251
|
Crouzet A, Lopez N, Riss Yaw B, Lepelletier Y, Demange L. The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang? Molecules 2024; 29:2716. [PMID: 38930784 PMCID: PMC11206022 DOI: 10.3390/molecules29122716] [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: 03/29/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.
Collapse
Affiliation(s)
- Aurore Crouzet
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
| | - Nicolas Lopez
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- ENOES, 62 Rue de Miromesnil, 75008 Paris, France
- Unité Mixte de Recherche «Institut de Physique Théorique (IPhT)» CEA-CNRS, UMR 3681, Bat 774, Route de l’Orme des Merisiers, 91191 St Aubin-Gif-sur-Yvette, France
| | - Benjamin Riss Yaw
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| | - Yves Lepelletier
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- Université Paris Cité, Imagine Institute, 24 Boulevard Montparnasse, 75015 Paris, France
- INSERM UMR 1163, Laboratory of Cellular and Molecular Basis of Normal Hematopoiesis and Hematological Disorders: Therapeutical Implications, 24 Boulevard Montparnasse, 75015 Paris, France
| | - Luc Demange
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| |
Collapse
|
252
|
Maksiutenko EM, Barbitoff YA, Danilov LG, Matveenko AG, Zemlyanko OM, Efremova EP, Moskalenko SE, Zhouravleva GA. Gene Expression Analysis of Yeast Strains with a Nonsense Mutation in the eRF3-Coding Gene Highlights Possible Mechanisms of Adaptation. Int J Mol Sci 2024; 25:6308. [PMID: 38928012 PMCID: PMC11203930 DOI: 10.3390/ijms25126308] [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: 05/18/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
In yeast Saccharomyces cerevisiae, there are two translation termination factors, eRF1 (Sup45) and eRF3 (Sup35), which are essential for viability. Previous studies have revealed that presence of nonsense mutations in these genes leads to amplification of mutant alleles (sup35-n and sup45-n), which appears to be necessary for the viability of such cells. However, the mechanism of this phenomenon remained unclear. In this study, we used RNA-Seq and proteome analysis to reveal the complete set of gene expression changes that occur during cellular adaptation to the introduction of the sup35-218 nonsense allele. Our analysis demonstrated significant changes in the transcription of genes that control the cell cycle: decreases in the expression of genes of the anaphase promoting complex APC/C (APC9, CDC23) and their activator CDC20, and increases in the expression of the transcription factor FKH1, the main cell cycle kinase CDC28, and cyclins that induce DNA biosynthesis. We propose a model according to which yeast adaptation to nonsense mutations in the translation termination factor genes occurs as a result of a delayed cell cycle progression beyond the G2-M stage, which leads to an extension of the S and G2 phases and an increase in the number of copies of the mutant sup35-n allele.
Collapse
Affiliation(s)
- Evgeniia M. Maksiutenko
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
- St. Petersburg Branch, Vavilov Institute of General Genetics of the Russian Academy of Sciences, 199034 St. Petersburg, Russia
| | - Yury A. Barbitoff
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
- Bioinformatics Institute, 197342 St. Petersburg, Russia
| | - Lavrentii G. Danilov
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
| | - Andrew G. Matveenko
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
| | - Olga M. Zemlyanko
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
- Laboratory of Amyloid Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Elena P. Efremova
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
| | - Svetlana E. Moskalenko
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
- St. Petersburg Branch, Vavilov Institute of General Genetics of the Russian Academy of Sciences, 199034 St. Petersburg, Russia
| | - Galina A. Zhouravleva
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.B.); (L.G.D.); (A.G.M.); (O.M.Z.); (E.P.E.); (S.E.M.)
- Laboratory of Amyloid Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
| |
Collapse
|
253
|
Lu Y, Elrod J, Herrmann M, Knopf J, Boettcher M. Neutrophil Extracellular Traps: A Crucial Factor in Post-Surgical Abdominal Adhesion Formation. Cells 2024; 13:991. [PMID: 38891123 PMCID: PMC11171752 DOI: 10.3390/cells13110991] [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: 04/07/2024] [Revised: 05/27/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
Post-surgical abdominal adhesions, although poorly understood, are highly prevalent. The molecular processes underlying their formation remain elusive. This review aims to assess the relationship between neutrophil extracellular traps (NETs) and the generation of postoperative peritoneal adhesions and to discuss methods for mitigating peritoneal adhesions. A keyword or medical subject heading (MeSH) search for all original articles and reviews was performed in PubMed and Google Scholar. It included studies assessing peritoneal adhesion reformation after abdominal surgery from 2003 to 2023. After assessing for eligibility, the selected articles were evaluated using the Critical Appraisal Skills Programme checklist for qualitative research. The search yielded 127 full-text articles for assessment of eligibility, of which 7 studies met our criteria and were subjected to a detailed quality review using the Critical Appraisal Skills Programme (CASP) checklist. The selected studies offer a comprehensive analysis of adhesion pathogenesis with a special focus on the role of neutrophil extracellular traps (NETs) in the development of peritoneal adhesions. Current interventional strategies are examined, including the use of mechanical barriers, advances in regenerative medicine, and targeted molecular therapies. In particular, this review emphasizes the potential of NET-targeted interventions as promising strategies to mitigate postoperative adhesion development. Evidence suggests that in addition to their role in innate defense against infections and autoimmune diseases, NETs also play a crucial role in the formation of peritoneal adhesions after surgery. Therefore, therapeutic strategies that target NETs are emerging as significant considerations for researchers. Continued research is vital to fully elucidate the relationship between NETs and post-surgical adhesion formation to develop effective treatments.
Collapse
Affiliation(s)
- Yuqing Lu
- Department of Pediatric Surgery, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Julia Elrod
- Department of Pediatric Surgery, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Martin Herrmann
- Department of Pediatric Surgery, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich Alexander University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich Alexander University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, 91054 Erlangen, Germany
| | - Jasmin Knopf
- Department of Pediatric Surgery, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Michael Boettcher
- Department of Pediatric Surgery, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| |
Collapse
|
254
|
Yeo WJ, Surapaneni AL, Hasson DC, Schmidt IM, Sekula P, Köttgen A, Eckardt KU, Rebholz CM, Yu B, Waikar SS, Rhee EP, Schrauben SJ, Feldman HI, Vasan RS, Kimmel PL, Coresh J, Grams ME, Schlosser P. Serum and Urine Metabolites and Kidney Function. J Am Soc Nephrol 2024:00001751-990000000-00343. [PMID: 38844075 DOI: 10.1681/asn.0000000000000403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
Key Points
We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort.Metabolic profiling in serum and urine provides distinct and complementary insights into disease.
Background
Metabolites represent a read-out of cellular processes underlying states of health and disease.
Methods
We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.
Results
At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m2 (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5).
Conclusions
Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.
Collapse
Affiliation(s)
- Wan-Jin Yeo
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
| | - Aditya L Surapaneni
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
| | - Denise C Hasson
- Division of Pediatric Critical Care Medicine, Hassenfeld Children's Hospital, NYU Langone Health, New York, New York
| | - Insa M Schmidt
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Peggy Sekula
- Department of Data Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Department of Data Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas
| | - Sushrut S Waikar
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Sarah J Schrauben
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Harold I Feldman
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ramachandran S Vasan
- School of Public Health, University of Texas Health San Antonio, San Antonio, Texas
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Optimal Aging Institute, Departments of Population Health and Medicine, NYU Langone Health, New York, New York
- Department of Population Health, NYU Langone Medical Center, New York, New York
| | - Morgan E Grams
- Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York
- Department of Population Health, NYU Langone Medical Center, New York, New York
| | - Pascal Schlosser
- Department of Data Driven Medicine, Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| |
Collapse
|
255
|
Wang S, Yin N, Li Y, Ma Z, Lin W, Zhang L, Cui Y, Xia J, Geng L. Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis Diffusa: an integrative study with real-world clinical data and experimental validation. Front Pharmacol 2024; 15:1355531. [PMID: 38903989 PMCID: PMC11187350 DOI: 10.3389/fphar.2024.1355531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/15/2024] [Indexed: 06/22/2024] Open
Abstract
Background With a variety of active ingredients, Hedyotis Diffusa (H. diffusa) can treat a variety of tumors. The purpose of our study is based on real-world data and experimental level, to double demonstrate the efficacy and possible molecular mechanism of H. diffusa in the treatment of lung adenocarcinom (LUAD). Methods Phenotype-genotype and herbal-target associations were extracted from the SymMap database. Disease-gene associations were extracted from the MalaCards database. A molecular network-based correlation analysis was further conducted on the collection of genes associated with TCM and the collection of genes associated with diseases and symptoms. Then, the network separation SAB metrics were applied to evaluate the network proximity relationship between TCM and symptoms. Finally, cell apoptosis experiment, Western blot, and Real-time PCR were used for biological experimental level validation analysis. Results Included in the study were 85,437 electronic medical records (318 patients with LUAD). The proportion of prescriptions containing H. diffusa in the LUAD group was much higher than that in the non-LUAD group (p < 0.005). We counted the symptom relief of patients in the group and the group without the use of H. diffusa: except for symptoms such as fatigue, palpitations, and dizziness, the improvement rate of symptoms in the user group was higher than that in the non-use group. We selected the five most frequently occurring symptoms in the use group, namely, cough, expectoration, fatigue, chest tightness and wheezing. We combined the above five symptom genes into one group. The overlapping genes obtained were CTNNB1, STAT3, CASP8, and APC. The selection of CTNNB1 target for biological experiments showed that the proliferation rate of LUAD A549 cells in the drug intervention group was significantly lower than that in the control group, and it was concentration-dependent. H. diffusa can promote the apoptosis of A549 cells, and the apoptosis rate of the high-concentration drug group is significantly higher than that of the low-concentration drug group. The transcription and expression level of CTNNB1 gene in the drug intervention group were significantly decreased. Conclusion H. diffusa inhibits the proliferation and promotes apoptosis of LUAD A549 cells, which may be related to the fact that H. diffusa can regulate the expression of CTNNB1.
Collapse
Affiliation(s)
- Sheng Wang
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Na Yin
- School of Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yingyue Li
- Medical Engineering Technology and Data Mining Institute, Zhengzhou University, Zhengzhou, China
| | - Zhaohang Ma
- School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
| | - Wei Lin
- School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
| | - Lihong Zhang
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yun Cui
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jianan Xia
- School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
| | - Liang Geng
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| |
Collapse
|
256
|
Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.
Collapse
Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| |
Collapse
|
257
|
Young R, Ahmed KA, Court L, Castro-Vargas C, Marcora A, Boctor J, Paull C, Wijffels G, Rane R, Edwards O, Walsh T, Pandey G. Improved reference quality genome sequence of the plastic-degrading greater wax moth, Galleria mellonella. G3 (BETHESDA, MD.) 2024; 14:jkae070. [PMID: 38564250 DOI: 10.1093/g3journal/jkae070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 12/19/2023] [Accepted: 03/22/2024] [Indexed: 04/04/2024]
Abstract
Galleria mellonella is a pest of honeybees in many countries because its larvae feed on beeswax. However, G. mellonella larvae can also eat various plastics, including polyethylene, polystyrene, and polypropylene, and therefore, the species is garnering increasing interest as a tool for plastic biodegradation research. This paper presents an improved genome (99.3% completed lepidoptera_odb10 BUSCO; genome mode) for G. mellonella. This 472 Mb genome is in 221 contigs with an N50 of 6.4 Mb and contains 13,604 protein-coding genes. Genes that code for known and putative polyethylene-degrading enzymes and their similarity to proteins found in other Lepidoptera are highlighted. An analysis of secretory proteins more likely to be involved in the plastic catabolic process has also been carried out.
Collapse
Affiliation(s)
| | | | - Leon Court
- CSIRO Environment, Acton, ACT 2601, Australia
| | | | - Anna Marcora
- CSIRO Agriculture and Food, Dutton Park, QLD 4102, Australia
| | - Joseph Boctor
- Bioplastics Innovation Hub, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
| | - Cate Paull
- CSIRO Agriculture and Food, Dutton Park, QLD 4102, Australia
| | - Gene Wijffels
- CSIRO Agriculture and Food, St Lucia, QLD 4067, Australia
| | - Rahul Rane
- CSIRO Health and Biosecurity, Parkville, VIC 3052, Australia
| | | | - Tom Walsh
- CSIRO Environment, Acton, ACT 2601, Australia
| | | |
Collapse
|
258
|
Chen J, Yang L, Ma Y, Zhang Y. Recent advances in understanding the immune microenvironment in ovarian cancer. Front Immunol 2024; 15:1412328. [PMID: 38903506 PMCID: PMC11188340 DOI: 10.3389/fimmu.2024.1412328] [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: 04/04/2024] [Accepted: 05/22/2024] [Indexed: 06/22/2024] Open
Abstract
The occurrence of ovarian cancer (OC) is a major factor in women's mortality rates. Despite progress in medical treatments, like new drugs targeting homologous recombination deficiency, survival rates for OC patients are still not ideal. The tumor microenvironment (TME) includes cancer cells, fibroblasts linked to cancer (CAFs), immune-inflammatory cells, and the substances these cells secrete, along with non-cellular components in the extracellular matrix (ECM). First, the TME mainly plays a role in inhibiting tumor growth and protecting normal cell survival. As tumors progress, the TME gradually becomes a place to promote tumor cell progression. Immune cells in the TME have attracted much attention as targets for immunotherapy. Immune checkpoint inhibitor (ICI) therapy has the potential to regulate the TME, suppressing factors that facilitate tumor advancement, reactivating immune cells, managing tumor growth, and extending the survival of patients with advanced cancer. This review presents an outline of current studies on the distinct cellular elements within the OC TME, detailing their main functions and possible signaling pathways. Additionally, we examine immunotherapy rechallenge in OC, with a specific emphasis on the biological reasons behind resistance to ICIs.
Collapse
Affiliation(s)
- Jinxin Chen
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Lu Yang
- Department of Internal Medicine, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Yiming Ma
- Department of Medical Oncology, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China
- Liaoning Key Laboratory of Gastrointestinal Cancer Translational Research, Shenyang, Liaoning, China
| | - Ye Zhang
- Department of Radiation Oncology, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| |
Collapse
|
259
|
Ramisetty S, Subbalakshmi AR, Pareek S, Mirzapoiazova T, Do D, Prabhakar D, Pisick E, Shrestha S, Achuthan S, Bhattacharya S, Malhotra J, Mohanty A, Singhal SS, Salgia R, Kulkarni P. Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies. J Clin Med 2024; 13:3337. [PMID: 38893049 PMCID: PMC11172618 DOI: 10.3390/jcm13113337] [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: 04/22/2024] [Revised: 05/30/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024] Open
Abstract
Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.
Collapse
Affiliation(s)
- Sravani Ramisetty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Ayalur Raghu Subbalakshmi
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Siddhika Pareek
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Tamara Mirzapoiazova
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Dana Do
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Dhivya Prabhakar
- City of Hope Atlanta, 600 Celebrate Life Parkway, Newnan, GA 30265, USA;
| | - Evan Pisick
- City of Hope Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA;
| | - Sagun Shrestha
- City of Hope Phoenix, 14200 West Celebrate Life Way, Goodyear, AZ 85338, USA;
| | - Srisairam Achuthan
- Center for Informatics, City of Hope National Medical Center, Duarte, CA 91010, USA;
| | - Supriyo Bhattacharya
- Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA 91010, USA;
| | - Jyoti Malhotra
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Sharad S. Singhal
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
| | - Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA; (S.R.); (A.R.S.); (S.P.); (T.M.); (D.D.); (J.M.); (A.M.); (S.S.S.)
- Department of Systems Biology, City of Hope National Medical Center, Duarte, CA 91010, USA
| |
Collapse
|
260
|
Budak B, Tükel EY, Turanlı B, Kiraz Y. Integrated systems biology analysis of acute lymphoblastic leukemia: unveiling molecular signatures and drug repurposing opportunities. Ann Hematol 2024:10.1007/s00277-024-05821-w. [PMID: 38836918 DOI: 10.1007/s00277-024-05821-w] [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: 04/04/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024]
Abstract
Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by aberrant proliferation and accumulation of lymphoid precursor cells within the bone marrow. The tyrosine kinase inhibitor (TKI), imatinib mesylate, has played a significant role in the treatment of Philadelphia chromosome-positive ALL (Ph + ALL). However, the achievement of durable and sustained therapeutic success remains a challenge due to the development of TKI resistance during the clinical course.The primary objective of this investigation is to propose a novel and efficacious treatment approach through drug repositioning, targeting ALL and its Ph + subtype by identifying and addressing differentially expressed genes (DEGs). This study involves a comprehensive analysis of transcriptome datasets pertaining to ALL and Ph + ALL in order to identify DEGs associated with the progression of these diseases to identify possible repurposable drugs that target identified hub proteins.The outcomes of this research have unveiled 698 disease-related DEGs for ALL and 100 for Ph + ALL. Furthermore, a subset of drugs, specifically glipizide for Ph + ALL, and maytansine and isoprenaline for ALL, have been identified as potential candidates for therapeutic intervention. Subsequently, cytotoxicity assessments were performed to confirm the in vitro cytotoxic effects of these selected drugs on both ALL and Ph + ALL cell lines.In conclusion, this study offers a promising avenue for the management of ALL and Ph + ALL through drug repurposed drugs. Further investigations are necessary to elucidate the mechanisms underlying cell death, and clinical trials are recommended to validate the promising results obtained through drug repositioning strategies.
Collapse
Affiliation(s)
- Betül Budak
- Department of Bioengineering, Marmara University, Istanbul, Türkiye
- Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Türkiye
| | - Ezgi Yağmur Tükel
- Department of Genetics and Bioengineering, Faculty of Engineering, Izmir University of Economics, Balçova, Izmir, Türkiye
| | - Beste Turanlı
- Department of Bioengineering, Marmara University, Istanbul, Türkiye
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Türkiye
| | - Yağmur Kiraz
- Department of Genetics and Bioengineering, Faculty of Engineering, Izmir University of Economics, Balçova, Izmir, Türkiye.
| |
Collapse
|
261
|
Otobe Y, Jeong EM, Ito S, Shinohara Y, Kurabayashi N, Aiba A, Fukada Y, Kim JK, Yoshitane H. Phosphorylation of DNA-binding domains of CLOCK-BMAL1 complex for PER-dependent inhibition in circadian clock of mammalian cells. Proc Natl Acad Sci U S A 2024; 121:e2316858121. [PMID: 38805270 PMCID: PMC11161756 DOI: 10.1073/pnas.2316858121] [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: 11/01/2023] [Accepted: 05/03/2024] [Indexed: 05/30/2024] Open
Abstract
In mammals, CLOCK and BMAL1 proteins form a heterodimer that binds to E-box sequences and activates transcription of target genes, including Period (Per). Translated PER proteins then bind to the CLOCK-BMAL1 complex to inhibit its transcriptional activity. However, the molecular mechanism and the impact of this PER-dependent inhibition on the circadian clock oscillation remain elusive. We previously identified Ser38 and Ser42 in a DNA-binding domain of CLOCK as phosphorylation sites at the PER-dependent inhibition phase. In this study, knockout rescue experiments showed that nonphosphorylatable (Ala) mutations at these sites shortened circadian period, whereas their constitutive-phospho-mimetic (Asp) mutations completely abolished the circadian rhythms. Similarly, we found that nonphosphorylatable (Ala) and constitutive-phospho-mimetic (Glu) mutations at Ser78 in a DNA-binding domain of BMAL1 also shortened the circadian period and abolished the rhythms, respectively. The mathematical modeling predicted that these constitutive-phospho-mimetic mutations weaken the DNA binding of the CLOCK-BMAL1 complex and that the nonphosphorylatable mutations inhibit the PER-dependent displacement (reduction of DNA-binding ability) of the CLOCK-BMAL1 complex from DNA. Biochemical experiments supported the importance of these phosphorylation sites for displacement of the complex in the PER2-dependent inhibition. Our results provide direct evidence that phosphorylation of CLOCK-Ser38/Ser42 and BMAL1-Ser78 plays a crucial role in the PER-dependent inhibition and the determination of the circadian period.
Collapse
Affiliation(s)
- Yuta Otobe
- Department of Biological Sciences, School of Science, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
- Circadian Clock Project, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo156-8506, Japan
| | - Eui Min Jeong
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon34141, Republic of Korea
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon34141, Republic of Korea
| | - Shunsuke Ito
- Department of Biological Sciences, School of Science, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
- Circadian Clock Project, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo156-8506, Japan
| | - Yuta Shinohara
- Division of Molecular Psychoimmunology, Institute for Genetic Medicine and Graduate School of Medicine, Hokkaido University, Kita-Ku, Sapporo060-0815, Japan
| | - Nobuhiro Kurabayashi
- Circadian Clock Project, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo156-8506, Japan
| | - Atsu Aiba
- Department of Biological Sciences, School of Science, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
- Laboratory of Animal Resources, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
| | - Yoshitaka Fukada
- Department of Biological Sciences, School of Science, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
- Circadian Clock Project, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo156-8506, Japan
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon34141, Republic of Korea
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon34141, Republic of Korea
| | - Hikari Yoshitane
- Department of Biological Sciences, School of Science, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan
- Circadian Clock Project, Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo156-8506, Japan
| |
Collapse
|
262
|
Shpigler A, Kolet N, Golan S, Weisbart E, Zaritsky A. Anomaly detection for high-content image-based phenotypic cell profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.595856. [PMID: 38895267 PMCID: PMC11185510 DOI: 10.1101/2024.06.01.595856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.
Collapse
Affiliation(s)
- Alon Shpigler
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Naor Kolet
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Shahar Golan
- Department of Computer Science, Jerusalem College of Technology, 91160 Jerusalem, Israel
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge (MA), USA
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| |
Collapse
|
263
|
Rodriguez-Flores CJ, Barrena N, Olaverri-Mendizabal D, Ochoa I, Valcárcel LV, Planes FJ. gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python. Bioinformatics 2024; 40:btae318. [PMID: 38748994 PMCID: PMC11199197 DOI: 10.1093/bioinformatics/btae318] [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: 11/07/2023] [Revised: 04/08/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
MOTIVATION The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. RESULTS Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. AVAILABILITY AND IMPLEMENTATION The Python package gMCSpy and the data underlying this manuscript can be accessed at: https://github.com/PlanesLab/gMCSpy.
Collapse
Affiliation(s)
- Carlos J Rodriguez-Flores
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Naroa Barrena
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Danel Olaverri-Mendizabal
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Idoia Ochoa
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| | - Luis V Valcárcel
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| | - Francisco J Planes
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| |
Collapse
|
264
|
Wagle MM, Long S, Chen C, Liu C, Yang P. Interpretable deep learning in single-cell omics. Bioinformatics 2024; 40:btae374. [PMID: 38889275 PMCID: PMC11211213 DOI: 10.1093/bioinformatics/btae374] [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: 02/25/2024] [Revised: 05/11/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. RESULTS In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions.
Collapse
Affiliation(s)
- Manoj M Wagle
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Siqu Long
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Carissa Chen
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Chunlei Liu
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| |
Collapse
|
265
|
Mohammed A, Amsalu B, Hailu M, Sintayehu Y, Weldeamanuel T, Belay Y, Hassen Z, Dinkesa T, Dechasa N, Mengist B, Mengesha T, Nuri A, Getnet T, Manaye Y, Aliyi Usso A, Legesse H, Sertsu A. Indigenous herbal medicine use and its associated factors among pregnant women attending antenatal care at public health facilities in Dire Dawa, Ethiopia: a cross-sectional study. BMJ Open 2024; 14:e079719. [PMID: 38830740 PMCID: PMC11149149 DOI: 10.1136/bmjopen-2023-079719] [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: 09/13/2023] [Accepted: 02/29/2024] [Indexed: 06/05/2024] Open
Abstract
OBJECTIVE The aim of this study was to investigate the prevalence of indigenous herbal medicine use and its associated factors among pregnant women attending antenatal care (ANC) at public health facilities in Dire Dawa, Ethiopia. DESIGN A facility-based cross-sectional study design. SETTING The study was conducted in seven public health facilities (one referral hospital, three urban and three rural health centres) in Dire Dawa, Ethiopia, from October to November 2022. PARTICIPANTS 628 pregnant women of any gestational age who had been on ANC follow-up at selected public health facilities were included. MAIN OUTCOME MEASURES Prevalence of indigenous herbal medicine (users vs non-users) and associated factors. RESULTS The study revealed that 47.8% (95% CI 43.8% to 51.6%) of pregnant women used herbal medicines. Lack of formal education (adjusted OR, AOR 5.47, 95% CI 2.40 to 12.46), primary level (AOR 4.74, 95% CI 2.15 to 10.44), housewives (AOR 4.15, 95% CI 1.83 to 9.37), number of ANC visits (AOR 2.58, 95% CI 1.27 to 5.25), insufficient knowledge (AOR 4.58, 95% CI 3.02 to 6.77) and favourable perception (AOR 2.54, 95% CI 1.71 to 3.77) were factors significantly associated with herbal medicine use. The most commonly used herbs were garden cress (Lepidium sativum) (32%), bitter leaf (Vernonia amygdalina) (25.2%), moringa (Moringa oleifera) (24.5%). Common indications were related to gastrointestinal problems, blood pressure and sugar. CONCLUSION The prevalence of herbal medicine use is high (one in two pregnant women) and significantly associated with education level, occupation, ANC visits, knowledge and perceptions. The study's findings are helpful in advancing comprehension of herbal medicines using status, types and enforcing factors. It is essential that health facilities provide herbal counselling during ANC visits, and health regulatory bodies ought to raise awareness and implement interventions to lower the risks from over-the-counter herbal medicine use by pregnant women.
Collapse
Affiliation(s)
- Aminu Mohammed
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Bezabih Amsalu
- Department of Public Health, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Mickiale Hailu
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Yitagesu Sintayehu
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Tadesse Weldeamanuel
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Yalelet Belay
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Zeyniya Hassen
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Tesema Dinkesa
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Natnael Dechasa
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Betelhem Mengist
- Department of Midwifery, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Teshale Mengesha
- Department of Pediatrics and Child Health Nursing, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Aliya Nuri
- Department of Public Health, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Tewodros Getnet
- Department of Public Health, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Yibekal Manaye
- Department of Public Health, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Ethiopia
| | - Ahmedin Aliyi Usso
- School of Midwifery, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia
| | - Henok Legesse
- School of Nursing and Midwifery, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia
| | - Addisu Sertsu
- School of Nursing and Midwifery, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia
| |
Collapse
|
266
|
Peng D, Cahan P. OneSC: A computational platform for recapitulating cell state transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596831. [PMID: 38895453 PMCID: PMC11185539 DOI: 10.1101/2024.05.31.596831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.
Collapse
Affiliation(s)
- Da Peng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| |
Collapse
|
267
|
Marafie SK, Al-Mulla F, Abubaker J. mTOR: Its Critical Role in Metabolic Diseases, Cancer, and the Aging Process. Int J Mol Sci 2024; 25:6141. [PMID: 38892329 PMCID: PMC11173325 DOI: 10.3390/ijms25116141] [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: 05/03/2024] [Revised: 05/26/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
The mammalian target of rapamycin (mTOR) is a pivotal regulator, integrating diverse environmental signals to control fundamental cellular functions, such as protein synthesis, cell growth, survival, and apoptosis. Embedded in a complex network of signaling pathways, mTOR dysregulation is implicated in the onset and progression of a range of human diseases, including metabolic disorders such as diabetes and cardiovascular diseases, as well as various cancers. mTOR also has a notable role in aging. Given its extensive biological impact, mTOR signaling is a prime therapeutic target for addressing these complex conditions. The development of mTOR inhibitors has proven advantageous in numerous research domains. This review delves into the significance of mTOR signaling, highlighting the critical components of this intricate network that contribute to disease. Additionally, it addresses the latest findings on mTOR inhibitors and their clinical implications. The review also emphasizes the importance of developing more effective next-generation mTOR inhibitors with dual functions to efficiently target the mTOR pathways. A comprehensive understanding of mTOR signaling will enable the development of effective therapeutic strategies for managing diseases associated with mTOR dysregulation.
Collapse
Affiliation(s)
- Sulaiman K. Marafie
- Biochemistry and Molecular Biology Department, Dasman Diabetes Institute, P.O. Box 1180, Dasman 15462, Kuwait
| | - Fahd Al-Mulla
- Department of Translational Research, Dasman Diabetes Institute, P.O. Box 1180, Dasman 15462, Kuwait;
| | - Jehad Abubaker
- Biochemistry and Molecular Biology Department, Dasman Diabetes Institute, P.O. Box 1180, Dasman 15462, Kuwait
| |
Collapse
|
268
|
Ribba B, Pagano G, Korsbo N, Ivaturi V, Soubret A. Artificial Intelligence and Disease Modeling: Focus on Neurological Disorders. Clin Pharmacol Ther 2024; 115:1208-1211. [PMID: 38480479 DOI: 10.1002/cpt.3253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/01/2024] [Indexed: 05/14/2024]
Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Gennaro Pagano
- Roche Pharma Research and Early Development (pRED), F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | | | | | - Antoine Soubret
- Roche Pharma Research and Early Development (pRED), F. Hoffmann-La Roche Ltd., Basel, Switzerland
| |
Collapse
|
269
|
Imenez Silva PH, Pepin M, Figurek A, Gutiérrez-Jiménez E, Bobot M, Iervolino A, Mattace-Raso F, Hoorn EJ, Bailey MA, Hénaut L, Nielsen R, Frische S, Trepiccione F, Hafez G, Altunkaynak HO, Endlich N, Unwin R, Capasso G, Pesic V, Massy Z, Wagner CA. Animal models to study cognitive impairment of chronic kidney disease. Am J Physiol Renal Physiol 2024; 326:F894-F916. [PMID: 38634137 DOI: 10.1152/ajprenal.00338.2023] [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: 10/19/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
Mild cognitive impairment (MCI) is common in people with chronic kidney disease (CKD), and its prevalence increases with progressive loss of kidney function. MCI is characterized by a decline in cognitive performance greater than expected for an individual age and education level but with minimal impairment of instrumental activities of daily living. Deterioration can affect one or several cognitive domains (attention, memory, executive functions, language, and perceptual motor or social cognition). Given the increasing prevalence of kidney disease, more and more people with CKD will also develop MCI causing an enormous disease burden for these individuals, their relatives, and society. However, the underlying pathomechanisms are poorly understood, and current therapies mostly aim at supporting patients in their daily lives. This illustrates the urgent need to elucidate the pathogenesis and potential therapeutic targets and test novel therapies in appropriate preclinical models. Here, we will outline the necessary criteria for experimental modeling of cognitive disorders in CKD. We discuss the use of mice, rats, and zebrafish as model systems and present valuable techniques through which kidney function and cognitive impairment can be assessed in this setting. Our objective is to enable researchers to overcome hurdles and accelerate preclinical research aimed at improving the therapy of people with CKD and MCI.
Collapse
Affiliation(s)
- Pedro H Imenez Silva
- Institute of Physiology, University of Zurich, Zurich, Switzerland
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam, The Netherlands
| | - Marion Pepin
- Institut National de la Santé et de la Recherche Médicale U-1018 Centre de Recherche en Épidémiologie et Santé des Population, Équipe 5, Paris-Saclay University, Versailles Saint-Quentin-en-Yvelines University, Villejuif, France
- Department of Geriatrics, Centre Hospitalier Universitaire Ambroise Paré, Assistance Publique-Hôpitaux de Paris Université Paris-Saclay, Paris, France
| | - Andreja Figurek
- Institute of Anatomy, University of Zurich, Zurich, Switzerland
| | - Eugenio Gutiérrez-Jiménez
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Mickaël Bobot
- Centre de Néphrologie et Transplantation Rénale, Hôpital de la Conception, Assistance Publique-Hopitaux de Marseille, and INSERM 1263, Institut National de la Recherche Agronomique 1260, C2VN, Aix-Marseille Universitaire, Marseille, France
| | - Anna Iervolino
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli,' Naples, Italy
| | - Francesco Mattace-Raso
- Division of Geriatrics, Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Ewout J Hoorn
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam, The Netherlands
| | - Matthew A Bailey
- Edinburgh Kidney, Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United Kingdom
| | - Lucie Hénaut
- UR UPJV 7517, Jules Verne University of Picardie, Amiens, France
| | - Rikke Nielsen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | | | - Francesco Trepiccione
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli,' Naples, Italy
| | - Gaye Hafez
- Department of Pharmacology, Faculty of Pharmacy, Altinbas University, Istanbul, Turkey
| | - Hande O Altunkaynak
- Department of Pharmacology, Gulhane Faculty of Pharmacy, University of Health Sciences, Istanbul, Turkey
| | - Nicole Endlich
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Robert Unwin
- Department of Renal Medicine, Royal Free Hospital, University College London, London, United Kingdom
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli,' Naples, Italy
- Biogem Research Institute, Ariano Irpino, Italy
| | - Vesna Pesic
- Department of Physiology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Ziad Massy
- Centre for Research in Epidemiology and Population Health, INSERM UMRS 1018, Clinical Epidemiology Team, University Paris-Saclay, University Versailles-Saint Quentin, Villejuif, France
- Department of Nephrology, Centre Hospitalier Universitaire Ambroise Paré, Assistance Publique-Hôpitaux de Paris Université Paris-Saclay, Paris, France
| | - Carsten A Wagner
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| |
Collapse
|
270
|
Catamero D, Benito PB, Shenoy S, Doyle M, Fowler J, Kobos R, Banerjee A, Kruyswijk S. Nursing Considerations for Cytokine Release Syndrome in Relapsed/Refractory Multiple Myeloma: Experience with Teclistamab from the MajesTEC-1 Study. Semin Oncol Nurs 2024; 40:151621. [PMID: 38600011 DOI: 10.1016/j.soncn.2024.151621] [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: 10/06/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 04/12/2024]
Abstract
OBJECTIVES Cytokine release syndrome (CRS) is a systemic inflammatory response that is commonly observed as a class effect of T-cell-redirecting therapies. This article provides important practical guidance for nurses relating to the diagnosis, monitoring, and management of CRS in patients receiving teclistamab, based on experience from the MajesTEC-1 clinical trial and real-life nursing practice. METHODS MajesTEC-1 is a phase 1/2 study of teclistamab in heavily pretreated patients with relapsed/refractory multiple myeloma. To mitigate the risk of high-grade CRS, patients were carefully monitored for early signs and symptoms of CRS (including fever, which must have fully resolved before teclistamab administration). RESULTS A survey of nurses from several of the study sites provided additional real-life insights into nursing best practices for managing CRS from four academic institutions in three countries. CONCLUSIONS In MajesTEC-1, 72% of patients treated with teclistamab experienced CRS, the majority of which was low grade. All cases resolved and none led to treatment discontinuation. Real-life supportive measures for CRS are generally aligned with those outlined in the study. IMPLICATIONS FOR NURSING PRACTICE Because nurses are on the frontline of patient care, they play a crucial role in promptly recognizing the signs and symptoms of CRS and responding with timely and appropriate supportive treatment. This review provides important practical guidance for nurses on diagnosis, monitoring, and management of CRS in patients receiving teclistamab, based on experience from the MajesTEC-1 trial and real-life nursing practice.
Collapse
Affiliation(s)
- Donna Catamero
- Associate Director, Myeloma Research, Adjunct Faculty, Mount Sinai Phillips School of Nursing, The Mount Sinai Health System, New York, NY.
| | | | - Samantha Shenoy
- Research Nurse Practitioner, Hematology, Blood & Marrow Transplant, Cellular Therapy, University of California San Francisco, San Francisco, CA
| | - Margaret Doyle
- Sr Global Medical Affairs Leader, Janssen Sciences Ireland, Dublin, Ireland
| | - Jessica Fowler
- Group Medical Director, Medical Group Oncology Heme, Janssen Scientific Affairs, Horsham, PA
| | - Rachel Kobos
- VP Oncology Research and Development, US Clinical Oncology PA, Janssen Research & Development, Raritan, NJ
| | - Arnob Banerjee
- Global Medical Head, Clinical Research, Oncology, Early Development, Janssen Research & Development, Spring House, PA
| | - Sandy Kruyswijk
- Team Leader, Research Nurse, Indication Team Multiple Myeloma, Amyloidosis, Hematology Trial Office, Amsterdam University Medical Center, Amsterdam, Netherlands
| |
Collapse
|
271
|
Cheng Y, Liu Y, Xu D, Zhang D, Yang Y, Miao Y, He S, Xu Q, Li E. An engineered TNFR1-selective human lymphotoxin-alpha mutant delivered by an oncolytic adenovirus for tumor immunotherapy. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167122. [PMID: 38492783 DOI: 10.1016/j.bbadis.2024.167122] [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: 10/16/2023] [Revised: 02/25/2024] [Accepted: 03/10/2024] [Indexed: 03/18/2024]
Abstract
Lymphotoxin α (LTα) is a soluble factor produced by activated lymphocytes which is cytotoxic to tumor cells. Although a promising candidate in cancer therapy, the application of recombinant LTα has been limited by its instability and toxicity by systemic administration. Secreted LTα interacts with several distinct receptors for its biological activities. Here, we report a TNFR1-selective human LTα mutant (LTα Q107E) with potent antitumor activity. Recombinant LTα Q107E with N-terminal 23 and 27 aa deletion (named LTα Q1 and Q2, respectively) showed selectivity to TNFR1 in both binding and NF-κB pathway activation assays. To test the therapeutic potential, we constructed an oncolytic adenovirus (oAd) harboring LTα Q107E Q2 mutant (named oAdQ2) and assessed the antitumor effect in mouse xenograft models. Intratumoral delivery of oAdQ2 inhibited tumor growth. In addition, oAdQ2 treatment enhanced T cell and IFNγ-positive CD8 T lymphocyte infiltration in a human PBMC reconstituted-SCID mouse xenograft model. This study provides evidence that reengineering of bioactive cytokines with tissue or cell specific properties may potentiate their therapeutic potential of cytokines with multiple receptors.
Collapse
Affiliation(s)
- Yan Cheng
- State Key Laboratory of Pharmaceutical Biotechnology, Medical School, Nanjing University, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, China
| | - Yu Liu
- Department of Oncology, Shanghai Tenth People's Hospital, Shanghai, China
| | - Dongge Xu
- State Key Laboratory of Pharmaceutical Biotechnology, Medical School, Nanjing University, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, China
| | - Dan Zhang
- Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, China
| | - Yang Yang
- Shanghai Baoyuan Pharmaceutical Co., Ltd, Shanghai, China
| | - Yuqing Miao
- The Affiliated Yancheng First People's Hospital, Medical School, Nanjing University, Yancheng, China
| | - Susu He
- State Key Laboratory of Pharmaceutical Biotechnology, Medical School, Nanjing University, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, China; The Affiliated Yancheng First People's Hospital, Medical School, Nanjing University, Yancheng, China
| | - Qing Xu
- Department of Oncology, Shanghai Tenth People's Hospital, Shanghai, China; Department of Medical Oncology, Shanghai Tenth People's Hospital, Tongji University Cancer Center, School of Medicine, Tongji University, Shanghai, China.
| | - Erguang Li
- State Key Laboratory of Pharmaceutical Biotechnology, Medical School, Nanjing University, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, China; Department of Oncology, Shanghai Tenth People's Hospital, Shanghai, China.
| |
Collapse
|
272
|
He Z, Xie L, Liu J, Wei X, Zhang W, Mei Z. Novel insight into the role of A-kinase anchoring proteins (AKAPs) in ischemic stroke and therapeutic potentials. Biomed Pharmacother 2024; 175:116715. [PMID: 38739993 DOI: 10.1016/j.biopha.2024.116715] [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: 02/25/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024] Open
Abstract
Ischemic stroke, a devastating disease associated with high mortality and disability worldwide, has emerged as an urgent public health issue. A-kinase anchoring proteins (AKAPs) are a group of signal-organizing molecules that compartmentalize and anchor a wide range of receptors and effector proteins and have a major role in stabilizing mitochondrial function and promoting neurodevelopmental development in the central nervous system (CNS). Growing evidence suggests that dysregulation of AKAPs expression and activity is closely associated with oxidative stress, ion disorder, mitochondrial dysfunction, and blood-brain barrier (BBB) impairment in ischemic stroke. However, the underlying mechanisms remain inadequately understood. This review provides a comprehensive overview of the composition and structure of A-kinase anchoring protein (AKAP) family members, emphasizing their physiological functions in the CNS. We explored in depth the molecular and cellular mechanisms of AKAP complexes in the pathological progression and risk factors of ischemic stroke, including hypertension, hyperglycemia, lipid metabolism disorders, and atrial fibrillation. Herein, we highlight the potential of AKAP complexes as a pharmacological target against ischemic stroke in the hope of inspiring translational research and innovative clinical approaches.
Collapse
Affiliation(s)
- Ziyu He
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese Medicine and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Letian Xie
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese Medicine and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Jiyong Liu
- Hunan Provincial Key Laboratory of Traditional Chinese Medicine Diagnostics, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Xuan Wei
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese Medicine and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Wenli Zhang
- School of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China.
| | - Zhigang Mei
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese Medicine and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China; Third-Grade Pharmacological Laboratory on Chinese Medicine Approved by State Administration of Traditional Chinese Medicine, College of Medicine and Health Sciences, China Three Gorges University, Yichang, Hubei 443002, China.
| |
Collapse
|
273
|
Xie X, Sinha S. Quantitative estimates of the regulatory influence of long non-coding RNAs on global gene expression variation using TCGA breast cancer transcriptomic data. PLoS Comput Biol 2024; 20:e1012103. [PMID: 38838009 PMCID: PMC11198904 DOI: 10.1371/journal.pcbi.1012103] [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: 01/19/2023] [Revised: 06/25/2024] [Accepted: 04/24/2024] [Indexed: 06/07/2024] Open
Abstract
Long non-coding RNAs (lncRNAs) have received attention in recent years for their regulatory roles in diverse biological contexts including cancer, yet large gaps remain in our understanding of their mechanisms and global maps of their targets. In this work, we investigated a basic unanswered question of lncRNA systems biology: to what extent can gene expression variation across individuals be attributed to lncRNA-driven regulation? To answer this, we analyzed RNA-seq data from a cohort of breast cancer patients, explaining each gene's expression variation using a small set of automatically selected lncRNA regulators. A key aspect of this analysis is that it accounts for confounding effects of transcription factors (TFs) as common regulators of a lncRNA-mRNA pair, to enrich the explained gene expression for lncRNA-mediated regulation. We found that for 16% of analyzed genes, lncRNAs can explain more than 20% of expression variation. We observed 25-50% of the putative regulator lncRNAs to be in 'cis' to, i.e., overlapping or located proximally to the target gene. This led us to quantify the global regulatory impact of such cis-located lncRNAs, which was found to be substantially greater than that of trans-located lncRNAs. Additionally, by including statistical interaction terms involving lncRNA-protein pairs as predictors in our regression models, we identified cases where a lncRNA's regulatory effect depends on the presence of a TF or RNA-binding protein. Finally, we created a high-confidence lncRNA-gene regulatory network whose edges are supported by co-expression as well as a plausible mechanism such as cis-action, protein scaffolding or competing endogenous RNAs. Our work is a first attempt to quantify the extent of gene expression control exerted globally by lncRNAs, especially those located proximally to their regulatory targets, in a specific biological (breast cancer) context. It also marks a first step towards systematic reconstruction of lncRNA regulatory networks, going beyond the current paradigm of co-expression networks, and motivates future analyses assessing the generalizability of our findings to additional biological contexts.
Collapse
Affiliation(s)
- Xiaoman Xie
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America
| | - Saurabh Sinha
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| |
Collapse
|
274
|
Ha Y, Ma HR, Wu F, Weiss A, Duncker K, Xu HZ, Lu J, Golovsky M, Reker D, You L. Data-driven learning of structure augments quantitative prediction of biological responses. PLoS Comput Biol 2024; 20:e1012185. [PMID: 38829926 PMCID: PMC11233023 DOI: 10.1371/journal.pcbi.1012185] [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: 12/11/2023] [Revised: 07/09/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.
Collapse
Affiliation(s)
- Yuanchi Ha
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Quantitative Biodesign, Duke University, Durham, North Carolina, United States of America
| | - Helena R. Ma
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Quantitative Biodesign, Duke University, Durham, North Carolina, United States of America
| | - Feilun Wu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Andrea Weiss
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Katherine Duncker
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Quantitative Biodesign, Duke University, Durham, North Carolina, United States of America
| | - Helen Z. Xu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Jia Lu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Quantitative Biodesign, Duke University, Durham, North Carolina, United States of America
| | - Max Golovsky
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Quantitative Biodesign, Duke University, Durham, North Carolina, United States of America
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Quantitative Biodesign, Duke University, Durham, North Carolina, United States of America
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, United States of America
| |
Collapse
|
275
|
Zehetner L, Széliová D, Kraus B, Hernandez Bort JA, Zanghellini J. Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways. PLoS Comput Biol 2024; 20:e1012236. [PMID: 38913731 PMCID: PMC11226097 DOI: 10.1371/journal.pcbi.1012236] [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: 12/09/2023] [Revised: 07/05/2024] [Accepted: 06/07/2024] [Indexed: 06/26/2024] Open
Abstract
Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.
Collapse
Affiliation(s)
- Leopold Zehetner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
- Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Diana Széliová
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Barbara Kraus
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Juan A. Hernandez Bort
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| |
Collapse
|
276
|
Guo Y, Luo L, Zhu J, Li C. Advance in Multi-omics Research Strategies on Cholesterol Metabolism in Psoriasis. Inflammation 2024; 47:839-852. [PMID: 38244176 DOI: 10.1007/s10753-023-01961-9] [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: 09/24/2023] [Revised: 11/29/2023] [Accepted: 12/25/2023] [Indexed: 01/22/2024]
Abstract
The skin is a complex and dynamic organ where homeostasis is maintained through the intricate interplay between the immune system and metabolism, particularly cholesterol metabolism. Various factors such as cytokines, inflammatory mediators, cholesterol metabolites, and metabolic enzymes play crucial roles in facilitating these interactions. Dysregulation of this delicate balance contributes to the pathogenic pathways of inflammatory skin conditions, notably psoriasis. In this article, we provide an overview of omics biomarkers associated with psoriasis in relation to cholesterol metabolism. We explore multi-omics approaches that reveal the communication between immunometabolism and psoriatic inflammation. Additionally, we summarize the use of multi-omics strategies to uncover the complexities of multifactorial and heterogeneous inflammatory diseases. Finally, we highlight potential future perspectives related to targeted drug therapies and research areas that can advance precise medicine. This review aims to serve as a valuable resource for those investigating the role of cholesterol metabolism in psoriasis.
Collapse
Affiliation(s)
- Youming Guo
- Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China
- Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Nanjing, Jiangsu, China
| | - Lingling Luo
- Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China
| | - Jing Zhu
- Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China
| | - Chengrang Li
- Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
- Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Nanjing, Jiangsu, China.
| |
Collapse
|
277
|
Toya Y, Shimizu H. Coupling and uncoupling growth and product formation for producing chemicals. Curr Opin Biotechnol 2024; 87:103133. [PMID: 38640846 DOI: 10.1016/j.copbio.2024.103133] [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: 12/29/2023] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 04/21/2024]
Abstract
Microbial fermentation employs two strategies: growth- and nongrowth-coupled productions. Stoichiometric metabolic models with flux balance analysis enable pathway engineering to couple target synthesis with growth, yielding numerous successful results. Growth-coupled engineering also contributes to improving bottleneck flux through subsequent adaptive evolution. However, because growth-coupled production inevitably shares resources between biomass and target syntheses, the cost-effective production of bulk chemicals mandates a nongrowth-coupled approach. In such processes, understanding how and when to transition the metabolic state from growth to production modes becomes crucial, as does maintaining cellular activity during the nongrowing state to achieve high productivity. In this paper, we review recent technologies for growth-coupled and nongrowth-coupled production, considering their advantages and disadvantages.
Collapse
Affiliation(s)
- Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| |
Collapse
|
278
|
Lewis KL, Cheah CY. The value of bispecific antibodies in relapsed and refractory DLBCL. Leuk Lymphoma 2024; 65:720-735. [PMID: 38454535 DOI: 10.1080/10428194.2024.2323085] [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: 10/18/2023] [Accepted: 02/19/2024] [Indexed: 03/09/2024]
Abstract
Diffuse large B-cell lymphoma (DLBCL) may be cured with anti-CD20 based chemoimmunotherapy in the majority of cases, however, relapsed/refractory disease occurs in 30-40% patients, and despite significant recent therapeutic advances, continues to represent an unmet clinical need. Bispecific antibodies represent a novel class of therapy currently in development for relapsed/refractory B-cell lymphoma. This review discusses the background clinical need, mechanism of action, and clinical data including efficacy and toxicity for bispecific antibodies in DLBCL, focusing on the most advanced class in development; CD20 targeting T-cell engaging antibodies. Emerging possibilities for future use of bispecific antibodies is also discussed, including novel and cytotoxic combination regimens in relapsed and first-line settings.
Collapse
MESH Headings
- Humans
- Antibodies, Bispecific/therapeutic use
- Antibodies, Bispecific/pharmacology
- Lymphoma, Large B-Cell, Diffuse/drug therapy
- Lymphoma, Large B-Cell, Diffuse/immunology
- Drug Resistance, Neoplasm/immunology
- Antineoplastic Agents, Immunological/therapeutic use
- Antineoplastic Agents, Immunological/adverse effects
- Neoplasm Recurrence, Local/immunology
- Neoplasm Recurrence, Local/drug therapy
- Treatment Outcome
- Antineoplastic Combined Chemotherapy Protocols/therapeutic use
- Antineoplastic Combined Chemotherapy Protocols/adverse effects
Collapse
Affiliation(s)
- Katharine Louise Lewis
- Department of Haematology, Sir Charles Gairdner Hospital, Nedlands, Australia
- Linear Clinical Research, Nedlands, Australia
- Medical School, Division of Internal Medicine, University of Western Australia, Nedlands, Australia
| | - Chan Yoon Cheah
- Department of Haematology, Sir Charles Gairdner Hospital, Nedlands, Australia
- Linear Clinical Research, Nedlands, Australia
- Medical School, Division of Internal Medicine, University of Western Australia, Nedlands, Australia
- Department of Haematology, Pathwest, QEII, Nedlands, Australia
| |
Collapse
|
279
|
Nussinov R, Yavuz BR, Jang H. Anticancer drugs: How to select small molecule combinations? Trends Pharmacol Sci 2024; 45:503-519. [PMID: 38782689 PMCID: PMC11162304 DOI: 10.1016/j.tips.2024.04.012] [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: 03/20/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
Small molecules are at the forefront of anticancer therapies. Successive treatments with single molecules incur drug resistance, calling for combination. Here, we explore the tough choices oncologists face - not just which drugs to use but also the best treatment plans, based on factors such as target proteins, pathways, and gene expression. We consider the reality of cancer's disruption of normal cellular processes, highlighting why it's crucial to understand the ins and outs of current treatment methods. The discussion on using combination drug therapies to target multiple pathways sheds light on a promising approach while also acknowledging the hurdles that come with it, such as dealing with pathway crosstalk. We review options and provide examples and the mechanistic basis, altogether providing the first comprehensive guide to combinatorial therapy selection.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| |
Collapse
|
280
|
Kariya Y, Honma M. Applications of model simulation in pharmacological fields and the problems of theoretical reliability. Drug Metab Pharmacokinet 2024; 56:100996. [PMID: 38797090 DOI: 10.1016/j.dmpk.2024.100996] [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: 11/02/2023] [Revised: 12/23/2023] [Accepted: 12/31/2023] [Indexed: 05/29/2024]
Abstract
The use of mathematical models has become increasingly prevalent in pharmacological fields, particularly in drug development processes. These models are instrumental in tasks such as designing clinical trials and assessing factors like efficacy, toxicity, and clinical practice. Various types of models have been developed and documented. Nevertheless, emphasizing the reliability of parameter values is crucial, as they play a pivotal role in shaping the behavior of the system. In some instances, parameter values reported previously are treated as fixed values, which can lead to convergence towards values that deviate substantially from those found in actual biological systems. This is especially true when parameter values are determined through fitting to limited observations. To mitigate this risk, the reuse of parameter values from previous reports should be approached with a critical evaluation of their validity. Currently, there is a proposal for a simultaneous search for plausible values for all parameters using comprehensive search algorithms in both pharmacokinetic and pharmacodynamic or systems pharmacological models. Implementing these methodologies can help address issues related to parameter determination. Furthermore, integrating these approaches with methods developed in the field of machine-learning field has the potential to enhance the reliability of parameter values and the resulting model outputs.
Collapse
Affiliation(s)
- Yoshiaki Kariya
- Education Center for Medical Pharmaceutics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Laboratory of Pharmaceutical Regulatory Sciences, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Masashi Honma
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| |
Collapse
|
281
|
Mahajan M, Sarkar A, Mondal S. Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer. Cancer Med 2024; 13:e7391. [PMID: 38872418 PMCID: PMC11176588 DOI: 10.1002/cam4.7391] [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: 02/13/2024] [Revised: 05/22/2024] [Accepted: 06/02/2024] [Indexed: 06/15/2024] Open
Abstract
INTRODUCTION Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels. METHODS In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression. RESULTS Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways. CONCLUSIONS This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.
Collapse
Affiliation(s)
- Mohita Mahajan
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, K.K. Birla Goa campus, Goa, India
| | - Angshuman Sarkar
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, K.K. Birla Goa campus, Goa, India
| | - Sukanta Mondal
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, K.K. Birla Goa campus, Goa, India
| |
Collapse
|
282
|
Han L, Xiang X, Fu Y, Wei S, Zhang C, Li L, Liu Y, Lv H, Shan B, Zhao L. Periplcymarin targets glycolysis and mitochondrial oxidative phosphorylation of esophageal squamous cell carcinoma: Implication in anti-cancer therapy. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155539. [PMID: 38522311 DOI: 10.1016/j.phymed.2024.155539] [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: 10/16/2023] [Revised: 01/28/2024] [Accepted: 03/14/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Esophageal squamous cell carcinoma (ESCC) is the predominant histological subtype of esophageal cancer (EC) in China, and demonstrates varying levels of resistance to multiple chemotherapeutic agents. Our previous studies have proved that periplocin (CPP), derived from the extract of cortex periplocae, exhibiting the capacity to hinder proliferation and induce apoptosis in ESCC cells. Several studies have identified additional anti-cancer constituents in the extract of cortex periplocae, named periplcymarin (PPM), sharing similar compound structure with CPP. Nevertheless, the inhibitory effects of PPM on ESCC and their underlying mechanisms remain to be further elucidated. PURPOSE The aim of this study was to investigate function of PPM inhibiting the growth of ESCC in vivo and in vitro and to explore its underlying mechanism, providing the potential anti-tumor drug for ESCC. METHODS Initially, a comparative analysis was conducted on the inhibitory activity of three naturally compounds obtained from the extract of cortex periplocae on ESCC cells. Among these compounds, PPM was chosen for subsequent investigation owing to its comparatively structure and anti-tumor activity simultaneously. Subsequently, a series of biological functional experiments were carried out to assess the impact of PPM on the proliferation, apoptosis and cell cycle arrest of ESCC cells in vitro. In order to elucidate the molecular mechanism of PPM, various methodologies were employed, including bioinformatics analyses and mechanistic experiments such as high-performance liquid chromatography combined with mass spectrometry (HPLC-MS), cell glycolysis pressure and mitochondrial pressure test. Additionally, the anti-tumor effects of PPM on ESCC cells and potential toxic side effects were evaluated in vivo using the nude mice xenograft assay. RESULTS Our study revealed that PPM possesses the ability to impede the proliferation of ESCC cells, induce apoptosis, and arrest the cell cycle of ESCC cells in the G2/M phase in vitro. Mechanistically, PPM exerted its effects by modulating glycolysis and mitochondrial oxidative phosphorylation (OXPHOS), as confirmed by glycolysis pressure and mitochondrial pressure tests. Moreover, rescue assays demonstrated that PPM inhibits glycolysis and OXPHOS in ESCC cells through the PI3K/AKT and MAPK/ERK signaling pathways. Additionally, we substantiated that PPM effectively suppresses the growth of ESCC cells in vivo, with only modest potential toxic side effects. CONCLUSION Our study provides novel evidence that PPM has the potential to simultaneously target glycolysis and mitochondrial OXPHOS in ESCC cells. This finding highlights the need for further investigation into PPM as a promising therapeutic agent that targets the tumor glucose metabolism pathway in ESCC.
Collapse
Affiliation(s)
- Lujuan Han
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Department of Pathogenic Biology, Hebei Medical University, Zhongshan Road 361, Shijiazhuang, 050017, PR China
| | - Xiaohan Xiang
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China
| | - Yuhui Fu
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China
| | - Sisi Wei
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China
| | - Cong Zhang
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China
| | - Lei Li
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China
| | - Yueping Liu
- Department of Pathology, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China
| | - Huilai Lv
- Department of Thoracic Surgery, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China
| | - Baoen Shan
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China.
| | - Lianmei Zhao
- Research Center, the Fourth Hospital of Hebei Medical University, Jiankang Road 12, Shijiazhuang, 050011, PR China; Key Laboratory of Tumor Gene Diagnosis, Prevention and Therapy, Clinical Oncology Research Center, Hebei Province, Shijiazhuang, 050011, PR China.
| |
Collapse
|
283
|
Islam MM, Kolling GL, Glass EM, Goldberg JB, Papin JA. Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly representative phenotypes. Microb Genom 2024; 10:001259. [PMID: 38836744 PMCID: PMC11261902 DOI: 10.1099/mgen.0.001259] [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: 10/20/2023] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
Pseudomonas aeruginosa is a leading cause of infections in immunocompromised individuals and in healthcare settings. This study aims to understand the relationships between phenotypic diversity and the functional metabolic landscape of P. aeruginosa clinical isolates. To better understand the metabolic repertoire of P. aeruginosa in infection, we deeply profiled a representative set from a library of 971 clinical P. aeruginosa isolates with corresponding patient metadata and bacterial phenotypes. The genotypic clustering based on whole-genome sequencing of the isolates, multilocus sequence types, and the phenotypic clustering generated from a multi-parametric analysis were compared to each other to assess the genotype-phenotype correlation. Genome-scale metabolic network reconstructions were developed for each isolate through amendments to an existing PA14 network reconstruction. These network reconstructions show diverse metabolic functionalities and enhance the collective P. aeruginosa pangenome metabolic repertoire. Characterizing this rich set of clinical P. aeruginosa isolates allows for a deeper understanding of the genotypic and metabolic diversity of the pathogen in a clinical setting and lays a foundation for further investigation of the metabolic landscape of this pathogen and host-associated metabolic differences during infection.
Collapse
Affiliation(s)
- Mohammad Mazharul Islam
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Glynis L. Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Emma M. Glass
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | | | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| |
Collapse
|
284
|
Caña-Bozada VH, Ovando-Vázquez C, Flores-Méndez LC, Martínez-Brown JM, Morales-Serna FN. Identifying potential drug targets in the kinomes of two monogenean species. Helminthologia 2024; 61:142-150. [PMID: 39040804 PMCID: PMC11260314 DOI: 10.2478/helm-2024-0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 05/24/2024] [Indexed: 07/24/2024] Open
Abstract
Protein kinases are enzymes involved in essential biological processes such as signal transduction, transcription, metabolism, and the cell cycle. Human kinases are targets for several drugs approved by the US Food and Drug Administration. Therefore, the identification and classification of kinases in other organisms, including pathogenic parasites, is an interesting subject of study. Monogeneans are platyhelminths, mainly ectoparasites, capable of causing health problems in farmed fish. Although some genomes and transcriptomes are available for monogenean species, their full repertoire of kinases is unknown. The aim of this study was to identify and classify the putative kinases in the transcriptomes of two monogeneans, Rhabdosynochus viridisi and Scutogyrus longicornis, and then to predict potential monogenean drug targets (MDTs) and selective inhibitor drugs using computational approaches. Monogenean kinases having orthologs in the lethal phenotype of C. elegans but not in fish or humans were considered MDTs. A total of 160 and 193 kinases were identified in R. viridisi and S. longicornis, respectively. Of these, 22 kinases, belonging mainly to the major groups CAMK, AGC, and TK, were classified as MDTs, five of which were evaluated further. Molecular docking analysis indicated that dihydroergotamine, ergotamine, and lomitapide have the highest affinity for the kinases BRSK and MEKK1. These well-known drugs could be evaluated in future studies for potential repurposing as anti-monogenean agents. The present study contributes valuable data for the development of new antiparasitic candidates for finfish aquaculture.
Collapse
Affiliation(s)
- V. H. Caña-Bozada
- Centro de Investigación en Alimentación y Desarrollo, A.C., Mazatlán, Sinaloa82112, Mexico
| | - C. Ovando-Vázquez
- Centro Nacional de Supercómputo, Instituto Potosino de investigación Científica y Tecnológica, San Luis Potosí78216, Mexico
- Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT), Ciudad de México, Mexico
| | - L. C. Flores-Méndez
- Centro de Investigación en Alimentación y Desarrollo, A.C., Mazatlán, Sinaloa82112, Mexico
- Present address:Universidad Autónoma de Occidente, Unidad Regional Mazatlán, Mazatlán, 82100, Sinaloa, Mexico
| | - J. M. Martínez-Brown
- Centro de Investigación en Alimentación y Desarrollo, A.C., Mazatlán, Sinaloa82112, Mexico
| | - F. N. Morales-Serna
- Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mazatlán82040, Sinaloa, Mexico
| |
Collapse
|
285
|
Yang L, He X, Xue Y, Zhi D, Meng Q, Zhao W, Gong X, Yue D, Dong K, Tian Y. Amelioration of melittin on adjuvant-induced rheumatoid arthritis: Integrated transcriptome and metabolome. Int J Biol Macromol 2024; 270:132293. [PMID: 38735618 DOI: 10.1016/j.ijbiomac.2024.132293] [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: 02/07/2024] [Revised: 04/02/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND Rheumatoid arthritis (RA) is a chronic autoimmune disease lacking a definitive cure. Although conventional treatments such as dexamethasone and methotrexate are prevalent, their usage is constrained by potential adverse effects. Melittin (MLT) has emerged as a promising natural anti-rheumatic drug; however, studies focusing on the role of MLT in modulating the expression and metabolism of RA-related genes are scarce. METHOD Arthritis was induced in rats using Complete Freund's Adjuvant (CFA), followed by MLT injections for treatment. Post-treatment, the inflammatory status of each group was assessed, and the mechanistic underpinnings of MLT's ameliorative effects on RA were elucidated through transcriptomic and metabolomic analyses. Additionally, this study conducted qRT-PCR validation of key therapeutic genes and characterized the molecular docking interactions of MLT with key receptor proteins (TNF-α and IL-1β) using the AutoDock Vina software. RESULT MLT significantly diminished redness and swelling in affected joints, ameliorated inflammatory cell infiltration, and mitigated joint damage. Integration of transcriptomic and metabolomic data revealed that MLT predominantly regulated the transcription levels of pathways and genes related to cytokines and immune responses, and the metabolic biomarkers of Sphingomyelin, fatty acid, and flavonoid. qRT-PCR confirmed MLT's downregulation of inflammation-related genes such as Il6, Jak2, Stat3, and Ptx3. Molecular docking simulations demonstrated the stable binding of MLT to TNF-α and IL-1β. CONCLUSION MLT demonstrated significant efficacy in alleviating RA. This study provides a comprehensive summary of MLT's impact on gene expression and metabolic processes associated with RA.
Collapse
Affiliation(s)
- Linfu Yang
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Xiying He
- First Clinical Medical College, Yunnan University of Chinese Medicine, Kunming 650000, China
| | - Yunfei Xue
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dandan Zhi
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Qingxin Meng
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Wenzheng Zhao
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Xueyang Gong
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dan Yue
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Kun Dong
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
| | - Yakai Tian
- Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honey Bee Resources, Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
| |
Collapse
|
286
|
Afroz S, Islam N, Habib MA, Reza MS, Ashad Alam M. Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network. Methods 2024; 226:138-150. [PMID: 38670415 DOI: 10.1016/j.ymeth.2024.04.017] [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: 07/18/2023] [Revised: 04/02/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024] Open
Abstract
In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.
Collapse
Affiliation(s)
- Sabrin Afroz
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Nadira Islam
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Md Ahsan Habib
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh; Statistical Learning Group, Bangladesh
| | - Md Selim Reza
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA; Statistical Learning Group, Bangladesh
| | - Md Ashad Alam
- Ochsner Center for Outcomes Research, Ochsner Research, Ochsner Clinic Foundation, New Orleans, LA 70121, USA; Statistical Learning Group, Bangladesh.
| |
Collapse
|
287
|
Shin S, Chae SJ, Lee S, Kim JK. Beyond homogeneity: Assessing the validity of the Michaelis-Menten rate law in spatially heterogeneous environments. PLoS Comput Biol 2024; 20:e1012205. [PMID: 38843305 PMCID: PMC11185478 DOI: 10.1371/journal.pcbi.1012205] [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: 01/26/2024] [Revised: 06/18/2024] [Accepted: 05/24/2024] [Indexed: 06/19/2024] Open
Abstract
The Michaelis-Menten (MM) rate law has been a fundamental tool in describing enzyme-catalyzed reactions for over a century. When substrates and enzymes are homogeneously distributed, the validity of the MM rate law can be easily assessed based on relative concentrations: the substrate is in large excess over the enzyme-substrate complex. However, the applicability of this conventional criterion remains unclear when species exhibit spatial heterogeneity, a prevailing scenario in biological systems. Here, we explore the MM rate law's applicability under spatial heterogeneity by using partial differential equations. In this study, molecules diffuse very slowly, allowing them to locally reach quasi-steady states. We find that the conventional criterion for the validity of the MM rate law cannot be readily extended to heterogeneous environments solely through spatial averages of molecular concentrations. That is, even when the conventional criterion for the spatial averages is satisfied, the MM rate law fails to capture the enzyme catalytic rate under spatial heterogeneity. In contrast, a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA), is accurate. Specifically, the tQSSA-based modified form, but not the original MM rate law, accurately predicts the drug clearance via cytochrome P450 enzymes and the ultrasensitive phosphorylation in heterogeneous environments. Our findings shed light on how to simplify spatiotemporal models for enzyme-catalyzed reactions in the right context, ensuring accurate conclusions and avoiding misinterpretations in in silico simulations.
Collapse
Affiliation(s)
- Seolah Shin
- Department of Applied Mathematics, Korea University, Sejong, Republic of Korea
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea
| | - Seok Joo Chae
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
| | - Seunggyu Lee
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea
- Division of Applied Mathematical Sciences, Korea University, Sejong, Republic of Korea
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
| |
Collapse
|
288
|
Rihm SD, Tan YR, Ang W, Hofmeister M, Deng X, Laksana MT, Quek HY, Bai J, Pascazio L, Siong SC, Akroyd J, Mosbach S, Kraft M. The digital lab manager: Automating research support. SLAS Technol 2024; 29:100135. [PMID: 38703999 DOI: 10.1016/j.slast.2024.100135] [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: 01/27/2024] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024]
Abstract
Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability.
Collapse
Affiliation(s)
- Simon D Rihm
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore; Department of Chemical Engineering and Biotechnology, University of Cambridge, Philipppa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom; Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
| | - Yong Ren Tan
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Wilson Ang
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Markus Hofmeister
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore; Department of Chemical Engineering and Biotechnology, University of Cambridge, Philipppa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom; Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
| | - Xinhong Deng
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Michael Teguh Laksana
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Hou Yee Quek
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Jiaru Bai
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philipppa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom
| | - Laura Pascazio
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Sim Chun Siong
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore
| | - Jethro Akroyd
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore; Department of Chemical Engineering and Biotechnology, University of Cambridge, Philipppa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom; CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, United Kingdom
| | - Sebastian Mosbach
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore; Department of Chemical Engineering and Biotechnology, University of Cambridge, Philipppa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom; CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, United Kingdom
| | - Markus Kraft
- CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 CREATE Way, CREATE Tower, #05-05, 138602, Singapore; Department of Chemical Engineering and Biotechnology, University of Cambridge, Philipppa Fawcett Drive, Cambridge, CB3 0AS, United Kingdom; CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, United Kingdom; School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore; The Alan Turing Institute, 2QR, John Dodson House, 96 Euston Rd, London, NW1 2DB, United Kingdom.
| |
Collapse
|
289
|
Wagner MM, Hogan WR, Levander JD, Diller M. Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines. J Biomed Inform 2024; 154:104647. [PMID: 38692465 PMCID: PMC11250896 DOI: 10.1016/j.jbi.2024.104647] [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: 11/15/2023] [Revised: 04/16/2024] [Accepted: 04/28/2024] [Indexed: 05/03/2024]
Abstract
OBJECTIVE To use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. METHOD We implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. RESULTS Precision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. CONCLUSION Algorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.
Collapse
Affiliation(s)
- Michael M Wagner
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, PA 15206-3701, USA.
| | - William R Hogan
- Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA
| | - John D Levander
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew Diller
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| |
Collapse
|
290
|
Zhou YT, Chu JH, Zhao SH, Li GL, Fu ZY, Zhang SJ, Gao XH, Ma W, Shen K, Gao Y, Li W, Yin YM, Zhao C. Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities. Acta Pharmacol Sin 2024; 45:1287-1304. [PMID: 38360930 PMCID: PMC11130324 DOI: 10.1038/s41401-024-01232-9] [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: 08/09/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.
Collapse
Affiliation(s)
- Ya-Ting Zhou
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jia-Hui Chu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shu-Han Zhao
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Ge-Li Li
- Gusu School, Nanjing Medical University, Suzhou, 215000, China
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China
| | - Zi-Yi Fu
- Department of Breast Disease Research Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Su-Jie Zhang
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China
| | - Xue-Hu Gao
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China
- Jiangsu Hengrui Medicine Co. Ltd, Shanghai, 200245, China
| | - Wen Ma
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China
| | - Kai Shen
- Jiangsu Hengrui Medicine Co. Ltd, Shanghai, 200245, China
| | - Yuan Gao
- QSPMed Technologies, Nanjing, 210000, China
| | - Wei Li
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yong-Mei Yin
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Chen Zhao
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
| |
Collapse
|
291
|
Toshimoto K. Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models. Drug Metab Pharmacokinet 2024; 56:101011. [PMID: 38833901 DOI: 10.1016/j.dmpk.2024.101011] [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: 11/06/2023] [Revised: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 06/06/2024]
Abstract
Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.
Collapse
Affiliation(s)
- Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Ibaraki, Japan.
| |
Collapse
|
292
|
Chakraborty S, Sharma G, Karmakar S, Banerjee S. Multi-OMICS approaches in cancer biology: New era in cancer therapy. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167120. [PMID: 38484941 DOI: 10.1016/j.bbadis.2024.167120] [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: 01/16/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 04/01/2024]
Abstract
Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.
Collapse
Affiliation(s)
- Sohini Chakraborty
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Gaurav Sharma
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sricheta Karmakar
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Satarupa Banerjee
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
| |
Collapse
|
293
|
Perrone MC, Lerner MG, Dunworth M, Ewald AJ, Bader JS. Prioritizing drug targets by perturbing biological network response functions. PLoS Comput Biol 2024; 20:e1012195. [PMID: 38935814 PMCID: PMC11236158 DOI: 10.1371/journal.pcbi.1012195] [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: 07/13/2023] [Revised: 07/10/2024] [Accepted: 05/24/2024] [Indexed: 06/29/2024] Open
Abstract
Therapeutic interventions are designed to perturb the function of a biological system. However, there are many types of proteins that cannot be targeted with conventional small molecule drugs. Accordingly, many identified gene-regulatory drivers and downstream effectors are currently undruggable. Drivers and effectors are often connected by druggable signaling and regulatory intermediates. Methods to identify druggable intermediates therefore have general value in expanding the set of targets available for hypothesis-driven validation. Here we identify and prioritize potential druggable intermediates by developing a network perturbation theory, termed NetPert, for response functions of biological networks. Dynamics are defined by a network structure in which vertices represent genes and proteins, and edges represent gene-regulatory interactions and protein-protein interactions. Perturbation theory for network dynamics prioritizes targets that interfere with signaling from driver to response genes. Applications to organoid models for metastatic breast cancer demonstrate the ability of this mathematical framework to identify and prioritize druggable intermediates. While the short-time limit of the perturbation theory resembles betweenness centrality, NetPert is superior in generating target rankings that correlate with previous wet-lab assays and are more robust to incomplete or noisy network data. NetPert also performs better than a related graph diffusion approach. Wet-lab assays demonstrate that drugs for targets identified by NetPert, including targets that are not themselves differentially expressed, are active in suppressing additional metastatic phenotypes.
Collapse
Affiliation(s)
- Matthew C. Perrone
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael G. Lerner
- Department of Physics, Engineering and Astronomy, Earlham College, Richmond, Indiana, United States of America
| | - Matthew Dunworth
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Andrew J. Ewald
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, United States of America
- Giovanis Institute for Translational Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Joel S. Bader
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, United States of America
- Giovanis Institute for Translational Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| |
Collapse
|
294
|
Wylezinski LS, Sesler CL, Shaginurova GI, Grigorenko EV, Wohlgemuth JG, Cockerill FR, Racke MK, Spurlock CF. Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates. J Mol Diagn 2024; 26:520-529. [PMID: 38522839 PMCID: PMC11163981 DOI: 10.1016/j.jmoldx.2024.03.003] [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: 08/30/2023] [Revised: 01/19/2024] [Accepted: 03/01/2024] [Indexed: 03/26/2024] Open
Abstract
This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methods, interrogating total RNA-sequencing data sets from peripheral whole blood of treatment-naïve patients with RRMS and NMO and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between patients with NMO and RRMS, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors identified biological associations and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Mitoxantrone and vorinostat-two identified small molecules with previously reported use in patients with NMO and experimental autoimmune encephalomyelitis-reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA-sequencing data further enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding further enhances detection of disease-specific signatures and possible therapeutic targets.
Collapse
Affiliation(s)
- Lukasz S Wylezinski
- Decode Health, Inc., Nashville, Tennessee; Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | | | | | | | - Jay G Wohlgemuth
- Quest Diagnostics, Secaucus, New Jersey; Trusted Health Advisors, San Juan Capistrano, California
| | - Franklin R Cockerill
- Decode Health, Inc., Nashville, Tennessee; Trusted Health Advisors, San Juan Capistrano, California; Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | | | - Charles F Spurlock
- Decode Health, Inc., Nashville, Tennessee; Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee; Wagner School of Public Service, New York University, New York, New York.
| |
Collapse
|
295
|
Victoria AJ, Astbury MJ, McCormick AJ. Engineering highly productive cyanobacteria towards carbon negative emissions technologies. Curr Opin Biotechnol 2024; 87:103141. [PMID: 38735193 DOI: 10.1016/j.copbio.2024.103141] [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: 02/28/2024] [Revised: 04/19/2024] [Accepted: 04/20/2024] [Indexed: 05/14/2024]
Abstract
Cyanobacteria are a diverse and ecologically important group of photosynthetic prokaryotes that contribute significantly to the global carbon cycle through the capture of CO2 as biomass. Cyanobacterial biotechnology could play a key role in a sustainable bioeconomy through negative emissions technologies (NETs), such as carbon sequestration or bioproduction. However, the primary issues of low productivities and high infrastructure costs currently limit the commercialisation of such applications. The isolation of several fast-growing strains and recent advancements in molecular biology tools now offer promising new avenues for improving yields, including metabolic engineering approaches guided by high-throughput screening and metabolic models. Furthermore, emerging research on engineering coculture communities could help to develop more robust culturing systems to support broader NET applications.
Collapse
Affiliation(s)
- Angelo J Victoria
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, EH9 3BF UK; Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, EH9 3BF UK
| | - Michael J Astbury
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, EH9 3BF UK; Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, EH9 3BF UK
| | - Alistair J McCormick
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, EH9 3BF UK; Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, EH9 3BF UK.
| |
Collapse
|
296
|
Zhang L, Xue G, Zhou X, Huang J, Li Z. A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems. PLoS Comput Biol 2024; 20:e1011882. [PMID: 38838038 PMCID: PMC11182560 DOI: 10.1371/journal.pcbi.1011882] [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: 02/02/2024] [Revised: 06/17/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
In embryonic development and organogenesis, cells sharing identical genetic codes acquire diverse gene expression states in a highly reproducible spatial distribution, crucial for multicellular formation and quantifiable through positional information. To understand the spontaneous growth of complexity, we constructed a one-dimensional division-decision model, simulating the growth of cells with identical genetic networks from a single cell. Our findings highlight the pivotal role of cell division in providing positional cues, escorting the system toward states rich in information. Moreover, we pinpointed lateral inhibition as a critical mechanism translating spatial contacts into gene expression. Our model demonstrates that the spatial arrangement resulting from cell division, combined with cell lineages, imparts positional information, specifying multiple cell states with increased complexity-illustrated through examples in C.elegans. This study constitutes a foundational step in comprehending developmental intricacies, paving the way for future quantitative formulations to construct synthetic multicellular patterns.
Collapse
Affiliation(s)
- Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Xiaolin Zhou
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
| | - Jiandong Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Chinese Academy of Sciences (CAS) Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhiyuan Li
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| |
Collapse
|
297
|
Oishi M, Sayama H, Toshimoto K, Nakayama T, Nagasaka Y. Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology. Drug Metab Pharmacokinet 2024; 56:101020. [PMID: 38797089 DOI: 10.1016/j.dmpk.2024.101020] [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: 11/06/2023] [Revised: 02/02/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.
Collapse
Affiliation(s)
- Masayo Oishi
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan.
| | - Hiroyuki Sayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Takeshi Nakayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Yasuhisa Nagasaka
- Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| |
Collapse
|
298
|
Mao S, Lin Y, Qin X, Miao Y, Luo C, Luo C, Wang J, Huang X, Zhu H, Lai J, Chen J. Droplet digital PCR: An effective method for monitoring and prognostic evaluation of minimal residual disease in JMML. Br J Haematol 2024; 204:2332-2341. [PMID: 38622924 DOI: 10.1111/bjh.19465] [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: 01/17/2024] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024]
Abstract
Juvenile myelomonocytic leukaemia (JMML) is a rare myeloproliferative neoplasm requiring haematopoietic stem cell transplantation (HSCT) for potential cure. Relapse poses a significant obstacle to JMML HSCT treatment, as the lack of effective minimal residual disease (MRD)-monitoring methods leads to delayed interventions. This retrospective study utilized the droplet digital PCR (ddPCR) technique, a highly sensitive nucleic acid detection and quantification technique, to monitor MRD in 32 JMML patients. The results demonstrated that ddPCR detected relapse manifestations earlier than traditional methods and uncovered molecular insights into JMML MRD dynamics. The findings emphasized a critical 1- to 3-month window post-HSCT for detecting molecular relapse, with 66.7% (8/12) of relapses occurring within this period. Slow MRD clearance post-HSCT was observed, as 65% (13/20) of non-relapse patients took over 6 months to achieve ddPCR-MRD negativity. Furthermore, bone marrow ddPCR-MRD levels at 1-month post-HSCT proved to be prognostically significant. Relapsed patients exhibited significantly elevated ddPCR-MRD levels at this time point (p = 0.026), with a cut-off of 0.465% effectively stratifying overall survival (p = 0.007), event-free survival (p = 0.035) and cumulative incidence of relapse (p = 0.035). In conclusion, this study underscored ddPCR's superiority in JMML MRD monitoring post-HSCT. It provided valuable insights into JMML MRD dynamics, offering guidance for the effective management of JMML.
Collapse
Affiliation(s)
- Shengqiao Mao
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuchen Lin
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xia Qin
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Miao
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Changying Luo
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chengjuan Luo
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jianmin Wang
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaohang Huang
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hua Zhu
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Junchen Lai
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Chen
- Department of Hematology and Oncology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
299
|
John AJ, Ghose ET, Gao H, Luck M, Jeong D, Kalari KR, Wang L. ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery. Front Oncol 2024; 14:1343091. [PMID: 38884087 PMCID: PMC11176476 DOI: 10.3389/fonc.2024.1343091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/18/2024] [Indexed: 06/18/2024] Open
Abstract
Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.
Collapse
Affiliation(s)
- August J John
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Emily T Ghose
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Huanyao Gao
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Meagan Luck
- Department of Biological Sciences, University of Notre Dame, South Bend, IN, United States
| | - Dabin Jeong
- Biochemistry Department, Lawrence University, Appleton, WI, United States
| | - Krishna R Kalari
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| |
Collapse
|
300
|
Dreyer A, Masanta WO, Lugert R, Bohne W, Groß U, Leha A, Dakna M, Lenz C, Zautner AE. Proteome profiling of Campylobacter jejuni 81-176 at 37 °C and 42 °C by label-free mass spectrometry. BMC Microbiol 2024; 24:191. [PMID: 38822261 PMCID: PMC11140963 DOI: 10.1186/s12866-024-03348-8] [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: 04/29/2023] [Accepted: 05/22/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND The main natural reservoir for Campylobacter jejuni is the avian intestinal tract. There, C. jejuni multiplies optimally at 42 °C - the avian body temperature. After infecting humans through oral intake, the bacterium encounters the lower temperature of 37 °C in the human intestinal tract. Proteome profiling by label-free mass spectrometry (DIA-MS) was performed to examine the processes which enable C. jejuni 81-176 to thrive at 37 °C in comparison to 42 °C. In total, four states were compared with each other: incubation for 12 h at 37 °C, for 24 h at 37 °C, for 12 h at 42 °C and 24 h at 42 °C. RESULTS It was shown that the proteomic changes not only according to the different incubation temperature but also to the length of the incubation period were evident when comparing 37 °C and 42 °C as well as 12 h and 24 h of incubation. Altogether, the expression of 957 proteins was quantifiable. 37.1 - 47.3% of the proteins analyzed showed significant differential regulation, with at least a 1.5-fold change in either direction (i.e. log2 FC ≥ 0.585 or log2 FC ≤ -0.585) and an FDR-adjusted p-value of less than 0.05. The significantly differentially expressed proteins could be arranged in 4 different clusters and 16 functional categories. CONCLUSIONS The C. jejuni proteome at 42 °C is better adapted to high replication rates than that at 37 °C, which was in particular indicated by the up-regulation of proteins belonging to the functional categories "replication" (e.g. Obg, ParABS, and NapL), "DNA synthesis and repair factors" (e.g. DNA-polymerase III, DnaB, and DnaE), "lipid and carbohydrate biosynthesis" (e.g. capsular biosynthesis sugar kinase, PrsA, AccA, and AccP) and "vitamin synthesis, metabolism, cofactor biosynthesis" (e.g. MobB, BioA, and ThiE). The relative up-regulation of proteins with chaperone function (GroL, DnaK, ClpB, HslU, GroS, DnaJ, DnaJ-1, and NapD) at 37 °C in comparison to 42 °C after 12 h incubation indicates a temporary lower-temperature proteomic response. Additionally the up-regulation of factors for DNA uptake (ComEA and RecA) at 37 °C compared to 42 °C indicate a higher competence for the acquisition of extraneous DNA at human body temperature.
Collapse
Affiliation(s)
- Annika Dreyer
- Institute for Medical Microbiology and Virology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Wycliffe O Masanta
- Institute for Medical Microbiology and Virology, University Medical Center Göttingen, 37075, Göttingen, Germany
- Department of Medical Microbiology, Maseno University Medical School, Private Bag, Maseno, 40105, Kenya
| | - Raimond Lugert
- Institute for Medical Microbiology and Virology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Wolfgang Bohne
- Institute for Medical Microbiology and Virology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Uwe Groß
- Institute for Medical Microbiology and Virology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Andreas Leha
- Department of Medical Statistics, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Mohammed Dakna
- Department of Medical Statistics, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Christof Lenz
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
- Institute of Clinical Chemistry, Bioanalytics, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Andreas E Zautner
- Institute for Medical Microbiology and Virology, University Medical Center Göttingen, 37075, Göttingen, Germany.
- Institute of Medical Microbiology and Hospital Hygiene, Medical Faculty, Otto-von-Guericke-University Magdeburg, Leipziger Straße 44, 39120, Magdeburg, Germany.
- CHaMP, Center for Health and Medical Prevention, Otto-von-Guericke-University Magdeburg, 39120, Magdeburg, Germany.
| |
Collapse
|