrahman Takiddeen
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
- Mila, Quebec AI Institute, Montreal, QC, Canada.
- The Rosalind and Morris Goodman Cancer Institute, Montreal, QC, Canada.
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230
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Kochel B. Negative feedback systems for modelling NF-κB transcription factor oscillatory activity. Transcription 2024:1-32. [PMID: 38739365 DOI: 10.1080/21541264.2024.2331887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/13/2024] [Indexed: 05/14/2024] Open
Abstract
Low-dimensional negative feedback systems (NFSs) were developed within a signal flow model to describe the oscillatory activities of NF-κB caused by interactions with its inhibitor IκBα. The NFSs were established as 3rd- and 4th-order linear systems containing unperturbed and perturbed negative feedback (NF) loops with constant or time-varying NF strengths and a feed-forward loop. NF-related analytical solutions to the NFSs representing the time courses of NF-κB and IκBα were determined and their exact mathematical relationship was found. The NFS's parameters were determined to fit the experimental time courses of NF-κB in TNF-α-stimulated embryonic fibroblasts, rela-/- embryonic fibroblasts reconstituted with RelA, C9L cells, GFP-p65 knock-in embryonic fibroblasts and embryogenic fibroblasts lacking Iκβ and IκBε, LPS-stimulated IC-21 macrophages treated or not with DCPA, and anti-IgM-stimulated DT40 B-lymphocytes. The unperturbed and perturbed NFSs describing the above biosystems generated isochronous and non-isochronous solutions, depending on a constant or time-varying NF strength, respectively. The oscillation period of the NF-coupled solutions, the phase difference between them and the time delays in the appearance of cytoplasmic IκBα after stimulation of NF-κB were determined. A significant divergence between the IκBα solutions to the NFSs and the IκBα experimental courses led to a rejection of the NF coupling between NF-κB and IκBα in the above biosystems. It was shown that neither the linearity nor the low dimensionality of the NFSs altered the NF relationship and the divergence between the IκBα solutions to the NFS and IκBα experimental time courses. Although the NF relationship between IκBα and NF-κB was not confirmed in all the experimental data analyzed, delayed negative feedback was found in some cases.
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Affiliation(s)
- Bonawentura Kochel
- Immunotherapy Central Europe, Wroclaw Medical University, Wrocław, Poland
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231
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Evans NJ, Mills GB, Wu G, Song X, McWeeney S. Data Valuation with Gradient Similarity. ARXIV 2024:arXiv:2405.08217v1. [PMID: 38800649 PMCID: PMC11118599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.
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Affiliation(s)
- Nathaniel J Evans
- Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Gordon B Mills
- Division of Oncological Sciences Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Guanming Wu
- Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Xubo Song
- Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Shannon McWeeney
- Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
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Bumbaca B, Birtwistle MR, Gallo JM. Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593202. [PMID: 38766170 PMCID: PMC11100715 DOI: 10.1101/2024.05.08.593202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.
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Affiliation(s)
- Brandon Bumbaca
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson SC, USA
- Department of Bioengineering, Clemson University, Clemson SC, USA
| | - James M Gallo
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
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Chen-Li G, Martinez-Archer R, Coghi A, Roca JA, Rodriguez FJ, Acaba-Berrocal L, Berrocal MH, Wu L. Beyond VEGF: Angiopoietin-Tie Signaling Pathway in Diabetic Retinopathy. J Clin Med 2024; 13:2778. [PMID: 38792322 PMCID: PMC11122151 DOI: 10.3390/jcm13102778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/12/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024] Open
Abstract
Complications from diabetic retinopathy such as diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR) constitute leading causes of preventable vision loss in working-age patients. Since vascular endothelial growth factor (VEGF) plays a major role in the pathogenesis of these complications, VEGF inhibitors have been the cornerstone of their treatment. Anti-VEGF monotherapy is an effective but burdensome treatment for DME. However, due to the intensive and burdensome treatment, most patients in routine clinical practice are undertreated, and therefore, their outcomes are compromised. Even in adequately treated patients, persistent DME is reported anywhere from 30% to 60% depending on the drug used. PDR is currently treated by anti-VEGF, panretinal photocoagulation (PRP) or a combination of both. Similarly, a number of eyes, despite these treatments, continue to progress to tractional retinal detachment and vitreous hemorrhage. Clearly there are other molecular pathways other than VEGF involved in the pathogenesis of DME and PDR. One of these pathways is the angiopoietin-Tie signaling pathway. Angiopoietin 1 (Ang1) plays a major role in maintaining vascular quiescence and stability. It acts as a molecular brake against vascular destabilization and inflammation that is usually promoted by angiopoietin 2 (Ang2). Several pathological conditions including chronic hyperglycemia lead to Ang2 upregulation. Recent regulatory approval of the bi-specific antibody, faricimab, may improve long term outcomes in DME. It targets both the Ang/Tie and VEGF pathways. The YOSEMITE and RHINE were multicenter, double-masked, randomized non-inferiority phase 3 clinical trials that compared faricimab to aflibercept in eyes with center-involved DME. At 12 months of follow-up, faricimab demonstrated non-inferior vision gains, improved anatomic outcomes and a potential for extended dosing when compared to aflibercept. The 2-year results of the YOSEMITE and RHINE trials demonstrated that the anatomic and functional results obtained at the 1 year follow-up were maintained. Short term outcomes of previously treated and treatment-naive eyes with DME that were treated with faricimab during routine clinical practice suggest a beneficial effect of faricimab over other agents. Targeting of Ang2 has been reported by several other means including VE-PTP inhibitors, integrin binding peptide and surrobodies.
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Affiliation(s)
- Genesis Chen-Li
- Asociados de Mácula Vitreo y Retina de Costa Rica, San José 60612, Costa Rica (R.M.-A.); (A.C.)
| | - Rebeca Martinez-Archer
- Asociados de Mácula Vitreo y Retina de Costa Rica, San José 60612, Costa Rica (R.M.-A.); (A.C.)
| | - Andres Coghi
- Asociados de Mácula Vitreo y Retina de Costa Rica, San José 60612, Costa Rica (R.M.-A.); (A.C.)
| | | | | | - Luis Acaba-Berrocal
- Department of Ophthalmology, Illinois Eye and Ear Infirmary, School of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA
| | | | - Lihteh Wu
- Asociados de Mácula Vitreo y Retina de Costa Rica, San José 60612, Costa Rica (R.M.-A.); (A.C.)
- Department of Ophthalmology, Illinois Eye and Ear Infirmary, School of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA
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Scanu M, Toto F, Petito V, Masi L, Fidaleo M, Puca P, Baldelli V, Reddel S, Vernocchi P, Pani G, Putignani L, Scaldaferri F, Del Chierico F. An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients. Front Cell Infect Microbiol 2024; 14:1366192. [PMID: 38779566 PMCID: PMC11109417 DOI: 10.3389/fcimb.2024.1366192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Background Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches. Methods The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis. Results In the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions. Conclusion In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.
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Affiliation(s)
- Matteo Scanu
- Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Francesca Toto
- Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Valentina Petito
- Dipartimento di Scienze Mediche e Chirurgiche, Unità Operativa Semplice di Malattie Infiammatorie Croniche Intestinali, CEMAD, Unità Operativa Complessa di Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Letizia Masi
- Dipartimento di Scienze Mediche e Chirurgiche, Unità Operativa Semplice di Malattie Infiammatorie Croniche Intestinali, CEMAD, Unità Operativa Complessa di Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Marco Fidaleo
- Department of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy
- CNIS Research Center for Nanotechnology Applied to Engineering, Sapienza University of Rome, Rome, Italy
| | - Pierluigi Puca
- Dipartimento di Scienze Mediche e Chirurgiche, Unità Operativa Semplice di Malattie Infiammatorie Croniche Intestinali, CEMAD, Unità Operativa Complessa di Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Valerio Baldelli
- Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Sofia Reddel
- Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Pamela Vernocchi
- Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Giovambattista Pani
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lorenza Putignani
- Unit of Microbiology and Diagnostic Immunology, Unit of Microbiomics and Research Area of Immunology, Rheumatology and Infectious Diseases, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Franco Scaldaferri
- Dipartimento di Scienze Mediche e Chirurgiche, Unità Operativa Semplice di Malattie Infiammatorie Croniche Intestinali, CEMAD, Unità Operativa Complessa di Medicina Interna e Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Federica Del Chierico
- Immunology, Rheumatology and Infectious Diseases Research Area, Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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Kumar N, Mim MS, Dowling A, Zartman JJ. Reverse engineering morphogenesis through Bayesian optimization of physics-based models. NPJ Syst Biol Appl 2024; 10:49. [PMID: 38714708 PMCID: PMC11076624 DOI: 10.1038/s41540-024-00375-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 04/17/2024] [Indexed: 05/10/2024] Open
Abstract
Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.
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Affiliation(s)
- Nilay Kumar
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Mayesha Sahir Mim
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Alexander Dowling
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Jeremiah J Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN, 46556, USA.
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White AG, Elias E, Orozco A, Robinson SA, Manners MT. Chronic Stress-Induced Neuroinflammation: Relevance of Rodent Models to Human Disease. Int J Mol Sci 2024; 25:5085. [PMID: 38791125 PMCID: PMC11121038 DOI: 10.3390/ijms25105085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
The brain is the central organ of adaptation to stress because it perceives and determines threats that induce behavioral, physiological, and molecular responses. In humans, chronic stress manifests as an enduring consistent feeling of pressure and being overwhelmed for an extended duration. This can result in a persistent proinflammatory response in the peripheral and central nervous system (CNS), resulting in cellular, physiological, and behavioral effects. Compounding stressors may increase the risk of chronic-stress-induced inflammation, which can yield serious health consequences, including mental health disorders. This review summarizes the current knowledge surrounding the neuroinflammatory response in rodent models of chronic stress-a relationship that is continually being defined. Many studies investigating the effects of chronic stress on neuroinflammation in rodent models have identified significant changes in inflammatory modulators, including nuclear factor-κB (NF-κB) and toll-like receptors (TLRs), and cytokines, including tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1β, and IL-6. This suggests that these are key inflammatory factors in the chronic stress response, which may contribute to the establishment of anxiety and depression-like symptoms. The behavioral and neurological effects of modulating inflammatory factors through gene knockdown (KD) and knockout (KO), and conventional and alternative medicine approaches, are discussed.
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Affiliation(s)
- Abigail G. White
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
| | - Elias Elias
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
| | - Andrea Orozco
- Department of Psychology, Williams College, Williamstown, MA 01267, USA
| | | | - Melissa T. Manners
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
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Sunwoo Y, Park J, Choi CY, Shin S, Choi YJ. Risk of Dementia and Alzheimer's Disease Associated With Antidiabetics: A Bayesian Network Meta-Analysis. Am J Prev Med 2024:S0749-3797(24)00140-5. [PMID: 38705542 DOI: 10.1016/j.amepre.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/07/2024]
Abstract
INTRODUCTION Dementia risk is substantially elevated in patients with diabetes. However, evidence on dementia risk associated with various antidiabetic regimens is still limited. This study aims to comprehensively investigate the risk of dementia and Alzheimer's disease (AD) associated with various antidiabetic classes. METHODS Cochrane Central Register of Controlled Trials, Embase, MEDLINE (PubMed), and Scopus were searched from inception to March 2024 (PROSPERO CRD 42022365927). Observational studies investigating dementia and AD incidences after antidiabetic initiation were identified. Bayesian network meta-analysis was performed to determine dementia and AD risks associated with antidiabetics. Preferred Reporting Items for Systematic Reviews-Network Meta-Analyses (PRISMA-NMA) guidelines were followed. Statistical analysis was performed and updated in November 2023 and March 2024, respectively. RESULTS A total of 1,565,245 patients from 16 studies were included. Dementia and AD risks were significantly lower with metformin and sodium glucose co-transporter-2 inhibitors (SGLT2i). Metformin displayed the lowest risk of dementia across diverse antidiabetics, whereas α-glucosidase inhibitors demonstrated the highest risk. SGLT2i exhibited the lowest dementia risk across second-line antidiabetics. Dementia risk was significantly higher with dipeptidyl peptidase-4 inhibitor (DPP4i), metformin, sulfonylureas, and thiazolidinediones (TZD) compared to SGLT2i in the elderly (≥75 years). Dementia risk associated with metformin was substantially lower, regardless of diabetic complication status or baseline A1C. DISCUSSION Metformin and SGLT2i demonstrated lower dementia risk than other antidiabetic classes. Patient-specific factors may affect this relationship and cautious interpretation is warranted as metformin is typically initiated at an earlier stage with fewer complications. Hence, further large-scaled clinical trials are required.
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Affiliation(s)
- Yongjun Sunwoo
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul, Korea; Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul, Korea; Institute of Regulatory Innovation Through Science (IRIS), Kyung Hee University, Seoul, Korea
| | - Jaeho Park
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul, Korea
| | - Chang-Young Choi
- Department of Internal Medicine, Ajou University Medical Center, Suwon, Korea
| | - Sooyoung Shin
- Department of Pharmacy, College of Pharmacy, Ajou University, Suwon, Korea; Research Institute of Pharmaceutical Science and Technology (RIPST), Ajou University, Suwon, Korea
| | - Yeo Jin Choi
- Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul, Korea; Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul, Korea; Institute of Regulatory Innovation Through Science (IRIS), Kyung Hee University, Seoul, Korea.
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Vaparanta K, Merilahti JAM, Ojala VK, Elenius K. De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways. Mol Cell Proteomics 2024; 23:100780. [PMID: 38703893 PMCID: PMC11259815 DOI: 10.1016/j.mcpro.2024.100780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 04/07/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024] Open
Abstract
New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.
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Affiliation(s)
- Katri Vaparanta
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; Medicity Research Laboratories, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, Turku, Finland.
| | - Johannes A M Merilahti
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; Medicity Research Laboratories, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, Turku, Finland
| | - Veera K Ojala
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; Medicity Research Laboratories, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, Turku, Finland
| | - Klaus Elenius
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; Medicity Research Laboratories, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, Turku, Finland; Department of Oncology, Turku University Hospital, Turku, Finland.
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240
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Kubat Oktem E. Biomarkers of Alzheimer's Disease Associated with Programmed Cell Death Reveal Four Repurposed Drugs. J Mol Neurosci 2024; 74:51. [PMID: 38700745 DOI: 10.1007/s12031-024-02228-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/21/2024] [Indexed: 07/20/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia. Programmed cell death (PCD) is mainly characterized by unique morphological features and energy-dependent biochemical processes. The predominant pathway leading to cell death in AD has not been thoroughly analyzed, although there is evidence of neuron loss in AD and numerous pathways of PCD have been associated with this process. A better understanding of the systems biology underlying the relationship between AD and PCD could lead to the development of new therapeutic approaches. To this end, publicly available transcriptome data were examined using bioinformatic methods such as differential gene expression and weighted gene coexpression network analysis (WGCNA) to find PCD-related AD biomarkers. The diagnostic significance of these biomarkers was evaluated using a logistic regression-based predictive model. Using these biomarkers, a multifactorial regulatory network was developed. Last, a drug repositioning study was conducted to propose new drugs for the treatment of AD targeting PCD. The development of 3PM (predictive, preventive, and personalized) drugs for the treatment of AD would be enabled by additional research on the effects of these drugs on this disease.
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Affiliation(s)
- Elif Kubat Oktem
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Istanbul Medeniyet University, North Campus, Istanbul, 34700, Turkey.
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241
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Giansanti V, Giannese F, Botrugno OA, Gandolfi G, Balestrieri C, Antoniotti M, Tonon G, Cittaro D. Scalable integration of multiomic single-cell data using generative adversarial networks. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae300. [PMID: 38696763 DOI: 10.1093/bioinformatics/btae300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/22/2024] [Accepted: 04/30/2024] [Indexed: 05/04/2024]
Abstract
MOTIVATION Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable. RESULTS We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. AVAILABILITY AND IMPLEMENTATION Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.
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Affiliation(s)
- Valentina Giansanti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, 20125, Italy
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Francesca Giannese
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Oronza A Botrugno
- Functional Genomics of Cancer Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Università Vita-Salute San Raffaele, Milan, 20132, Italy
| | - Giorgia Gandolfi
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Chiara Balestrieri
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Experimental Hematology Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, 20125, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Università degli Studi di Milano-Bicocca, Milan, 20125, Italy
- Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche (CNR), Milan, 20090, Italy
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Functional Genomics of Cancer Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Università Vita-Salute San Raffaele, Milan, 20132, Italy
| | - Davide Cittaro
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
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242
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Liu Y, Yu H, Duan X, Zhang X, Cheng T, Jiang F, Tang H, Ruan Y, Zhang M, Zhang H, Zhang Q. TransGEM: a molecule generation model based on Transformer with gene expression data. Bioinformatics 2024; 40:btae189. [PMID: 38632084 PMCID: PMC11078772 DOI: 10.1093/bioinformatics/btae189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/26/2024] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
MOTIVATION It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/hzauzqy/TransGEM.
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Affiliation(s)
- Yanguang Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hailong Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xinya Duan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xiaomin Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Ting Cheng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Feng Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hao Tang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Yao Ruan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Miao Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hongyu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Qingye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
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243
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Lyons MA, Obregon-Henao A, Ramey ME, Bauman AA, Pauly S, Rossmassler K, Reid J, Karger B, Walter ND, Robertson GT. Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis. Antimicrob Agents Chemother 2024; 68:e0101023. [PMID: 38501805 PMCID: PMC11064538 DOI: 10.1128/aac.01010-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen [bedaquiline-pretomanid-linezolid (BPaL)] during the first 3 weeks of treatment at human equivalent doses. The rRNA synthesis (RS) ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, together with solid culture CFU counts and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time-course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.
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Affiliation(s)
- M. A. Lyons
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - A. Obregon-Henao
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - M. E. Ramey
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - A. A. Bauman
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - S. Pauly
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - K. Rossmassler
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - J. Reid
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - B. Karger
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
| | - N. D. Walter
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Consortium for Applied Microbial Metrics, Aurora, Colorado, USA
- Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| | - G. T. Robertson
- Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, Colorado, USA
- Consortium for Applied Microbial Metrics, Aurora, Colorado, USA
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244
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Stock M, Popp N, Fiorentino J, Scialdone A. Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data. Bioinformatics 2024; 40:btae267. [PMID: 38627250 PMCID: PMC11096270 DOI: 10.1093/bioinformatics/btae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/28/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. RESULTS To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. AVAILABILITY AND IMPLEMENTATION STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.
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Affiliation(s)
- Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 85354, Germany
| | - Niclas Popp
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Jonathan Fiorentino
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
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245
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Blasco T, Balzerani F, Valcárcel LV, Larrañaga P, Bielza C, Francino MP, Rufián-Henares JÁ, Planes FJ, Pérez-Burillo S. BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities. Bioinformatics 2024; 40:btae266. [PMID: 38688585 PMCID: PMC11082422 DOI: 10.1093/bioinformatics/btae266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. RESULTS Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. AVAILABILITY AND IMPLEMENTATION MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.
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Affiliation(s)
- Telmo Blasco
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Francesco Balzerani
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Pedro Larrañaga
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - Concha Bielza
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - María Pilar Francino
- Area de Genómica y Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia 46020, Spain
- CIBER en Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Centro de Investigación Biomédica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, Granada 18016, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Universidad de Granada, Granada 18012, Spain
| | - Francisco J Planes
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Sergio Pérez-Burillo
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
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246
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Rahimikollu J, Xiao H, Rosengart A, Rosen ABI, Tabib T, Zdinak PM, He K, Bing X, Bunea F, Wegkamp M, Poholek AC, Joglekar AV, Lafyatis RA, Das J. SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains. Nat Methods 2024; 21:835-845. [PMID: 38374265 DOI: 10.1038/s41592-024-02175-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/09/2024] [Indexed: 02/21/2024]
Abstract
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.
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Affiliation(s)
- Javad Rahimikollu
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - Hanxi Xiao
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - AnnaElaine Rosengart
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aaron B I Rosen
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - Tracy Tabib
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Paul M Zdinak
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kun He
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xin Bing
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Florentina Bunea
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
| | - Marten Wegkamp
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
- Department of Mathematics, Cornell University, Ithaca, NY, USA
| | - Amanda C Poholek
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Alok V Joglekar
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Robert A Lafyatis
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Jishnu Das
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
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247
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Gao W, Liu J, Shtylla B, Venkatakrishnan K, Yin D, Shah M, Nicholas T, Cao Y. Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics Syst Pharmacol 2024; 13:691-709. [PMID: 37969061 PMCID: PMC11098159 DOI: 10.1002/psp4.13079] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/17/2023] Open
Abstract
Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.
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Affiliation(s)
- Wei Gao
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Jiang Liu
- Food and Drug AdministrationSilver SpringMarylandUSA
| | - Blerta Shtylla
- Quantitative Systems PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Karthik Venkatakrishnan
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Donghua Yin
- Clinical PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Mirat Shah
- Food and Drug AdministrationSilver SpringMarylandUSA
| | | | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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248
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Jiang W, Wang H, Dong X, Yu X, Zhao Y, Chen D, Yan B, Cheng J, Zhuo S, Wang H, Yan J. Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer. JAMA Surg 2024; 159:519-528. [PMID: 38416471 PMCID: PMC10902777 DOI: 10.1001/jamasurg.2023.8015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/12/2023] [Indexed: 02/29/2024]
Abstract
Importance The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer. Objective To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides. Design, Setting, and Participants This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023. Main Outcomes and Measures The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated. Results This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001). Conclusions and Relevance These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.
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Affiliation(s)
- Wei Jiang
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- School of Science, Jimei University, Xiamen, China
| | - Huaiming Wang
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Department of Colorectal Surgery & Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Supported by National Key Clinical Discipline, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyu Dong
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xian Yu
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China
| | - Yandong Zhao
- Department of Pathology, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Dexin Chen
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Botao Yan
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Jiaxin Cheng
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | | | - Hui Wang
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Department of Colorectal Surgery & Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Supported by National Key Clinical Discipline, Sun Yat-sen University, Guangzhou, China
| | - Jun Yan
- Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Department of General Surgery, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Gastrointestinal Surgery, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
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249
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Osonoi S, Takebe T. Organoid-guided precision hepatology for metabolic liver disease. J Hepatol 2024; 80:805-821. [PMID: 38237864 DOI: 10.1016/j.jhep.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/28/2023] [Accepted: 01/02/2024] [Indexed: 03/09/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease affects millions of people worldwide. Progress towards a definitive cure has been incremental and treatment is currently limited to lifestyle modification. Hepatocyte-specific lipid accumulation is the main trigger of lipotoxic events, driving inflammation and fibrosis. The underlying pathology is extraordinarily heterogenous, and the manifestations of steatohepatitis are markedly influenced by metabolic communications across non-hepatic organs. Synthetic human tissue models have emerged as powerful platforms to better capture the mechanistic diversity in disease progression, while preserving person-specific genetic traits. In this review, we will outline current research efforts focused on integrating multiple synthetic tissue models of key metabolic organs, with an emphasis on organoid-based systems. By combining functional genomics and population-scale en masse profiling methodologies, human tissues derived from patients can provide insights into personalised genetic, transcriptional, biochemical, and metabolic states. These collective efforts will advance our understanding of steatohepatitis and guide the development of rational solutions for mechanism-directed diagnostic and therapeutic investigation.
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Affiliation(s)
- Sho Osonoi
- Center for Stem Cell and Organoid Medicine (CuSTOM), Division of Gastroenterology, Hepatology and Nutrition, Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki, 036-8562, Japan
| | - Takanori Takebe
- Center for Stem Cell and Organoid Medicine (CuSTOM), Division of Gastroenterology, Hepatology and Nutrition, Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; WPI Premium Institute for Human Metaverse Medicine (WPI-PRIMe) and Department of Genome Biology, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan; Institute of Research, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan; Communication Design Center, Advanced Medical Research Center, Yokohama City University, Yokohama 236-0004, Japan.
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250
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Naik HM, Cai X, Ladiwala P, Reddy JV, Betenbaugh MJ, Antoniewicz MR. Elucidating uptake and metabolic fate of dipeptides in CHO cell cultures using 13C labeling experiments and kinetic modeling. Metab Eng 2024; 83:12-23. [PMID: 38460784 DOI: 10.1016/j.ymben.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/05/2024] [Accepted: 03/06/2024] [Indexed: 03/11/2024]
Abstract
The rapidly growing market of biologics including monoclonal antibodies has stimulated the need to improve biomanufacturing processes including mammalian host systems such as Chinese Hamster Ovary (CHO) cells. Cell culture media formulations continue to be enhanced to enable intensified cell culture processes and optimize cell culture performance. Amino acids, major components of cell culture media, are consumed in large amounts by CHO cells. Due to their low solubility and poor stability, certain amino acids including tyrosine, leucine, and phenylalanine can pose major challenges leading to suboptimal bioprocess performance. Dipeptides have the potential to replace amino acids in culture media. However, very little is known about the cleavage, uptake, and utilization kinetics of dipeptides in CHO cell cultures. In this study, replacing amino acids, including leucine and tyrosine by their respective dipeptides including but not limited to Ala-Leu and Gly-Tyr, supported similar cell growth, antibody production, and lactate profiles. Using 13C labeling techniques and spent media studies, dipeptides were shown to undergo both intracellular and extracellular cleavage in cultures. Extracellular cleavage increased with the culture duration, indicating cleavage by host cell proteins that are likely secreted and accumulate in cell culture over time. A kinetic model was built and for the first time, integrated with 13C labeling experiments to estimate dipeptide utilization rates, in CHO cell cultures. Dipeptides with alanine at the N-terminus had a higher utilization rate than dipeptides with alanine at the C-terminus and dipeptides with glycine instead of alanine at N-terminus. Simultaneous supplementation of more than one dipeptide in culture led to reduction in individual dipeptide utilization rates indicating that dipeptides compete for the same cleavage enzymes, transporters, or both. Dipeptide utilization rates in culture and cleavage rates in cell-free experiments appeared to follow Michaelis-Menten kinetics, reaching a maximum at higher dipeptide concentrations. Dipeptide utilization behavior was found to be similar in cell-free and cell culture environments, paving the way for future testing approaches for dipeptides in cell-free environments prior to use in large-scale bioreactors. Thus, this study provides a deeper understanding of the fate of dipeptides in CHO cell cultures through an integration of cell culture, 13C labeling, and kinetic modeling approaches providing insights in how to best use dipeptides in media formulations for robust and optimal mammalian cell culture performance.
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Affiliation(s)
- Harnish Mukesh Naik
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Xiangchen Cai
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Pranay Ladiwala
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Maciek R Antoniewicz
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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