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Razgonova MP, Nawaz MA, Sabitov AS, Golokhvast KS. Genus Ribes: Ribes aureum, Ribes pauciflorum, Ribes triste, and Ribes dikuscha-Comparative Mass Spectrometric Study of Polyphenolic Composition and Other Bioactive Constituents. Int J Mol Sci 2024; 25:10085. [PMID: 39337572 PMCID: PMC11432568 DOI: 10.3390/ijms251810085] [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: 07/22/2024] [Revised: 09/15/2024] [Accepted: 09/16/2024] [Indexed: 09/30/2024] Open
Abstract
This study presents the metabolomic profiles of the four Ribes species (Ribes pauciflorum Turcz., Ribes triste Pall., Ribes dicuscha Fisch., and Ribes aureum Purch.). The plant material was collected during two expeditions in the Russian Far East. Tandem mass spectrometry was used to detect target analytes. A total of 205 bioactive compounds (155 compounds from polyphenol group and 50 compounds from other chemical groups) were tentatively identified from the berries and extracts of the four Ribes species. For the first time, 29 chemical constituents from the polyphenol group were tentatively identified in the genus Ribes. The newly identified polyphenols include flavones, flavonols, flavan-3-ols, lignans, coumarins, stilbenes, and others. The other newly detected compounds in Ribes species are the naphthoquinone group (1,8-dihydroxy-anthraquinone, 1,3,6,8-tetrahydroxy-9(10H)-anthracenone, 8,8'-dihydroxy-2,2'-binaphthalene-1,1',4,4'-tetrone, etc.), polyhydroxycarboxylic acids, omega-3 fatty acids (stearidonic acid, linolenic acid), and others. Our results imply that Ribes species are rich in polyphenols, especially flavanols, anthocyanins, flavones, and flavan-3-ols. These results indicate the utility of Ribes species for the health and pharmaceutical industry.
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Affiliation(s)
- Mayya P. Razgonova
- N.I. Vavilov All-Russian Institute of Plant Genetic Resources, B. Morskaya 42-44, Saint-Petersburg 190000, Russia; (A.S.S.); (K.S.G.)
- Advanced Engineering School, Far Eastern Federal University, Sukhanova 8, Vladivostok 690950, Russia
| | - Muhammad Amjad Nawaz
- Advanced Engineering School (Agrobiotek), National Research Tomsk State University, Lenin Ave, 36, Tomsk 634050, Russia
- Center for Research in the Field of Materials and Technologies, Tomsk State University, Lenin Ave, 36, Tomsk 634050, Russia
| | - Andrey S. Sabitov
- N.I. Vavilov All-Russian Institute of Plant Genetic Resources, B. Morskaya 42-44, Saint-Petersburg 190000, Russia; (A.S.S.); (K.S.G.)
| | - Kirill S. Golokhvast
- N.I. Vavilov All-Russian Institute of Plant Genetic Resources, B. Morskaya 42-44, Saint-Petersburg 190000, Russia; (A.S.S.); (K.S.G.)
- Advanced Engineering School (Agrobiotek), National Research Tomsk State University, Lenin Ave, 36, Tomsk 634050, Russia
- Siberian Federal Scientific Centre of Agrobiotechnology RAS, Centralnaya 2b, Presidium, Krasnoobsk 633501, Russia
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Wang A, Zhou J, Zhang P, Cao H, Xin H, Xu X, Zhou H. Large language model answers medical questions about standard pathology reports. Front Med (Lausanne) 2024; 11:1402457. [PMID: 39359921 PMCID: PMC11445125 DOI: 10.3389/fmed.2024.1402457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 08/28/2024] [Indexed: 10/04/2024] Open
Abstract
This study aims to evaluate the feasibility of large language model (LLM) in answering pathology questions based on pathology reports (PRs) of colorectal cancer (CRC). Four common questions (CQs) and corresponding answers about pathology were retrieved from public webpages. These questions were input as prompts for Chat Generative Pretrained Transformer (ChatGPT) (gpt-3.5-turbo). The quality indicators (understanding, scientificity, satisfaction) of all answers were evaluated by gastroenterologists. Standard PRs from 5 CRC patients who received radical surgeries in Shanghai Changzheng Hospital were selected. Six report questions (RQs) and corresponding answers were generated by a gastroenterologist and a pathologist. We developed an interactive PRs interpretation system which allows users to upload standard PRs as JPG images. Then the ChatGPT's responses to the RQs were generated. The quality indicators of all answers were evaluated by gastroenterologists and out-patients. As for CQs, gastroenterologists rated AI answers similarly to non-AI answers in understanding, scientificity, and satisfaction. As for RQ1-3, gastroenterologists and patients rated the AI mean scores higher than non-AI scores among the quality indicators. However, as for RQ4-6, gastroenterologists rated the AI mean scores lower than non-AI scores in understanding and satisfaction. In RQ4, gastroenterologists rated the AI scores lower than non-AI scores in scientificity (P = 0.011); patients rated the AI scores lower than non-AI scores in understanding (P = 0.004) and satisfaction (P = 0.011). In conclusion, LLM could generate credible answers to common pathology questions and conceptual questions on the PRs. It holds great potential in improving doctor-patient communication.
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Affiliation(s)
- Anqi Wang
- Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Jieli Zhou
- UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Peng Zhang
- Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Haotian Cao
- Department of Pathology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Hongyi Xin
- UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyun Xu
- Division of Breast and Thyroid Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Haiyang Zhou
- Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
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3
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Koesterich J, Liu J, Williams SE, Yang N, Kreimer A. Network Analysis of Enhancer-Promoter Interactions Highlights Cell-Type-Specific Mechanisms of Transcriptional Regulation Variation. Int J Mol Sci 2024; 25:9840. [PMID: 39337329 PMCID: PMC11432627 DOI: 10.3390/ijms25189840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 09/08/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we generated enhancer-promoter interaction (EPI) networks and investigated the presence of disease-associated variants that fall within these regions. Our study analyzed six neuronal cell types across neural differentiation, allowing us to examine closely related cell types and across differentiation stages. Our results expand upon previous findings of cell-type specificity of enhancer, promoter, and transcription factor binding sites. Notably, we find that regulatory regions within EPI networks can identify the enrichment of variants associated with neuropsychiatric disorders within specific cell types and network sub-structures. This enrichment within sub-structures can allow for a better understanding of potential mechanisms by which variants may disrupt transcription. Together, our findings suggest that EPIs can be leveraged to better understand cell-type-specific regulatory architecture and used as a selection method for disease-associated variants to be tested in future functional assays. Combined with these future functional characterization assays, EPIs can be used to better identify and characterize regulatory variants' effects on such networks and model their mechanisms of gene regulation disruption across different disorders. Such findings can be applied in practical settings, such as diagnostic tools and drug development.
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Affiliation(s)
- Justin Koesterich
- Graduate Programs in Molecular Biosciences, Rutgers The State University of New Jersey, 604 Allison Rd., Piscataway, NJ 08854, USA
- Department of Biochemistry and Molecular Biology, Rutgers The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854, USA
- Center for Advanced Biotechnology and Medicine, Rutgers The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ 08854, USA
| | - Jiayi Liu
- Graduate Programs in Molecular Biosciences, Rutgers The State University of New Jersey, 604 Allison Rd., Piscataway, NJ 08854, USA
- Department of Biochemistry and Molecular Biology, Rutgers The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854, USA
- Center for Advanced Biotechnology and Medicine, Rutgers The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ 08854, USA
| | - Sarah E Williams
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Institute of Regenerative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Alper Center for Neurodevelopment and Regeneration, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nan Yang
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Institute of Regenerative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Alper Center for Neurodevelopment and Regeneration, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anat Kreimer
- Graduate Programs in Molecular Biosciences, Rutgers The State University of New Jersey, 604 Allison Rd., Piscataway, NJ 08854, USA
- Department of Biochemistry and Molecular Biology, Rutgers The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854, USA
- Center for Advanced Biotechnology and Medicine, Rutgers The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ 08854, USA
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Fainberg HP, Moodley Y, Triguero I, Corte TJ, Sand JMB, Leeming DJ, Karsdal MA, Wells AU, Renzoni E, Mackintosh J, Tan DBA, Li R, Porte J, Braybrooke R, Saini G, Johnson SR, Wain LV, Molyneaux PL, Maher TM, Stewart ID, Jenkins RG. Cluster analysis of blood biomarkers to identify molecular patterns in pulmonary fibrosis: assessment of a multicentre, prospective, observational cohort with independent validation. THE LANCET. RESPIRATORY MEDICINE 2024; 12:681-692. [PMID: 39025091 DOI: 10.1016/s2213-2600(24)00147-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 04/10/2024] [Accepted: 05/01/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND Pulmonary fibrosis results from alveolar injury, leading to extracellular matrix remodelling and impaired lung function. This study aimed to classify patients with pulmonary fibrosis according to blood biomarkers to differentiate distinct disease patterns, known as endotypes. METHODS In this cluster analysis, we first classified patients from the PROFILE study, a multicentre, prospective, observational cohort of individuals with incident idiopathic pulmonary fibrosis or non-specific interstitial pneumonia in the UK (Nottingham University Hospitals, Nottingham; and Royal Brompton Hospital, London). 13 blood biomarkers representing extracellular matrix remodelling, epithelial stress, and thrombosis were measured by ELISA in the PROFILE study. We classified patients by unsupervised consensus clustering. To evaluate generalisability, a machine learning classifier trained on biomarker signatures derived from consensus clustering was applied to a replication dataset from the Australian Idiopathic Pulmonary Fibrosis Registry (AIPFR). Biomarker associations with mortality and change in percentage of predicted forced vital capacity (FVC%) were assessed, adjusting for age, gender, baseline FVC%, and antifibrotic treatment and steroid treatment before and after baseline. Mortality risk associated with the clusters in the PROFILE cohort was evaluated with Cox proportional hazards models, and mixed-effects models were used to analyse how clustering was associated with longitudinal FVC% in the PROFILE and AIPFR cohorts. FINDINGS 455 of 580 participants from the PROFILE study (348 [76%] men and 107 [24%] women; mean age 72·4 years [SD 8·3]) were included in the analysis. Within this group, three clusters were identified based on blood biomarkers. A basement membrane collagen (BM) cluster (n=248 [55%]) showed high concentrations of PRO-C4, PRO-C28, C3M, and C6M, whereas an epithelial injury (EI) cluster (n=109 [24%]) showed high concentrations of MMP-7, SP-D, CYFRA211, CA19-9, and CA-125. The third cluster (crosslinked fibrin [XF] cluster; n=98 [22%]) had high concentrations of X-FIB. In the replication dataset (117 of 833 patients from AIPFR; 87 [74%] men and 30 [26%] women; mean age 72·9 years [SD 7·9]), we identified the same three clusters (BM cluster, n=93 [79%]; EI cluster, n=8 [7%]; XF cluster, n=16 [14%]). These clusters showed similarities with clusters in the PROFILE dataset regarding blood biomarkers and phenotypic signatures. In the PROFILE dataset, the EI and XF clusters were associated with increased mortality risk compared with the BM cluster (EI vs BM: adjusted hazard ratio [HR] 1·88 [95% CI 1·42-2·49], p<0·0001; XF vs BM: adjusted HR 1·53 [1·13-2·06], p=0·0058). The EI cluster showed the greatest annual FVC% decline, followed by the BM and XF clusters. A similar FVC% decline pattern was observed in these clusters in the AIPFR replication dataset. INTERPRETATION Blood biomarker clustering in pulmonary fibrosis identified three distinct blood biomarker signatures associated with lung function and prognosis, suggesting unique pulmonary fibrosis biomarker patterns. These findings support the presence of pulmonary fibrosis endotypes with the potential to guide targeted therapy development. FUNDING None.
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Affiliation(s)
- Hernan P Fainberg
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK.
| | - Yuben Moodley
- NHMRC Centre of Research Excellence in Pulmonary Fibrosis, Camperdown, NSW, Australia; Centre for Respiratory Health, School of Biomedical Sciences, University of Western Australia, Nedlands, WA, Australia; Cell Biology Group, Institute for Respiratory Health, Nedlands, WA, Australia; Department of Respiratory Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Isaac Triguero
- Department of Computer Science and Artificial Intelligence, DaSCI Andalusian Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain; School of Computer Science, University of Nottingham, Nottingham, UK
| | - Tamera J Corte
- Royal Prince Alfred Hospital, Camperdown, NSW, Australia; The University of Sydney Central Clinical School, Camperdown, NSW, Australia
| | - Jannie M B Sand
- Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Diana J Leeming
- Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Morten A Karsdal
- Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Athol U Wells
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK; Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Elisabetta Renzoni
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK; Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - John Mackintosh
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Dino B A Tan
- NHMRC Centre of Research Excellence in Pulmonary Fibrosis, Camperdown, NSW, Australia; Centre for Respiratory Health, School of Biomedical Sciences, University of Western Australia, Nedlands, WA, Australia; Cell Biology Group, Institute for Respiratory Health, Nedlands, WA, Australia
| | - Roger Li
- NHMRC Centre of Research Excellence in Pulmonary Fibrosis, Camperdown, NSW, Australia; Centre for Respiratory Health, School of Biomedical Sciences, University of Western Australia, Nedlands, WA, Australia; Cell Biology Group, Institute for Respiratory Health, Nedlands, WA, Australia
| | - Joanne Porte
- Centre for Respiratory Research and NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Rebecca Braybrooke
- Centre for Respiratory Research and NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Gauri Saini
- Centre for Respiratory Research and NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Simon R Johnson
- Centre for Respiratory Research and NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Louise V Wain
- Department of Population Health Sciences, University of Leicester, Leicester, UK; NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Philip L Molyneaux
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK; Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Toby M Maher
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK; Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Iain D Stewart
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK
| | - R Gisli Jenkins
- NIHR Imperial Biomedical Respiratory Research Centre, National Heart and Lung Institute, Imperial College London, London, UK; Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
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5
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Roe HM, Tsai HHD, Ball N, Wright FA, Chiu WA, Rusyn I. A Systematic Analysis of Read-Across Adaptations in Testing Proposal Evaluations by the European Chemicals Agency. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.29.610278. [PMID: 39257792 PMCID: PMC11384022 DOI: 10.1101/2024.08.29.610278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
An important element of the European Union's "Registration, Evaluation, Authorisation and Restriction of Chemicals" (REACH) regulation is the evaluation by the European Chemicals Agency (ECHA) of testing proposals submitted by the registrants to address data gaps in standard REACH information requirements. The registrants may propose adaptations, and ECHA evaluates the reasoning and issues a written decision. Read-across is a common adaptation type, yet it is widely assumed that ECHA often does not agree that the justifications are adequate to waive standard testing requirements. From 2008 to August 2023, a total of 2,630 Testing Proposals were submitted to ECHA; of these, 1,538 had published decisions that were systematically evaluated in this study. Each document was manually reviewed, and information extracted for further analyses. Read-across hypotheses were standardized into 17 assessment elements (AEs); each submission was classified as to the AEs relied upon by the registrants and by ECHA. Data was analyzed for patterns and associations. Testing Proposal Evaluations (TPEs) with adaptations comprised 23% (353) of the total; analogue (168) or group (136) read-across adaptations were most common. Of 304 read-across-containing TPEs, 49% were accepted; the odds of acceptance were significantly greater for group read-across submissions. The data was analyzed by Annex (i.e., tonnage), test guideline study, read-across hypothesis AEs, as well as target and source substance types and their structural similarity. While most ECHA decisions with both positive and negative decisions on whether the proposed read-across was adequate were context-specific, a number of significant associations were identified that influence the odds of acceptance. Overall, this analysis provides an unbiased overview of 15 years of experience with testing proposal-specific read-across adaptations by both registrants and ECHA. These data will inform future submissions as they identify most critical AEs to increase the odds of read-across acceptance.
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Affiliation(s)
- Hannah M Roe
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Han-Hsuan D Tsai
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | | | - Fred A Wright
- Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Weihsueh A Chiu
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Ivan Rusyn
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
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6
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Wang N, Wu X, Liang J, Liu B, Wang B. Molecular design of hydroxamic acid-based derivatives as urease inhibitors of Helicobacter pylori. Mol Divers 2024; 28:2229-2244. [PMID: 39020133 DOI: 10.1007/s11030-024-10914-9] [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: 04/27/2024] [Accepted: 06/08/2024] [Indexed: 07/19/2024]
Abstract
Helicobacter pylori is the main causative agent of gastric cancer, especially non-cardiac gastric cancers. This bacterium relies on urease producing much ammonia to colonize the host. Herein, the study provides valuable insights into structural patterns driving urease inhibition for high-activity molecules designed via exploring known inhibitors. Firstly, an ensemble model was devised to predict the inhibitory activity of novel compounds in an automated workflow (R2 = 0.761) that combines four machine learning approaches. The dataset was characterized in terms of chemical space, including molecular scaffolds, clustering analysis, distribution for physicochemical properties, and activity cliffs. Through these analyses, the hydroxamic acid group and the benzene ring responsible for distinct activity were highlighted. Activity cliff pairs uncovered substituents of the benzene ring on hydroxamic acid derivatives are key structures for substantial activity enhancement. Moreover, 11 hydroxamic acid derivatives were designed, named mol1-11. Results of molecular dynamic simulations showed that the mol9 exhibited stabilization of the active site flap's closed conformation and are expected to be promising drug candidates for Helicobacter pylori infection and further in vitro, in vivo, and clinical trials to demonstrate in future.
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Affiliation(s)
- Na Wang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Xiaoyan Wu
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Jianhuai Liang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China
| | - Boping Liu
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.
| | - Bingfeng Wang
- College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.
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Evans C, Mogg SL, Soraru C, Wallington E, Coates J, Borrill P. Wheat NAC transcription factor NAC5-1 is a positive regulator of senescence. PLANT DIRECT 2024; 8:e620. [PMID: 38962173 PMCID: PMC11217990 DOI: 10.1002/pld3.620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/04/2024] [Accepted: 06/10/2024] [Indexed: 07/05/2024]
Abstract
Wheat (Triticum aestivum L.) is an important source of both calories and protein in global diets, but there is a trade-off between grain yield and protein content. The timing of leaf senescence could mediate this trade-off as it is associated with both declines in photosynthesis and nitrogen remobilization from leaves to grain. NAC transcription factors play key roles in regulating senescence timing. In rice, OsNAC5 expression is correlated with increased protein content and upregulated in senescing leaves, but the role of the wheat ortholog in senescence had not been characterized. We verified that NAC5-1 is the ortholog of OsNAC5 and that it is expressed in senescing flag leaves in wheat. To characterize NAC5-1, we combined missense mutations in NAC5-A1 and NAC5-B1 from a TILLING mutant population and overexpressed NAC5-A1 in wheat. Mutation in NAC5-1 was associated with delayed onset of flag leaf senescence, while overexpression of NAC5-A1 was associated with slightly earlier onset of leaf senescence. DAP-seq was performed to locate transcription factor binding sites of NAC5-1. Analysis of DAP-seq and comparison with other studies identified putative downstream target genes of NAC5-1 which could be associated with senescence. This work showed that NAC5-1 is a positive transcriptional regulator of leaf senescence in wheat. Further research is needed to test the effect of NAC5-1 on yield and protein content in field trials, to assess the potential to exploit this senescence regulator to develop high-yielding wheat while maintaining grain protein content.
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Affiliation(s)
- Catherine Evans
- Department of Crop GeneticsJohn Innes CentreNorwichUK
- School of BiosciencesUniversity of BirminghamBirminghamUK
| | | | | | | | - Juliet Coates
- School of BiosciencesUniversity of BirminghamBirminghamUK
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8
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Yang Q, Fan L, Hao E, Hou X, Deng J, Du Z, Xia Z. Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides. Drug Chem Toxicol 2024:1-13. [PMID: 38938098 DOI: 10.1080/01480545.2024.2364905] [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: 01/18/2024] [Accepted: 06/01/2024] [Indexed: 06/29/2024]
Abstract
It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.
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Affiliation(s)
- Qi Yang
- School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
| | - Lili Fan
- School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
| | - Erwei Hao
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Xiaotao Hou
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Jiagang Deng
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhengcai Du
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Scientific Research Center of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhongshang Xia
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
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9
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Chapple RH, Liu X, Natarajan S, Alexander MIM, Kim Y, Patel AG, LaFlamme CW, Pan M, Wright WC, Lee HM, Zhang Y, Lu M, Koo SC, Long C, Harper J, Savage C, Johnson MD, Confer T, Akers WJ, Dyer MA, Sheppard H, Easton J, Geeleher P. An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs. Genome Biol 2024; 25:161. [PMID: 38898465 PMCID: PMC11186099 DOI: 10.1186/s13059-024-03309-4] [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: 11/26/2023] [Accepted: 06/14/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. RESULTS Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. CONCLUSIONS Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.
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Affiliation(s)
- Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xueying Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Sivaraman Natarajan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Margaret I M Alexander
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yuna Kim
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Anand G Patel
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Christy W LaFlamme
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Graduate School of Biomedical Sciences, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Min Pan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - William C Wright
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Hyeong-Min Lee
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yinwen Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Meifen Lu
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Selene C Koo
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Courtney Long
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Harper
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Chandra Savage
- Animal Resources Center, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Melissa D Johnson
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Thomas Confer
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Walter J Akers
- Department of Biomedical Engineering, University of Texas Southwestern Medical School, Dallas, TX, 75390, USA
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, 20815, USA
| | - Heather Sheppard
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
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10
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Juárez-Mercado KE, Avellaneda-Tamayo JF, Villegas-Quintero H, Chávez-Hernández AL, López-López CD, Medina-Franco JL. Food Chemicals and Epigenetic Targets: An Epi Food Chemical Database. ACS OMEGA 2024; 9:25322-25331. [PMID: 38882162 PMCID: PMC11170626 DOI: 10.1021/acsomega.4c03321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 06/18/2024]
Abstract
There is increasing awareness of epigenetics's importance in understanding disease etiologies and developing novel therapeutics. An increasing number of publications in the past few years reflect the renewed interest in epigenetic processes and their relationship with food chemicals. However, there needs to be a recent study that accounts for the most recent advances in the area by associating the chemical structures of food and natural product components with their biological activity. Here, we analyze the status of food chemicals and their intersection with natural products in epigenetic research. Using chemoinformatics tools, we compared quantitatively the chemical contents, structural diversity, and coverage in the chemical space of food chemicals with reported epigenetic activity. As part of this work, we built and curated a compound database of food and natural product chemicals annotated with structural information, an epigenetic target activity profile, and the main source of the food chemical or natural product, among other relevant features. The compounds are cross-linked with identifiers from other major public databases such as FooDB and the collection of open natural products, COCONUT. The compound database, the "Epi Food Chemical Database", is accessible in HTML and CSV formats at https://github.com/DIFACQUIM/Epi_food_Chemical_Database.
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Affiliation(s)
- K Eurídice Juárez-Mercado
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Juan F Avellaneda-Tamayo
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Hassan Villegas-Quintero
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Ana L Chávez-Hernández
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | | | - José L Medina-Franco
- DIFACQUIM Research Group. Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
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11
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Liu H, Chen P, Hu B, Wang S, Wang H, Luan J, Wang J, Lin B, Cheng M. FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening. Comput Biol Chem 2024; 110:108057. [PMID: 38581840 DOI: 10.1016/j.compbiolchem.2024.108057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/06/2024] [Accepted: 03/20/2024] [Indexed: 04/08/2024]
Abstract
Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.
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Affiliation(s)
- Haihan Liu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Peiying Chen
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Shizun Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Hanxun Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jiasi Luan
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Medical Devices, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Bin Lin
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Maosheng Cheng
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
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12
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Mehta VS, Ma Y, Wijesuriya N, DeVere F, Howell S, Elliott MK, Mannkakara NN, Hamakarim T, Wong T, O'Brien H, Niederer S, Razavi R, Rinaldi CA. Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models. Heart Rhythm 2024; 21:919-928. [PMID: 38354872 DOI: 10.1016/j.hrthm.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). OBJECTIVE The purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). METHODS We hypothesized certain features-(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC-detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features. RESULTS A total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74-0.87), sensitivity (68%-83%), specificity (72%-91%), and area under the curve (AUC) (0.767-0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76-0.86), sensitivity (75%-85%), specificity (63%-87%), and AUC (0.684-0.913). CONCLUSION Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.
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Affiliation(s)
- Vishal S Mehta
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - YingLiang Ma
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; School of Computing Sciences, University of East Anglia, Norwich, United Kingdom
| | - Nadeev Wijesuriya
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Felicity DeVere
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sandra Howell
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark K Elliott
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nilanka N Mannkakara
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Tatiana Hamakarim
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Tom Wong
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Hugh O'Brien
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Reza Razavi
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Heart Vascular & Thoracic Institute, Cleveland Clinic London, London, United Kingdom
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13
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Kikuchi T, Iga J, Oosawa M, Hoshino T, Moriguchi Y, Izutsu M. A web-based survey on the occurrence of emotional blunting in patients with major depressive disorder in Japan: Patient perceptions and attitudes. Neuropsychopharmacol Rep 2024; 44:321-332. [PMID: 38616339 PMCID: PMC11144621 DOI: 10.1002/npr2.12417] [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: 04/21/2023] [Revised: 12/05/2023] [Accepted: 01/15/2024] [Indexed: 04/16/2024] Open
Abstract
AIMS To determine the prevalence and impact of emotional blunting (EB) in patients with major depressive disorder (MDD) in Japan, and identify treatment needs for EB using patients' perceptions and attitudes. METHODS Eligible patients in Japan (aged 18-59 years) who reported a diagnosis of MDD and antidepressant medication use for >3 months were eligible to complete an online survey. The primary outcome was the prevalence of EB, self-reported using a validated screening question. Secondary outcomes included the correlation between EB symptoms (measured by the Oxford Depression Questionnaire [ODQ]) and scores on the Patient Health Questionnaire 9-item (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), Work and Social Adjustment Scale (WSAS), and the EuroQol 5-Dimension 5-Levels questionnaire (EQ-5D-5L). Descriptive questions were used to explore patients' perceptions and attitudes toward EB. RESULTS In total, 3376 patients were included in the analysis (56% male; 48% aged 50-59 years). Overall, 67.1% of patients self-reported symptoms of EB, with 10% rating these as severe. The mean (SD) ODQ total score was 78.2 (21.5), which increased with worsening EB symptoms. There were correlations between ODQ total scores and the PHQ-9, GAD-7, WSAS, and EQ-5D-5L scores (correlation coefficients: 0.67, 0.55, 0.56, -0.51, respectively; all p < 0.0001). Descriptive analyses showed that one-third of patients reporting EB symptoms did not tell their physician, with two-thirds finding these symptoms distressing and likely to affect recovery. CONCLUSION EB is an important clinical issue in Japan that needs to be considered alongside functional recovery when managing treatment of patients with MDD.
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Affiliation(s)
- Toshiaki Kikuchi
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Jun‐ichi Iga
- Department of NeuropsychiatryEhime University Graduate School of MedicineToonEhimeJapan
| | - Masato Oosawa
- Japan Medical OfficeTakeda Pharmaceutical Company LimitedTokyoJapan
| | - Tatsuya Hoshino
- Japan Medical OfficeTakeda Pharmaceutical Company LimitedTokyoJapan
| | | | - Miwa Izutsu
- Japan Medical OfficeTakeda Pharmaceutical Company LimitedTokyoJapan
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14
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Rask GC, Taslim C, Bayanjargal A, Cannon MV, Selich-Anderson J, Crow JC, Duncan A, Theisen ER. Seclidemstat blocks the transcriptional function of multiple FET-fusion oncoproteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.19.594897. [PMID: 38826330 PMCID: PMC11142045 DOI: 10.1101/2024.05.19.594897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Genes encoding the RNA-binding proteins FUS, EWSR1, and TAF15 (FET proteins) are involved in chromosomal translocations in rare sarcomas. FET-rearranged sarcomas are often aggressive malignancies affecting patients of all ages. New therapies are needed. These translocations fuse the 5' portion of the FET gene with a 3' partner gene encoding a transcription factor (TF). The resulting fusion proteins are oncogenic TFs with a FET protein low complexity domain (LCD) and a DNA binding domain. FET fusion proteins have proven stubbornly difficult to target directly and promising strategies target critical co-regulators. One candidate is lysine specific demethylase 1 (LSD1). LSD1 is recruited by multiple FET fusions, including EWSR1::FLI1. LSD1 promotes EWSR1::FLI1 activity and treatment with the noncompetitive inhibitor SP-2509 blocks EWSR1::FLI1 transcriptional function. A similar molecule, seclidemstat (SP-2577), is currently in clinical trials for FET-rearranged sarcomas (NCT03600649). However, whether seclidemstat has pharmacological activity against FET fusions has not been demonstrated. Here, we evaluate the in vitro potency of seclidemstat against multiple FET-rearranged sarcoma cell lines, including Ewing sarcoma, desmoplastic small round cell tumor, clear cell sarcoma, and myxoid liposarcoma. We also define the transcriptomic effects of seclidemstat treatment and evaluated the activity of seclidemstat against FET fusion transcriptional regulation. Seclidemstat showed potent activity in cell viability assays across FET-rearranged sarcomas and disrupted the transcriptional function of all tested fusions. Though epigenetic and targeted inhibitors are unlikely to be effective as a single agents in the clinic, these data suggest seclidemstat remains a promising new treatment strategy for patients with FET-rearranged sarcomas.
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Affiliation(s)
- Galen C. Rask
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Cenny Taslim
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Ariunaa Bayanjargal
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
- Medical Scientist Training Program, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Matthew V. Cannon
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Julia Selich-Anderson
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Jesse C. Crow
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | | | - Emily R. Theisen
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
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15
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Stock M, Popp N, Fiorentino J, Scialdone A. Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data. Bioinformatics 2024; 40:btae267. [PMID: 38627250 PMCID: PMC11096270 DOI: 10.1093/bioinformatics/btae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/28/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. RESULTS To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. AVAILABILITY AND IMPLEMENTATION STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.
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Affiliation(s)
- Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 85354, Germany
| | - Niclas Popp
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Jonathan Fiorentino
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
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16
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Geistlinger L, Mirzayi C, Zohra F, Azhar R, Elsafoury S, Grieve C, Wokaty J, Gamboa-Tuz SD, Sengupta P, Hecht I, Ravikrishnan A, Gonçalves RS, Franzosa E, Raman K, Carey V, Dowd JB, Jones HE, Davis S, Segata N, Huttenhower C, Waldron L. BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures. Nat Biotechnol 2024; 42:790-802. [PMID: 37697152 PMCID: PMC11098749 DOI: 10.1038/s41587-023-01872-y] [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: 10/24/2022] [Accepted: 06/20/2023] [Indexed: 09/13/2023]
Abstract
The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.
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Affiliation(s)
- Ludwig Geistlinger
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
| | - Chloe Mirzayi
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Fatima Zohra
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Rimsha Azhar
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Shaimaa Elsafoury
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Clare Grieve
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Jennifer Wokaty
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Samuel David Gamboa-Tuz
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Pratyay Sengupta
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology (IIT) Madras, Chennai, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India
| | | | - Aarthi Ravikrishnan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Rafael S Gonçalves
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
| | - Eric Franzosa
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology (IIT) Madras, Chennai, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Vincent Carey
- Channing Division of Network Medicine, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Jennifer B Dowd
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Heidi E Jones
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA
| | - Sean Davis
- Departments of Biomedical Informatics and Medicine, University of Colorado Anschutz School of Medicine, Denver, CO, USA
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy
- Istituto Europeo di Oncologia (IEO) IRCSS, Milan, Italy
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Levi Waldron
- Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA.
- Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, USA.
- Department CIBIO, University of Trento, Trento, Italy.
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Tran XTD, Phan TL, To VT, Tran NVN, Nguyen NNS, Nguyen DNH, Tran NTN, Truong TN. Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists. Front Chem 2024; 12:1382319. [PMID: 38690013 PMCID: PMC11058650 DOI: 10.3389/fchem.2024.1382319] [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: 02/05/2024] [Accepted: 03/18/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.
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Affiliation(s)
- Xuan-Truc Dinh Tran
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Tieu-Long Phan
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Van-Thinh To
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | - Nhu-Ngoc Song Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Dong-Nghi Hoang Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Ngoc-Tam Nguyen Tran
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Tuyen Ngoc Truong
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
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18
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Fiorentino J, Armaos A, Colantoni A, Tartaglia G. Prediction of protein-RNA interactions from single-cell transcriptomic data. Nucleic Acids Res 2024; 52:e31. [PMID: 38364867 PMCID: PMC11014251 DOI: 10.1093/nar/gkae076] [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: 07/17/2023] [Revised: 01/12/2024] [Accepted: 01/26/2024] [Indexed: 02/18/2024] Open
Abstract
Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.
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Affiliation(s)
- Jonathan Fiorentino
- Center for Life Nano- and Neuro-Science, RNA Systems Biology Lab, Fondazione Istituto Italiano di Tecnologia (IIT), 00161 Rome, Italy
| | - Alexandros Armaos
- Centre for Human Technologies (CHT), RNA Systems Biology Lab, Fondazione Istituto Italiano di Tecnologia (IIT), 16152 Genova, Italy
| | - Alessio Colantoni
- Center for Life Nano- and Neuro-Science, RNA Systems Biology Lab, Fondazione Istituto Italiano di Tecnologia (IIT), 00161 Rome, Italy
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, 00185 Rome, Italy
| | - Gian Gaetano Tartaglia
- Center for Life Nano- and Neuro-Science, RNA Systems Biology Lab, Fondazione Istituto Italiano di Tecnologia (IIT), 00161 Rome, Italy
- Centre for Human Technologies (CHT), RNA Systems Biology Lab, Fondazione Istituto Italiano di Tecnologia (IIT), 16152 Genova, Italy
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19
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Li Y, Yang Y, Tong Z, Wang Y, Mi Q, Bai M, Liang G, Li B, Shu K. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Brief Bioinform 2024; 25:bbae172. [PMID: 38647153 PMCID: PMC11033846 DOI: 10.1093/bib/bbae172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.
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Affiliation(s)
- Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yinqi Yang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Zhuohao Tong
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yu Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Qin Mi
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, P. R. China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, P. R. China
| | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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20
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Imbaná R, Daniele de Almeida Valente F, Siqueira RG, Moquedace CM, Rodrigues de Assis I. Assessing the quality of constructed technosols enabled holistic monitoring of ecological restoration. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 353:120237. [PMID: 38310796 DOI: 10.1016/j.jenvman.2024.120237] [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: 11/01/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/06/2024]
Abstract
The soil quality index (SQI) serves as a general ecological restoration indicator, however, statistics approaches that accurately assess the minimum data set (MDS) for SQI remain susceptible. The present study aims to evaluate the short-term reclamation results at the Ferro-Carvão stream and propose a system for ecological restoration monitoring, by selecting influential attributes and indexing soil quality. We hypothesized that the reclamation activities at the Ferro-Carvão stream, referred to as the "Marco zero" (MZ) area, can bring its soil quality to levels comparable to those of the native area. We collected soil samples at 0-20 and 20-40 cm depths from transects of MZ and reference sites (R1 and R2). Principal component analysis showed the MDS for each soil depth. Permutational analysis of variance, in conjunction with Nonmetric Multidimensional Scaling, exposed relationships between transects of areas. An additive non-linear factorial algorithm allowed SQI assessment. The results indicated a similar soil quality between transects of areas at 0-20 cm depth, whereas a dissimilarity at 20-40 cm. To sum up, reclamation activities allowed MZ-constructed Technosol to present a soil quality similar to native areas. The soil quality assessment at both depths offered insights into reclamation activities' immediate and long-term impacts on the Ferro-Carvão stream. This robust framework effectively monitors ecological restoration progress and guides future efforts in post-mining and post-dam collapse sites.
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Affiliation(s)
- Rugana Imbaná
- Department of Soil Science and Plant Nutrition, Universidade Federal de Viçosa, R. Purdue S/N, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil.
| | - Fernanda Daniele de Almeida Valente
- Department of Soil Science and Plant Nutrition, Universidade Federal de Viçosa, R. Purdue S/N, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil.
| | - Rafael Gomes Siqueira
- Department of Soil Science and Plant Nutrition, Universidade Federal de Viçosa, R. Purdue S/N, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil.
| | - Cássio Marques Moquedace
- Department of Soil Science and Plant Nutrition, Universidade Federal de Viçosa, R. Purdue S/N, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil
| | - Igor Rodrigues de Assis
- Department of Soil Science and Plant Nutrition, Universidade Federal de Viçosa, R. Purdue S/N, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil.
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21
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Russo TA, Alvarado CL, Davies CJ, Drayer ZJ, Carlino-MacDonald U, Hutson A, Luo TL, Martin MJ, Corey BW, Moser KA, Rasheed JK, Halpin AL, McGann PT, Lebreton F. Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance. mBio 2024; 15:e0286723. [PMID: 38231533 PMCID: PMC10865842 DOI: 10.1128/mbio.02867-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024] Open
Abstract
Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N = 16) or cKp (N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp.IMPORTANCEHypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.
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Affiliation(s)
- Thomas A. Russo
- Veterans Administration Western New York Healthcare System, University at Buffalo, Buffalo, New York, USA
- Department of Medicine, University at Buffalo, Buffalo, New York, USA
- Department of Microbiology and Immunology, University at Buffalo, Buffalo, New York, USA
- The Witebsky Center for Microbial Pathogenesis, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Cassandra L. Alvarado
- Veterans Administration Western New York Healthcare System, University at Buffalo, Buffalo, New York, USA
- Department of Medicine, University at Buffalo, Buffalo, New York, USA
| | - Connor J. Davies
- Veterans Administration Western New York Healthcare System, University at Buffalo, Buffalo, New York, USA
- Department of Medicine, University at Buffalo, Buffalo, New York, USA
| | - Zachary J. Drayer
- Department of Medicine, University at Buffalo, Buffalo, New York, USA
| | - Ulrike Carlino-MacDonald
- Veterans Administration Western New York Healthcare System, University at Buffalo, Buffalo, New York, USA
- Department of Medicine, University at Buffalo, Buffalo, New York, USA
| | - Alan Hutson
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Ting L. Luo
- Multidrug-Resistant Organism Repository and Surveillance Network (MRSN), Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Melissa J. Martin
- Multidrug-Resistant Organism Repository and Surveillance Network (MRSN), Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Brendan W. Corey
- Multidrug-Resistant Organism Repository and Surveillance Network (MRSN), Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Kara A. Moser
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - J. Kamile Rasheed
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Alison L. Halpin
- Division of Healthcare Quality Promotion, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Patrick T. McGann
- Multidrug-Resistant Organism Repository and Surveillance Network (MRSN), Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Francois Lebreton
- Multidrug-Resistant Organism Repository and Surveillance Network (MRSN), Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
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Gao K, He Z, Xiong J, Chen Q, Lai B, Liu F, Chen P, Chen M, Luo W, Huang J, Ding W, Wang H, Pu Y, Zheng L, Jiao Y, Zhang M, Tang Z, Yue Q, Yang D, Yan T. Population structure and adaptability analysis of Schizothorax o'connori based on whole-genome resequencing. BMC Genomics 2024; 25:145. [PMID: 38321406 PMCID: PMC10845765 DOI: 10.1186/s12864-024-09975-9] [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: 07/16/2023] [Accepted: 01/04/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Schizothorax o'connori is an endemic fish distributed in the upper and lower reaches of the Yarlung Zangbo River in China. It has experienced a fourth round of whole gene replication events and is a good model for exploring the genetic differentiation and environmental adaptability of fish in the Qinghai-Tibet Plateau. The uplift of the Qinghai-Tibet Plateau has led to changes in the river system, thereby affecting gene exchange and population differentiation between fish populations. With the release of fish whole genome data, whole genome resequencing has been widely used in genetic evolutionary analysis and screening of selected genes in fish, which can better elucidate the genetic basis and molecular environmental adaptation mechanisms of fish. Therefore, our purpose of this study was to understand the population structure and adaptive characteristics of S. o'connori using the whole-genome resequencing method. RESULTS The results showed that 23,602,746 SNPs were identified from seven populations, mostly distributed on chromosomes 2 and 23. There was no significant genetic differentiation between the populations, and the genetic diversity was relatively low. However, the Zangga population could be separated from the Bomi, Linzhi, and Milin populations in the cluster analysis. Based on historical dynamics analysis of the population, the size of the ancestral population of S. o'connori was affected by the late accelerated uplift of the Qinghai Tibet Plateau and the Fourth Glacial Age. The selected sites were mostly enriched in pathways related to DNA repair and energy metabolism. CONCLUSION Overall, the whole-genome resequencing analysis provides valuable insights into the population structure and adaptive characteristics of S. o'connori. There was no obvious genetic differentiation at the genome level between the S. o'connori populations upstream and downstream of the Yarlung Zangbo River. The current distribution pattern and genetic diversity are influenced by the late accelerated uplift of the Qinghai Tibet Plateau and the Fourth Ice Age. The selected sites of S. o'connori are enriched in the energy metabolism and DNA repair pathways to adapt to the low temperature and strong ultraviolet radiation environment at high altitude.
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Affiliation(s)
- Kuo Gao
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Zhi He
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Jinxin Xiong
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Qiqi Chen
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Bolin Lai
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Fei Liu
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Ping Chen
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Mingqiang Chen
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Wenjie Luo
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Junjie Huang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Wenxiang Ding
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Haochen Wang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yong Pu
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Li Zheng
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yuanyuan Jiao
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Mingwang Zhang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Ziting Tang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Qingsong Yue
- Huadian Tibet Hydropower Development Co.,Ltd, Dagu Hydropower Station, Sangri, 856200, Shannan, China
| | - Deying Yang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China.
| | - Taiming Yan
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China.
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23
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Hackett WE, Chang D, Carvalho L, Zaia J. RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein composition that occur during biological processes. BIOINFORMATICS ADVANCES 2024; 4:vbae012. [PMID: 38384861 PMCID: PMC10879752 DOI: 10.1093/bioadv/vbae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/15/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024]
Abstract
Motivation Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically synthesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. Glycoproteins, accounting for approximately half of all proteins, require specialized proteomics data analysis methods due to micro- and macro-heterogeneities as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, a specialized toolset is needed to determine if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations. Results We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses a permutation test to generate contextual similarity, which assesses the quality of mass spectral data and outputs a graphical demonstration of the likelihood of finding biologically significant differences in glycosylation abundance datasets. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern change. RAMZIS is validated by theoretical cases and a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using this tool, researchers will be able to rigorously define the role of glycosylation and the changes that occur during biological processes. Availability and implementation https://github.com/WillHackett22/RAMZIS.
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Affiliation(s)
| | - Deborah Chang
- Department of Biochemistry, Boston University, Boston, MA 02215, United States
| | - Luis Carvalho
- Bioinformatics Program, Boston University, Boston, MA 02215, United States
- Department of Mathematics, Boston University, Boston, MA 02215, United States
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA 02215, United States
- Department of Biochemistry, Boston University, Boston, MA 02215, United States
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Baker M, Zhang X, Maciel-Guerra A, Babaarslan K, Dong Y, Wang W, Hu Y, Renney D, Liu L, Li H, Hossain M, Heeb S, Tong Z, Pearcy N, Zhang M, Geng Y, Zhao L, Hao Z, Senin N, Chen J, Peng Z, Li F, Dottorini T. Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China. Nat Commun 2024; 15:206. [PMID: 38182559 PMCID: PMC10770378 DOI: 10.1038/s41467-023-44272-1] [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: 04/04/2023] [Accepted: 12/06/2023] [Indexed: 01/07/2024] Open
Abstract
Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.
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Affiliation(s)
- Michelle Baker
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Xibin Zhang
- Shandong New Hope Liuhe Group Co. Ltd. and Qingdao Key Laboratory of Animal Feed Safety, Qingdao, Shandong, 266000, P.R. China
| | - Alexandre Maciel-Guerra
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Kubra Babaarslan
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Yinping Dong
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - Wei Wang
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - Yujie Hu
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - David Renney
- Nimrod Veterinary Products Limited, 2, Wychwood Court, Cotswold Business Village, Moreton-in-Marsh, GL56 0JQ, London, UK
| | - Longhai Liu
- Shandong Kaijia Food Co. Ltd, Weifang, P. R. China
| | - Hui Li
- Luoyang Center for Disease Control and Prevention, No. 9, Zhenghe Road, Luolong District, Luoyang City, Henan Province, Luolong, 471000, P. R. China
| | - Maqsud Hossain
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
| | - Stephan Heeb
- School of Life Sciences, University of Nottingham, East Drive, Nottingham, Nottinghamshire, NG7 2RD, UK
| | - Zhiqin Tong
- Luoyang Center for Disease Control and Prevention, No. 9, Zhenghe Road, Luolong District, Luoyang City, Henan Province, Luolong, 471000, P. R. China
| | - Nicole Pearcy
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK
- School of Life Sciences, University of Nottingham, East Drive, Nottingham, Nottinghamshire, NG7 2RD, UK
| | - Meimei Zhang
- Liaoning Provincial Center for Disease Control and Prevention, No. 168, Jinfeng Street, Hunnan District, Shenyang City, Liaoning Province, 110072, P. R. China
| | - Yingzhi Geng
- Liaoning Provincial Center for Disease Control and Prevention, No. 168, Jinfeng Street, Hunnan District, Shenyang City, Liaoning Province, 110072, P. R. China
| | - Li Zhao
- Agricultural Biopharmaceutical Laboratory, College of Chemistry and Pharmaceutical Sciences, Qingdao Agricultural University, No. 700 Changcheng Road, Chengyang District, Qingdao City, Shandong Province, 266109, P. R. China
| | - Zhihui Hao
- Chinese Veterinary Medicine Innovation Center, College of Veterinary Medicine, China Agricultural University, Haidian District, Beijing City, 100193, P. R. China
| | - Nicola Senin
- Department of Engineering, University of Perugia, Perugia, I06125, Italy
| | - Junshi Chen
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China
| | - Zixin Peng
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China.
| | - Fengqin Li
- NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, 100021, P. R. China.
| | - Tania Dottorini
- School of Veterinary Medicine and Science, University of Nottingham, College Road, Sutton Bonington, Loughborough, Leicestershire, LE12 5RD, UK.
- Centre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China.
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Tzanis E, Stratakis J, Myronakis M, Damilakis J. A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT. Phys Med 2024; 117:103195. [PMID: 38048731 DOI: 10.1016/j.ejmp.2023.103195] [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: 08/20/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023] Open
Abstract
PURPOSE To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT. METHODS Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed. RESULTS The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry. CONCLUSIONS The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.
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Affiliation(s)
- Eleftherios Tzanis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Stratakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - Marios Myronakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece.
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Abrhámová K, Groušlová M, Valentová A, Hao X, Liu B, Převorovský M, Gahura O, Půta F, Sunnerhagen P, Folk P. Truncating the spliceosomal 'rope protein' Prp45 results in Htz1 dependent phenotypes. RNA Biol 2024; 21:1-17. [PMID: 38711165 PMCID: PMC11085953 DOI: 10.1080/15476286.2024.2348896] [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] [Accepted: 04/24/2024] [Indexed: 05/08/2024] Open
Abstract
Spliceosome assembly contributes an important but incompletely understood aspect of splicing regulation. Prp45 is a yeast splicing factor which runs as an extended fold through the spliceosome, and which may be important for bringing its components together. We performed a whole genome analysis of the genetic interaction network of the truncated allele of PRP45 (prp45(1-169)) using synthetic genetic array technology and found chromatin remodellers and modifiers as an enriched category. In agreement with related studies, H2A.Z-encoding HTZ1, and the components of SWR1, INO80, and SAGA complexes represented prominent interactors, with htz1 conferring the strongest growth defect. Because the truncation of Prp45 disproportionately affected low copy number transcripts of intron-containing genes, we prepared strains carrying intronless versions of SRB2, VPS75, or HRB1, the most affected cases with transcription-related function. Intron removal from SRB2, but not from the other genes, partly repaired some but not all the growth phenotypes identified in the genetic screen. The interaction of prp45(1-169) and htz1Δ was detectable even in cells with SRB2 intron deleted (srb2Δi). The less truncated variant, prp45(1-330), had a synthetic growth defect with htz1Δ at 16°C, which also persisted in the srb2Δi background. Moreover, htz1Δ enhanced prp45(1-330) dependent pre-mRNA hyper-accumulation of both high and low efficiency splicers, genes ECM33 and COF1, respectively. We conclude that while the expression defects of low expression intron-containing genes contribute to the genetic interactome of prp45(1-169), the genetic interactions between prp45 and htz1 alleles demonstrate the sensitivity of spliceosome assembly, delayed in prp45(1-169), to the chromatin environment.
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Affiliation(s)
- Kateřina Abrhámová
- Department of Cell Biology, Faculty of Science, Charles University, Praha, Czech Republic
| | - Martina Groušlová
- Department of Cell Biology, Faculty of Science, Charles University, Praha, Czech Republic
| | - Anna Valentová
- Department of Cell Biology, Faculty of Science, Charles University, Praha, Czech Republic
| | - Xinxin Hao
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Beidong Liu
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Martin Převorovský
- Department of Cell Biology, Faculty of Science, Charles University, Praha, Czech Republic
| | - Ondřej Gahura
- Institute of Parasitology, Biology Centre, Czech Academy of Sciences, České Budějovice, Czech Republic
| | - František Půta
- Department of Cell Biology, Faculty of Science, Charles University, Praha, Czech Republic
| | - Per Sunnerhagen
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Petr Folk
- Department of Cell Biology, Faculty of Science, Charles University, Praha, Czech Republic
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Holmes KE, VanInsberghe D, Ferreri LM, Elie B, Ganti K, Lee CY, Lowen AC. Viral expansion after transfer is a primary driver of influenza A virus transmission bottlenecks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.19.567585. [PMID: 38014182 PMCID: PMC10680852 DOI: 10.1101/2023.11.19.567585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
For many viruses, narrow bottlenecks acting during transmission sharply reduce genetic diversity in a recipient host relative to the donor. Since genetic diversity represents adaptive potential, such losses of diversity are though to limit the opportunity for viral populations to undergo antigenic change and other adaptive processes. Thus, a detailed picture of evolutionary dynamics during transmission is critical to understanding the forces driving viral evolution at an epidemiologic scale. To advance this understanding, we used a novel barcoded virus library and a guinea pig model of transmission to decipher where in the transmission process diversity is lost for influenza A viruses. In inoculated guinea pigs, we show that a high level of viral genetic diversity is maintained across time. Continuity in the barcodes detected furthermore indicates that stochastic effects are not pronounced within inoculated hosts. Importantly, in both aerosol-exposed and direct contact-exposed animals, we observed many barcodes at the earliest time point(s) positive for infectious virus, indicating robust transfer of diversity through the environment. This high viral diversity is short-lived, however, with a sharp decline seen 1-2 days after initiation of infection. Although major losses of diversity at transmission are well described for influenza A virus, our data indicate that events that occur following viral transfer and during the earliest stages of natural infection have a predominant role in this process. This finding suggests that immune selection may have greater opportunity to operate during influenza A transmission than previously recognized.
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Affiliation(s)
- Katie E. Holmes
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - David VanInsberghe
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Lucas M. Ferreri
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Baptiste Elie
- MIVEGEC, CNRS, IRD, Université de Montpellier, Montpellier, France
| | - Ketaki Ganti
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Chung-Young Lee
- Department of Microbiology, School of Medicine, Kyungpook National University, Jung-gu, Daegu, Republic of Korea
| | - Anice C. Lowen
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
- Emory Center of Excellence for Influenza Research and Response (CEIRR), Atlanta, GA, USA
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Kim T, Lim H, Jun S, Park J, Lee D, Lee JH, Lee JY, Bang D. Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer. Sci Rep 2023; 13:20485. [PMID: 37993659 PMCID: PMC10665396 DOI: 10.1038/s41598-023-47740-2] [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: 04/15/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Gynecologic cancer, including ovarian cancer and endometrial cancer, is characterized by morphological and molecular heterogeneity. Germline and somatic testing are available for patients to screen for pathogenic variants in genes such as BRCA1/2. Tissue expression levels of immunogenomic markers such as PD-L1 are also being used in clinical research. The basic therapeutic approach to gynecologic cancer combines surgery with chemotherapy. Immunotherapy, while not yet a mainstream treatment for gynecologic cancers, is advancing, with Dostarlimab recently receiving approval as a treatment for endometrial cancer. The goal remains to harness stimulated immune cells in the bloodstream to eradicate multiple metastases, a feat currently deemed challenging in a typical clinical setting. For the discovery of novel immunotherapy-based tumor targets, tumor-infiltrating lymphocytes (TILs) give a key insight on tumor-related immune activities by providing T cell receptor (TCR) sequences. Understanding the TCR repertoires of TILs in metastatic tissues and the circulation is important from an immunotherapy standpoint, as a subset of T cells in the blood have the potential to help kill tumor cells. To explore the relationship between distant tissue biopsy regions and blood circulation, we investigated the TCR beta chain (TCRβ) in bulk tumor and matched blood samples from 39 patients with gynecologic cancer. We found that the TCR clones of TILs at different tumor sites were globally shared within patients and had high overlap with the TCR clones in peripheral blood.
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Affiliation(s)
- Taehoon Kim
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Hyeonseob Lim
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Soyeong Jun
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Junsik Park
- Department of Obstetrics and Gynecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Dongin Lee
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Ji Hyun Lee
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
- Department of Biomedical Science and Technology, Kyung Hee Medical Science Research Institute, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
| | - Jung-Yun Lee
- Department of Obstetrics and Gynecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Duhee Bang
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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Yang J, Lim JT, Victor P, Chen C, Khwaja H, Schnellmann RG, Roe DJ, Gokhale PC, DeCaprio JA, Padi M. Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565218. [PMID: 37961132 PMCID: PMC10635082 DOI: 10.1101/2023.11.01.565218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Merkel Cell Carcinoma (MCC) is a highly aggressive neuroendocrine cutaneous malignancy arising from either ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. It is the only known neuroendocrine tumor (NET) with a virus etiology. Despite extensive research, our understanding of the molecular mechanisms driving the transition from normal cells to MCC remains limited. To address this knowledge gap, we assessed the impact of inducible MCPyV T antigens into normal human fibroblasts by performing RNA sequencing. Our findings suggested that the WNT signaling pathway plays a critical role in the development of MCC. To test this model, we bioinformatically evaluated various perturbagens for their ability to reverse the MCC gene expression signature and identified pyrvinium pamoate, an FDA-approved anthelminthic drug known for its anti-tumor potential in multiple cancers. Leveraging transcriptomic, network, and molecular analyses, we found that pyrvinium effectively targets multiple MCC vulnerabilities. Specifically, pyrvinium not only reverses the neuroendocrine features of MCC by modulating canonical and non-canonical WNT signaling pathways but also inhibits cancer cell growth by activating the p53-mediated apoptosis pathway, disrupting mitochondrial function, and inducing endoplasmic reticulum (ER) stress. Pyrvinium also effectively inhibits tumor growth in an MCC mouse xenograft model. These findings offer new avenues for the development of therapeutic strategies for neuroendocrine cancer and highlight the utility of pyrvinium as a potential treatment for MCC.
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Affiliation(s)
- Jiawen Yang
- University of Arizona Cancer Center, Tucson, Arizona, USA
| | - James T Lim
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona, USA
| | - Paul Victor
- Department of Pharmacology and Toxicology, The University of Arizona R. Ken Coit College of Pharmacy, Skaggs Pharmaceutical Sciences Center, Tucson, Arizona, USA
| | - Chen Chen
- University of Arizona Cancer Center, Tucson, Arizona, USA
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, AZ, USA
| | - Hunain Khwaja
- University of Arizona Cancer Center, Tucson, Arizona, USA
| | - Rick G Schnellmann
- Department of Pharmacology and Toxicology, The University of Arizona R. Ken Coit College of Pharmacy, Skaggs Pharmaceutical Sciences Center, Tucson, Arizona, USA
- The University of Arizona College of Medicine, Tucson, Arizona, USA
- The University of Arizona, BIO5 Institute, Tucson, Arizona, USA
- Southern Arizona VA Health Care System, USA
| | - Denise J Roe
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, AZ, USA
| | - Prafulla C Gokhale
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - James A DeCaprio
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Megha Padi
- University of Arizona Cancer Center, Tucson, Arizona, USA
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona, USA
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Russo TA, Alvarado CL, Davies CJ, Drayer ZJ, Carlino-MacDonald U, Hutson A, Luo TL, Martin MJ, Corey BW, Moser KA, Rasheed JK, Halpin AL, McGann PT, Lebreton F. Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.30.547231. [PMID: 37961280 PMCID: PMC10634668 DOI: 10.1101/2023.06.30.547231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g. siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N=16) or cKp (N=33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥ 4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49) respectively. These findings can be used to inform the identification of hvKp. Importance Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all 5 of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥ 4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which in turn would assist in optimizing patient care.
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Coto-Segura P, Segú-Vergés C, Martorell A, Moreno-Ramírez D, Jorba G, Junet V, Guerri F, Daura X, Oliva B, Cara C, Suárez-Magdalena O, Abraham S, Mas JM. A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis. Front Immunol 2023; 14:1212981. [PMID: 37809085 PMCID: PMC10552644 DOI: 10.3389/fimmu.2023.1212981] [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: 04/27/2023] [Accepted: 08/07/2023] [Indexed: 10/10/2023] Open
Abstract
Background Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles. Methods We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug. Results The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed. Conclusion Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.
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Affiliation(s)
- Pablo Coto-Segura
- Dermatology Department, Hospital Vital Alvarez-Buylla de Mieres, Asturias, Spain
| | - Cristina Segú-Vergés
- Anaxomics Biotech SL, Barcelona, Spain
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - David Moreno-Ramírez
- Dermatology Department, University Hospital Virgen Macarena, Andalusian Health Service, University of Seville, Seville, Spain
| | - Guillem Jorba
- Anaxomics Biotech SL, Barcelona, Spain
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Valentin Junet
- Anaxomics Biotech SL, Barcelona, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Filippo Guerri
- Anaxomics Biotech SL, Barcelona, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Xavier Daura
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Cerdanyola del Vallès, Spain
| | - Baldomero Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | - Sonya Abraham
- National Heart and Lung Institute (NHLI), Faculty of Medicine, Imperial College, London, United Kingdom
- Medical Affairs, UCB Pharma, Brussels, Belgium
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Wang C, Zhang M, Zhao J, Li B, Xiao X, Zhang Y. The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer. Comput Biol Med 2023; 163:107220. [PMID: 37406589 DOI: 10.1016/j.compbiomed.2023.107220] [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: 05/15/2023] [Revised: 06/14/2023] [Accepted: 06/30/2023] [Indexed: 07/07/2023]
Abstract
Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tools in predicting cancer drug sensitivity. In this study, we proposed a novel method called Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), which leverages integrated omics knowledge, including genomic and transcriptomic data, to fill in missing cancer-drug associations and enhance drug sensitivity prediction. Our method employs an encoder-decoder network to learn latent feature representations from cell lines. These learned feature vectors are then fed into a collective variational autoencoder network to train an association matrix. We evaluated MOICVAE on the GDSC and CCLE benchmark datasets using 10-fold cross-validation and achieved impressive AUCs of 0.856 and 0.808, respectively, outperforming state-of-the-art methods. Furthermore, on the TCGA dataset, consisting of 25 drugs across 7 cancer types, MOICVAE exhibited an average AUC of 0.91 in predicting drug sensitivity. Additionally, significant differences were observed in survival, tumor inflammatory assessment, and tumor microenvironment between the predicted drug-sensitive and drug-resistant groups. These results are consistent with predictions made on the METABRIC dataset. Moreover, we discovered that fusing omics data based on mRNA and CNV (copy number variations) yielded superior results in drug sensitivity prediction. MOICVAE not only achieved higher accuracy in drug sensitivity prediction but also provided additional value for combining immunotherapy with chemotherapy, offering patients with more precise treatment options. The code and dataset for MOICVAE are freely available at https://github.com/wanggnoc/MOICVAE.
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Affiliation(s)
- Cong Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Mengyan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Jiyun Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Bin Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xingjun Xiao
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, 150086, People's Republic of China.
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China; College of Pathology, Qiqihar Medical University, Qiqihar, 161042, China.
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33
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Downs M, Zaia J, Sethi MK. Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases. MASS SPECTROMETRY REVIEWS 2023; 42:1848-1875. [PMID: 35719114 PMCID: PMC9763553 DOI: 10.1002/mas.21792] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/12/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.
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Affiliation(s)
- Margaret Downs
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts, USA
| | - Joseph Zaia
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Manveen K Sethi
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts, USA
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Hackett WE, Chang D, Carvalho L, Zaia J. RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein structure that occur during biological processes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.30.542895. [PMID: 37398011 PMCID: PMC10312533 DOI: 10.1101/2023.05.30.542895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Motivation Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically syn-thesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. While glycoproteins account for approximately half of all proteins, their macro- and micro-heterogeneity requires specialized proteomics data analysis methods as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, this necessitated specialized statistical metrics to identify if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations. Results We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide biomedical researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses contextual similarity to assess the quality of mass spectral data and generates graphical output that demonstrates the likelihood of finding biologically significant differences in glycosylation abundance dataset. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern expression change. Herein RAMZIS approach is validated by theoretical cases and by a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using our tool, researchers will be able to rigor-ously define the role of glycosylation and the changes that occur during biological processes.
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Affiliation(s)
| | - Deborah Chang
- Department of Biochemistry, Boston University, One Silber Way, Boston 02215
| | - Luis Carvalho
- Boston University, Bioinformatics Program, One Silber Way, Boston 02215, MA, USA
- Department of Mathematics, Boston University, One Silber Way, Boston 02215
| | - Joseph Zaia
- Boston University, Bioinformatics Program, One Silber Way, Boston 02215, MA, USA
- Department of Biochemistry, Boston University, One Silber Way, Boston 02215
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35
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An EffcientNet-encoder U-Net Joint Residual Refinement Module with Tversky–Kahneman Baroni–Urbani–Buser loss for biomedical image Segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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36
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Abbosh C, Frankell AM, Harrison T, Kisistok J, Garnett A, Johnson L, Veeriah S, Moreau M, Chesh A, Chaunzwa TL, Weiss J, Schroeder MR, Ward S, Grigoriadis K, Shahpurwalla A, Litchfield K, Puttick C, Biswas D, Karasaki T, Black JRM, Martínez-Ruiz C, Bakir MA, Pich O, Watkins TBK, Lim EL, Huebner A, Moore DA, Godin-Heymann N, L'Hernault A, Bye H, Odell A, Roberts P, Gomes F, Patel AJ, Manzano E, Hiley CT, Carey N, Riley J, Cook DE, Hodgson D, Stetson D, Barrett JC, Kortlever RM, Evan GI, Hackshaw A, Daber RD, Shaw JA, Aerts HJWL, Licon A, Stahl J, Jamal-Hanjani M, Birkbak NJ, McGranahan N, Swanton C. Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA. Nature 2023; 616:553-562. [PMID: 37055640 PMCID: PMC7614605 DOI: 10.1038/s41586-023-05776-4] [Citation(s) in RCA: 89] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/30/2023] [Indexed: 04/15/2023]
Abstract
Circulating tumour DNA (ctDNA) can be used to detect and profile residual tumour cells persisting after curative intent therapy1. The study of large patient cohorts incorporating longitudinal plasma sampling and extended follow-up is required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1,069 plasma samples collected from 197 patients enrolled in the TRACERx study2. A lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days after surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark-negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with a poor clinical outcome. By measuring subclone cancer cell fractions in preoperative plasma, we found that subclones seeding future metastases were significantly more expanded compared with non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide insights into the process of metastatic dissemination using low-ctDNA-level liquid biopsy.
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Affiliation(s)
- Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Alexander M Frankell
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | - Judit Kisistok
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | | | | | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | | | - Tafadzwa L Chaunzwa
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Freiburg University Hospital, Freiburg, Germany
| | | | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Clare Puttick
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Dhruva Biswas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Emilia L Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | | | | | | | | | | | - Fabio Gomes
- The Christie NHS Foundation Trust, Manchester, UK
| | - Akshay J Patel
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
| | - Elizabeth Manzano
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Crispin T Hiley
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Nicolas Carey
- Cancer Research Centre, University of Leicester, Leicester, UK
| | - Joan Riley
- Cancer Research Centre, University of Leicester, Leicester, UK
| | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | | | | | | | - Gerard I Evan
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Allan Hackshaw
- Cancer Research UK & UCL Cancer Trials Centre, London, UK
| | | | - Jacqui A Shaw
- Cancer Research Centre, University of Leicester, Leicester, UK
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| | | | | | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Nicolai J Birkbak
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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Hewitt J, Lawson C, Verrall B, Grealish L. Implementing voluntary assisted dying laws in healthcare: Exploring beliefs to identify implementation hurdles. Res Nurs Health 2023; 46:113-126. [PMID: 36538345 PMCID: PMC10107130 DOI: 10.1002/nur.22287] [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: 04/29/2022] [Revised: 11/20/2022] [Accepted: 11/26/2022] [Indexed: 12/24/2022]
Abstract
The number of countries introducing voluntary assisted dying (VAD) laws is increasing. Actively taking steps to end a person's life is contentious so implementing these laws into healthcare services presents unique challenges. Theoretically underpinned by the Advocacy Coalition Framework, this study identified the beliefs of classes of stakeholders who engaged with the parliamentary process associated with the introduction of VAD laws in Queensland, Australia. Submissions about VAD made to a parliamentary inquiry were allocated to a class and qualitatively analysed to identify underlying beliefs. The data were then subjected to statistical analysis including nonmetric dimensional scaling and one-way analysis of similarity. Data visualisation techniques were used to generate a chord map and heatmap, to identify the belief types, as well as similarities and differences between beliefs and among classes. Fourteen different beliefs were identified in the 91 reviewed submissions. Six were deep core beliefs and eight were policy core beliefs. Beliefs could be associated with a positive or negative sentiment toward VAD. In this study, the class of Health Services expressed more negative sentiments to VAD than neutral or positive sentiments. The sentiments expressed by the class of Health Professionals were equally divided between positive, neutral and negative. These findings provide important insights for implementors as laws become operational. In particular, for organisations that provide health services, clear articulation of their stance in relevant policy and guidance documents is recommended.
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Affiliation(s)
- Jayne Hewitt
- School of Nursing and MidwiferyGriffith UniversityQueenslandAustralia
- Griffith Law SchoolGriffith UniversityQueenslandAustralia
| | - Charles Lawson
- Griffith Law SchoolGriffith UniversityQueenslandAustralia
| | - Brodie Verrall
- Centre for Planetary Health and Food SecurityGriffith UniversityQueenslandAustralia
| | - Laurie Grealish
- Gold Coast University HospitalQueenslandAustralia
- Menzies Health Institute QueenslandGriffith UniversityQueenslandAustralia
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38
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Stroynowska-Czerwinska AM, Klimczak M, Pastor M, Kazrani AA, Misztal K, Bochtler M. Clustered PHD domains in KMT2/MLL proteins are attracted by H3K4me3 and H3 acetylation-rich active promoters and enhancers. Cell Mol Life Sci 2023; 80:23. [PMID: 36598580 PMCID: PMC9813062 DOI: 10.1007/s00018-022-04651-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 11/18/2022] [Accepted: 11/23/2022] [Indexed: 01/05/2023]
Abstract
Histone lysine-specific methyltransferase 2 (KMT2A-D) proteins, alternatively called mixed lineage leukemia (MLL1-4) proteins, mediate positive transcriptional memory. Acting as the catalytic subunits of human COMPASS-like complexes, KMT2A-D methylate H3K4 at promoters and enhancers. KMT2A-D contain understudied highly conserved triplets and a quartet of plant homeodomains (PHDs). Here, we show that all clustered (multiple) PHDs localize to the well-defined loci of H3K4me3 and H3 acetylation-rich active promoters and enhancers. Surprisingly, we observe little difference in binding pattern between PHDs from promoter-specific KMT2A-B and enhancer-specific KMT2C-D. Fusion of the KMT2A CXXC domain to the PHDs drastically enhances their preference for promoters over enhancers. Hence, the presence of CXXC domains in KMT2A-B, but not KMT2C-D, may explain the promoter/enhancer preferences of the full-length proteins. Importantly, targets of PHDs overlap with KMT2A targets and are enriched in genes involved in the cancer pathways. We also observe that PHDs of KMT2A-D are mutated in cancer, especially within conserved folding motifs (Cys4HisCys2Cys/His). The mutations cause a domain loss-of-function. Taken together, our data suggest that PHDs of KMT2A-D guide the full-length proteins to active promoters and enhancers, and thus play a role in positive transcriptional memory.
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Affiliation(s)
| | - Magdalena Klimczak
- International Institute of Molecular and Cell Biology, 02-109, Warsaw, Poland
| | - Michal Pastor
- International Institute of Molecular and Cell Biology, 02-109, Warsaw, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106, Warsaw, Poland
| | - Asgar Abbas Kazrani
- International Institute of Molecular and Cell Biology, 02-109, Warsaw, Poland
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch-Graffenstaden, France
| | - Katarzyna Misztal
- International Institute of Molecular and Cell Biology, 02-109, Warsaw, Poland
| | - Matthias Bochtler
- International Institute of Molecular and Cell Biology, 02-109, Warsaw, Poland.
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106, Warsaw, Poland.
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39
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Olusoji OD, Barabás G, Spaak JW, Fontana S, Neyens T, De Laender F, Aerts M. Measuring individual‐level trait diversity: a critical assessment of methods. OIKOS 2022. [DOI: 10.1111/oik.09178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Oluwafemi D. Olusoji
- Center for Statistics, Data Science Inst., Hasselt Univ. Hasselt Belgium
- Research Unit in Environmental and Evolutionary Biology (URBE), Inst. of Life‐Earth‐Environment (ILEE), Namur Inst. for Complex Systems (NAXYS), Univ. de Namur Namur Belgium
| | - György Barabás
- Division of Ecological and Environmental Modeling, Linköping Univ. Linköping Sweden
- ELTE‐MTA Theoretical Biology and Evolutionary Ecology Research Group Budapest Hungary
- Inst. of Evolution, Centre for Ecological Research Budapest Hungary
| | - Jurg W. Spaak
- Research Unit in Environmental and Evolutionary Biology (URBE), Inst. of Life‐Earth‐Environment (ILEE), Namur Inst. for Complex Systems (NAXYS), Univ. de Namur Namur Belgium
| | - Simone Fontana
- Nature Conservation and Landscape Ecology, Univ. of Freiburg Freiburg Germany
- Biodiversity and Conservation Biology, Swiss Federal Research Inst. WSL Birmensdorf Switzerland
- Abteilung Natur und Landschaft, Amt für Natur, Jagd und Fischerei, Kanton St. Gallen St. Gallen Switzerland
| | - Thomas Neyens
- Center for Statistics, Data Science Inst., Hasselt Univ. Hasselt Belgium
- L‐BioStat, Dept of Public Health and Primary Care, Faculty of Medicine, KU Leuven Leuven Belgium
| | - Frederik De Laender
- Research Unit in Environmental and Evolutionary Biology (URBE), Inst. of Life‐Earth‐Environment (ILEE), Namur Inst. for Complex Systems (NAXYS), Univ. de Namur Namur Belgium
| | - Marc Aerts
- Center for Statistics, Data Science Inst., Hasselt Univ. Hasselt Belgium
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40
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Chen S, Chang Y, Li L, Acosta D, Li Y, Guo Q, Wang C, Turkes E, Morrison C, Julian D, Hester ME, Scharre DW, Santiskulvong C, Song SX, Plummer JT, Serrano GE, Beach TG, Duff KE, Ma Q, Fu H. Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer's disease. Acta Neuropathol Commun 2022; 10:188. [PMID: 36544231 PMCID: PMC9773466 DOI: 10.1186/s40478-022-01494-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
Human middle temporal gyrus (MTG) is a vulnerable brain region in early Alzheimer's disease (AD), but little is known about the molecular mechanisms underlying this regional vulnerability. Here we utilize the 10 × Visium platform to define the spatial transcriptomic profile in both AD and control (CT) MTG. We identify unique marker genes for cortical layers and the white matter, and layer-specific differentially expressed genes (DEGs) in human AD compared to CT. Deconvolution of the Visium spots showcases the significant difference in particular cell types among cortical layers and the white matter. Gene co-expression analyses reveal eight gene modules, four of which have significantly altered co-expression patterns in the presence of AD pathology. The co-expression patterns of hub genes and enriched pathways in the presence of AD pathology indicate an important role of cell-cell-communications among microglia, oligodendrocytes, astrocytes, and neurons, which may contribute to the cellular and regional vulnerability in early AD. Using single-molecule fluorescent in situ hybridization, we validated the cell-type-specific expression of three novel DEGs (e.g., KIF5A, PAQR6, and SLC1A3) and eleven previously reported DEGs associated with AD pathology (i.e., amyloid beta plaques and intraneuronal neurofibrillary tangles or neuropil threads) at the single cell level. Our results may contribute to the understanding of the complex architecture and neuronal and glial response to AD pathology of this vulnerable brain region.
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Affiliation(s)
- Shuo Chen
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH 43210 USA
| | - Yuzhou Chang
- grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Liangping Li
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Diana Acosta
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Yang Li
- grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Qi Guo
- grid.261331.40000 0001 2285 7943Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH 43210 USA ,grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Cankun Wang
- grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Emir Turkes
- grid.83440.3b0000000121901201UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London, UK
| | - Cody Morrison
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Dominic Julian
- grid.240344.50000 0004 0392 3476The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205 USA
| | - Mark E. Hester
- grid.240344.50000 0004 0392 3476The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205 USA
| | - Douglas W. Scharre
- grid.261331.40000 0001 2285 7943Department of Neurology, Center for Cognitive and Memory Disorders, Center for Neuromodulation, Ohio State University, Columbus, OH 43210 USA
| | - Chintda Santiskulvong
- grid.50956.3f0000 0001 2152 9905Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | - Sarah XueYing Song
- grid.50956.3f0000 0001 2152 9905Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | - Jasmine T. Plummer
- grid.50956.3f0000 0001 2152 9905Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | - Geidy E. Serrano
- grid.414208.b0000 0004 0619 8759Banner Sun Health Research Institute, Sun City, AZ 85351 USA
| | - Thomas G. Beach
- grid.414208.b0000 0004 0619 8759Banner Sun Health Research Institute, Sun City, AZ 85351 USA
| | - Karen E. Duff
- grid.83440.3b0000000121901201UK Dementia Research Institute, UCL Queen Square Institute of Neurology, London, UK
| | - Qin Ma
- grid.261331.40000 0001 2285 7943Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Hongjun Fu
- grid.261331.40000 0001 2285 7943Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
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Chechetkin VR, Lobzin VV. Evolving ribonucleocapsid assembly/packaging signals in the genomes of the human and animal coronaviruses: targeting, transmission and evolution. J Biomol Struct Dyn 2022; 40:11239-11263. [PMID: 34338591 DOI: 10.1080/07391102.2021.1958061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
A world-wide COVID-19 pandemic intensified strongly the studies of molecular mechanisms related to the coronaviruses. The origin of coronaviruses and the risks of human-to-human, animal-to-human and human-to-animal transmission of coronaviral infections can be understood only on a broader evolutionary level by detailed comparative studies. In this paper, we studied ribonucleocapsid assembly-packaging signals (RNAPS) in the genomes of all seven known pathogenic human coronaviruses, SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-229E and HCoV-NL63 and compared them with RNAPS in the genomes of the related animal coronaviruses including SARS-Bat-CoV, MERS-Camel-CoV, MHV, Bat-CoV MOP1, TGEV and one of camel alphacoronaviruses. RNAPS in the genomes of coronaviruses were evolved due to weakly specific interactions between genomic RNA and N proteins in helical nucleocapsids. Combining transitional genome mapping and Jaccard correlation coefficients allows us to perform the analysis directly in terms of underlying motifs distributed over the genome. In all coronaviruses, RNAPS were distributed quasi-periodically over the genome with the period about 54 nt biased to 57 nt and to 51 nt for the genomes longer and shorter than that of SARS-CoV, respectively. The comparison with the experimentally verified packaging signals for MERS-CoV, MHV and TGEV proved that the distribution of particular motifs is strongly correlated with the packaging signals. We also found that many motifs were highly conserved in both characters and positioning on the genomes throughout the lineages that make them promising therapeutic targets. The mechanisms of encapsidation can affect the recombination and co-infection as well.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Vladimir R Chechetkin
- Engelhardt Institute of Molecular Biology of Russian Academy of Sciences, Moscow, Russia
| | - Vasily V Lobzin
- School of Physics, University of Sydney, Sydney, NSW, Australia
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Onah E, Uzor PF, Ugwoke IC, Eze JU, Ugwuanyi ST, Chukwudi IR, Ibezim A. Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors. BMC Bioinformatics 2022; 23:466. [DOI: 10.1186/s12859-022-05017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.
Results
Among the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).
Conclusions
Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.
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Gómez-Bañuelos E, Shi J, Wang H, Danila MI, Bridges SL, Giles JT, Sims GP, Andrade F, Darrah E. Heavy Chain Constant Region Usage in Antibodies to Peptidylarginine Deiminase 4 as a Marker of Disease Subsets in Rheumatoid Arthritis. Arthritis Rheumatol 2022; 74:1746-1754. [PMID: 35675168 PMCID: PMC9617771 DOI: 10.1002/art.42262] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/03/2022] [Accepted: 06/02/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The study of autoantibody isotypes in autoimmune diseases is useful for identifying clinically relevant endotypes. This study was undertaken to study the prevalence and clinical significance of different isotypes and IgG subclasses of anti-peptidylarginine deiminase 4 (anti-PAD4) autoantibodies in individuals with rheumatoid arthritis (RA). METHODS In 196 RA subjects and 64 healthy controls, anti-PAD4 antibody types were determined using enzyme-linked immunosorbent assay. We investigated associations between anti-PAD4 antibodies and clinical outcomes, and relevant features were confirmed in an independent RA cohort. RESULTS Anti-PAD4 IgG1, anti-PAD4 IgG2, anti-PAD4 IgG3, anti-PAD4 IgG4, anti-PAD4 IgA, and anti-PAD4 IgE antibodies were more frequent in RA patients than healthy controls (P < 0.001). Anti-PAD4 IgG1, anti-PAD4 IgG3, and anti-PAD4 IgE were associated with distinct clinical features. Anti-PAD4 IgG1 was predictive of progressive radiographic joint damage (odds ratio [OR] 4.88, P = 0.005), especially in RA patients without baseline joint damage (40% versus 0%, P = 0.003) or in those negative for anti-cyclic citrullinated peptide and/or rheumatoid factor (OR 32; P = 0.009). IgG1 was also associated with higher levels of C-reactive protein (P = 0.006) and interleukin-6 (P = 0.021). RA patients with anti-PAD4 IgG3 had higher baseline joint damage scores (median Sharp/van der Heijde score 13 versus 7, P = 0.046), while those with anti-PAD4 IgE had higher Disease Activity Score in 28 joints (median 4.0 versus 3.5, P = 0.025), more frequent rheumatoid nodules (31% versus 16%, P = 0.025), and more frequent interstitial lung disease (ground-glass opacification) (24% versus 9%, P = 0.014). Anti-PAD4 IgG1 antibody associations with joint damage were corroborated in an independent RA cohort. CONCLUSION Anti-PAD4 IgG1, anti-PAD4 IgG3, and anti-PAD4 IgE antibodies identify discrete disease subsets in RA, suggesting that heavy chain usage drives distinct effector mechanisms of anti-PAD4 antibodies in RA.
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Affiliation(s)
- E Gómez-Bañuelos
- Division of Rheumatology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - J Shi
- Division of Rheumatology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - H Wang
- Division of Rheumatology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - MI Danila
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - SL Bridges
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - JT Giles
- Division of Rheumatology, Columbia University, College of Physicians and Surgeons, New York, NY, USA
| | - GP Sims
- Early Respiratory & Inflammation, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - F Andrade
- Division of Rheumatology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - E Darrah
- Division of Rheumatology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Johnson CA, Drever CR, Kirby P, Neave E, Martin AE. Protecting boreal caribou habitat can help conserve biodiversity and safeguard large quantities of soil carbon in Canada. Sci Rep 2022; 12:17067. [PMID: 36224283 PMCID: PMC9556649 DOI: 10.1038/s41598-022-21476-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 09/27/2022] [Indexed: 12/30/2022] Open
Abstract
Boreal caribou require large areas of undisturbed habitat for persistence. They are listed as threatened with the risk of extinction in Canada because of landscape changes induced by human activities and resource extraction. Here we ask: Can the protection of habitat for boreal caribou help Canada meet its commitments under the United Nations Convention on Biological Diversity and United Nations Framework Convention on Climate Change? We identified hotspots of high conservation value within the distribution of boreal caribou based on: (1) three measures of biodiversity for at risk species (species richness, unique species and taxonomic diversity); (2) climate refugia or areas forecasted to remain unchanged under climate change; and, (3) areas of high soil carbon that could add to Canada's greenhouse gas emissions if released into the atmosphere. We evaluated the overlap among hotspot types and how well hotspots were represented in Canada's protected and conserved areas network. While hotspots are widely distributed across the boreal caribou distribution, with nearly 80% of the area falling within at least one hotspot type, only 3% of the distribution overlaps three or more hotspots. Moreover, the protected and conserved areas network only captures about 10% of all hotspots within the boreal caribou distribution. While the protected and conserved areas network adequately represents hotspots with high numbers of at risk species, areas occupied by unique species, as well as the full spectrum of areas occupied by different taxa, are underrepresented. Climate refugia and soil carbon hotspots also occur at lower percentages than expected. These findings illustrate the potential co-benefits of habitat protection for caribou to biodiversity and ecosystem services and suggest caribou may be a good proxy for future protected areas planning and for developing effective conservation strategies in regional assessments.
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Affiliation(s)
- Cheryl A. Johnson
- grid.34428.390000 0004 1936 893XEnvironment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON K1A 0H3 Canada ,grid.86715.3d0000 0000 9064 6198Department of Applied Geomatics, University of Sherbrooke, Sherbrooke, QC J1K 2R1 Canada
| | | | - Patrick Kirby
- grid.34428.390000 0004 1936 893XEnvironment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON K1A 0H3 Canada
| | - Erin Neave
- grid.34428.390000 0004 1936 893XEnvironment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON K1A 0H3 Canada
| | - Amanda E. Martin
- grid.34428.390000 0004 1936 893XEnvironment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON K1A 0H3 Canada ,grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, ON K1S 5B6 Canada
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Coleman AL, Wait DA. Urban Prairie Plots and Gardens Can Sustain Plant-Pollinator Interactions Similar to Established Rural Prairies. AMERICAN MIDLAND NATURALIST 2022. [DOI: 10.1674/0003-0031-188.1.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Martin AE, Neave E, Kirby P, Drever CR, Johnson CA. Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap. Sci Rep 2022; 12:11895. [PMID: 35831324 PMCID: PMC9279314 DOI: 10.1038/s41598-022-15274-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/21/2022] [Indexed: 11/09/2022] Open
Abstract
The biodiversity and climate change crises have led countries-including Canada-to commit to protect more land and inland waters and to stabilize greenhouse gas concentrations. Canada is also obligated to recover populations of at-risk species, including boreal caribou. Canada has the opportunity to expand its protected areas network to protect hotspots of high value for biodiversity and climate mitigation. However, co-occurrence of hotspots is rare. Here we ask: is it possible to expand the network to simultaneously protect areas important for boreal caribou, other species at risk, climate refugia, and carbon stores? We used linear programming to prioritize areas for protection based on these conservation objectives, and assessed how prioritization for multiple, competing objectives affected the outcome for each individual objective. Our multi-objective approach produced reasonably strong representation of value across objectives. Although trade-offs were required, the multi-objective outcome was almost always better than when we ignored one objective to maximize value for another, highlighting the risk of assuming that a plan based on one objective will also result in strong outcomes for others. Multi-objective optimization approaches could be used to plan for protected areas networks that address biodiversity and climate change objectives, even when hotspots do not co-occur.
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Affiliation(s)
- Amanda E Martin
- Environment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON, K1S 5B6, Canada.
- Department of Biology, Carleton University, Ottawa, ON, K1S 5B6, Canada.
| | - Erin Neave
- Environment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON, K1S 5B6, Canada
| | - Patrick Kirby
- Environment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON, K1S 5B6, Canada
| | | | - Cheryl A Johnson
- Environment and Climate Change Canada, Science and Technology, National Wildlife Research Centre, Ottawa, ON, K1S 5B6, Canada
- Department of Applied Geomatics, University of Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada
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Panchy N, Watanabe K, Takahashi M, Willems A, Hong T. Comparative single-cell transcriptomes of dose and time dependent epithelial–mesenchymal spectrums. NAR Genom Bioinform 2022; 4:lqac072. [PMID: 36159174 PMCID: PMC9492285 DOI: 10.1093/nargab/lqac072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/17/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Epithelial–mesenchymal transition (EMT) is a cellular process involved in development and disease progression. Intermediate EMT states were observed in tumors and fibrotic tissues, but previous in vitro studies focused on time-dependent responses with single doses of signals; it was unclear whether single-cell transcriptomes support stable intermediates observed in diseases. Here, we performed single-cell RNA-sequencing with human mammary epithelial cells treated with multiple doses of TGF-β. We found that dose-dependent EMT harbors multiple intermediate states at nearly steady state. Comparisons of dose- and time-dependent EMT transcriptomes revealed that the dose-dependent data enable higher sensitivity to detect genes associated with EMT. We identified cell clusters unique to time-dependent EMT, reflecting cells en route to stable states. Combining dose- and time-dependent cell clusters gave rise to accurate prognosis for cancer patients. Our transcriptomic data and analyses uncover a stable EMT continuum at the single-cell resolution, and complementary information of two types of single-cell experiments.
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Affiliation(s)
- Nicholas Panchy
- Department of Biochemistry & Cellular and Molecular Biology. The University of Tennessee , Knoxville, Knoxville, TN 37996, USA
| | - Kazuhide Watanabe
- RIKEN Center for Integrative Medical Sciences , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Masataka Takahashi
- RIKEN Center for Integrative Medical Sciences , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Andrew Willems
- School of Genome Science and Technology, The University of Tennessee , Knoxville, Knoxville, TN 37916, USA
| | - Tian Hong
- Department of Biochemistry & Cellular and Molecular Biology. The University of Tennessee , Knoxville, Knoxville, TN 37996, USA
- National Institute for Mathematical and Biological Synthesis , Knoxville, TN 37996, USA
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Pyridazinones and Structurally Related Derivatives with Anti-Inflammatory Activity. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27123749. [PMID: 35744876 PMCID: PMC9229294 DOI: 10.3390/molecules27123749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 01/20/2023]
Abstract
Persistent inflammation contributes to a number of diseases; therefore, control of the inflammatory response is an important therapeutic goal. In an effort to identify novel anti-inflammatory compounds, we screened a library of pyridazinones and structurally related derivatives that were used previously to identify N-formyl peptide receptor (FPR) agonists. Screening of the compounds for their ability to inhibit lipopolysaccharide (LPS)-induced nuclear factor κB (NF-κB) transcriptional activity in human THP1-Blue monocytic cells identified 48 compounds with anti-inflammatory activity. Interestingly, 34 compounds were FPR agonists, whereas 14 inhibitors of LPS-induced NF-κB activity were not FPR agonists, indicating that they inhibited different signaling pathways. Further analysis of the most potent inhibitors showed that they also inhibited LPS-induced production of interleukin 6 (IL-6) by human MonoMac-6 monocytic cells, again verifying their anti-inflammatory properties. Structure–activity relationship (SAR) classification models based on atom pair descriptors and physicochemical ADME parameters were developed to achieve better insight into the relationships between chemical structures of the compounds and their biological activities, and we found that there was little correlation between FPR agonist activity and inhibition of LPS-induced NF-κB activity. Indeed, Cmpd43, a well-known pyrazolone-based FPR agonist, as well as FPR1 and FPR2 peptide agonists had no effect on the LPS-induced NF-κB activity in THP1-Blue cells. Thus, some FPR agonists reported to have anti-inflammatory activity may actually mediate their effects through FPR-independent pathways, as it is suggested by our results with this series of compounds. This could explain how treatment with some agonists known to be inflammatory (i.e., FPR1 agonists) could result in anti-inflammatory effects. Further research is clearly needed to define the molecular targets of pyridazinones and structurally related compounds with anti-inflammatory activity and to define their relationships (if any) to FPR signaling events.
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Režen T, Martins A, Mraz M, Zimic N, Rozman D, Moškon M. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models. Comput Biol Med 2022; 145:105428. [PMID: 35339845 PMCID: PMC8940269 DOI: 10.1016/j.compbiomed.2022.105428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 12/16/2022]
Abstract
COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.
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Affiliation(s)
- Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Nikolaj Zimic
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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The lung microbiome in HIV-positive patients with active pulmonary tuberculosis. Sci Rep 2022; 12:8975. [PMID: 35643931 PMCID: PMC9148312 DOI: 10.1038/s41598-022-12970-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 05/19/2022] [Indexed: 11/08/2022] Open
Abstract
Tuberculosis poses one of the greatest infectious disease threats of our time, especially when associated with human immunodeficiency virus (HIV) infection. Very little data is available on the lung microbiome in pulmonary tuberculosis (PTB) in HIV-positive patients. Three patient cohorts were studied: (i) HIV-positive with no respiratory disease (control cohort), (ii) HIV-positive with pneumonia and (iii) HIV-positive with PTB. Sputum specimens were collected in all patients and where possible a paired BALF was collected. DNA extraction was performed using the QIAamp DNA mini kit (QIAGEN, Germany) and extracted DNA specimens were sent to Inqaba Biotechnical Industries (Pty) Ltd for 16S rRNA gene sequence analysis using the Illumina platform (Illumina Inc, USA). Data analysis was performed using QIMME II and R Studio version 3.6.2 (2020). The lung microbiomes of patients with PTB, in the context of HIV co-infection, were dominated by Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Loss of biodiversity and dysbiosis was found in these patients when compared to the HIV-positive control cohort. Microbial community structure was also distinct from the control cohort, with the dominance of genera such as Achromobacter, Mycobacterium, Acinetobacter, Stenotrophomonas and Pseudomonas in those patients with PTB. This is the first study to describe the lung microbiome in patients with HIV and PTB co-infection and to compare findings with an HIV-positive control cohort. The lung microbiomes of patients with HIV and PTB were distinct from the HIV-positive control cohort without PTB, with an associated loss of microbial diversity.
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