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Barratt DT, Klepstad P, Dale O, Kaasa S, Somogyi AA. Pharmacokinetic and neuroimmune pharmacogenetic impacts on slow-release morphine cancer pain control and adverse effects. THE PHARMACOGENOMICS JOURNAL 2024; 24:18. [PMID: 38824169 PMCID: PMC11144121 DOI: 10.1038/s41397-024-00339-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 05/21/2024] [Accepted: 05/21/2024] [Indexed: 06/03/2024]
Abstract
The aim was to determine if opioid neuroimmunopharmacology pathway gene polymorphisms alter serum morphine, morphine-3-glucuronide and morphine-6-glucuronide concentration-response relationships in 506 cancer patients receiving controlled-release oral morphine. Morphine-3-glucuronide concentrations (standardised to 11 h post-dose) were higher in patients without pain control (median (interquartile range) 1.2 (0.7-2.3) versus 1.0 (0.5-1.9) μM, P = 0.006), whereas morphine concentrations were higher in patients with cognitive dysfunction (40 (20-81) versus 29 (14-60) nM, P = 0.02). TLR2 rs3804100 variant carriers had reduced odds (adjusted odds ratio (95% confidence interval) 0.42 (0.22-0.82), P = 0.01) of opioid adverse events. IL2 rs2069762 G/G (0.20 (0.06-0.52)), BDNF rs6265 A/A (0.15 (0.02-0.63)) and IL6R rs8192284 carrier (0.55 (0.34-0.90)) genotypes had decreased, and IL6 rs10499563 C/C increased (3.3 (1.2-9.3)), odds of sickness response (P ≤ 0.02). The study has limitations in heterogeneity in doses, sampling times and diagnoses but still suggests that pharmacokinetics and immune genetics co-contribute to morphine pain control and adverse effects in cancer patients.
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Affiliation(s)
- Daniel T Barratt
- Discipline of Pharmacology, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia.
- Discipline of Physiology, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia.
| | - Pål Klepstad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anaesthesiology and Intensive Care Medicine, St Olavs University Hospital, Trondheim, Norway
| | - Ola Dale
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stein Kaasa
- Department of Oncology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Andrew A Somogyi
- Discipline of Pharmacology, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia
- Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, Australia
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202
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CHU M, WANG Y, LIN Z, Lyu J, ZHANG X, ZHANG B. Investigation of the active ingredients and mechanism of Shuangling extract in dextran sulfate sodium salt induced ulcerative colitis mice based on network pharmacology and experimental verification. J TRADIT CHIN MED 2024; 44:478-488. [PMID: 38767631 PMCID: PMC11077278 DOI: 10.19852/j.cnki.jtcm.20240408.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/15/2023] [Indexed: 05/22/2024]
Abstract
OBJECTIVE To explore the pharmacodynamic effects and potential mechanisms of Shuangling extract against ulcerative colitis (UC). METHODS The bioinformatics method was used to predict the active ingredients and action targets of Shuangling extract against UC in mice. And the biological experiments such as serum biochemical indexes and histopathological staining were used to verify the pharmacological effect and mechanism of Shuangling extract against UC in mice. RESULTS The Shuangling extract reduced the levels of seruminterleukin-1β (IL-1β), tumor necrosis factor-α (TNF-N), interleukin-6 (IL-6) and other inflammatory factors in UC mice and inhibited the inflammatory response. AKT Serine/threonine Kinase 1 and IL-6 may be the main targets of the anti-UC action of Shuangling extract, and the TNF signaling pathway, Forkhead box O signaling pathway and T-cell receptor signaling pathway may be the main signaling pathways. CONCLUSION The Shuangling extract could inhibit the inflammatory response induced by UC and regulate intestinal immune function through multiple targets and multiple channels, which provided a new option and theoretical basis for anti-UC.
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Affiliation(s)
- Mengzhen CHU
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Yu WANG
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Zhijian LIN
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Jintao Lyu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Xiaomeng ZHANG
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Bing ZHANG
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
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203
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Hu H, Zhang Y, Zhai H, Dong J, Zuo L, Guo X, Wang C. P300 reduces TUBB4B expression to facilitate the biological process of migration and invasion of non-small cell lung cancer cells. Tissue Cell 2024; 88:102386. [PMID: 38636368 DOI: 10.1016/j.tice.2024.102386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
This article explored the mechanism of E1A binding protein p300 (P300) and beta-tubulin 4B isotype-encoding gene (TUBB4B) in regulating the migration and invasion of non-small cell lung cancer (NSCLC) cells. TUBB4B and P300 expression in NSCLC tissues and cells was monitored by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot. TUBB4B function on NSCLC cell migration, invasion and epithelial-mesenchymal transition (EMT) was monitored by wound healing assay, Transwell experiment and Western blot. The regulation of P300 on TUBB4B was monitored by qRT-PCR and Western blot. Mechanism of P300 and TUBB4B in regulating NSCLC cell migration and invasion was explored by rescue experiment. A xenograft tumor model was established by using nude mouse. As a result, low TUBB4B expression and high P300 expression was discovered in NSCLC tissues and cells. TUBB4B and P300 expression showed a negative correlation in NSCLC tissues. Lower TUBB4B but higher P300 was observed in tumor tissues of NSCLC patients with metastasis. TUBB4B overexpression suppressed NSCLC cell migration, invasion and EMT. TUBB4B silencing had opposite results. P300 overexpression inhibited TUBB4B expression, and P300 silencing facilitated TUBB4B overexpression in NSCLC cells. TUBB4B overexpression counteracted the promotion of P300 overexpression on NSCLC cell invasion and migration. TUBB4B silencing abrogated the inhibition of P300 knockdown on NSCLC cell invasion and migration. TUBB4B overexpression suppressed NSCLC cell in vivo growth. Thus, TUBB4B could be reduced by P300 in NSCLC. It exerted suppression role on NSCLC cell migration, invasion and EMT. TUBB4B may be a novel target for NSCLC treatment.
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Affiliation(s)
- Haifeng Hu
- Thoracic Surgery, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China
| | - Yun Zhang
- Medical Oncology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China
| | - Haibo Zhai
- Thoracic Surgery, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China
| | - Juanjuan Dong
- Medical Oncology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China
| | - Lei Zuo
- Thoracic Surgery, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China
| | - Xiaolei Guo
- Medical Oncology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China
| | - Chao Wang
- Medical Oncology, Chaohu Hospital of Anhui Medical University, Chaohu 238000, China.
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204
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Garley M, Nowak K, Jabłońska E. Neutrophil microRNAs. Biol Rev Camb Philos Soc 2024; 99:864-877. [PMID: 38148491 DOI: 10.1111/brv.13048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 12/28/2023]
Abstract
Neutrophils are considered 'first-line defence' cells as they can be rapidly recruited to the site of the immune response. As key components of non-specific immune mechanisms, neutrophils use phagocytosis, degranulation, and formation of neutrophil extracellular traps (NETs) to fight pathogens. Recently, immunoregulatory abilities of neutrophils associated with the secretion of several mediators, including cytokines and extracellular vesicles (EVs) containing, among other components, microRNAs (miRNAs), have also been reported. EVs are small structures released by cells into the extracellular space and are present in all body fluids. Microvesicles show the composition and status of the releasing cell, its physiological state, and pathological changes. Currently, EVs have gained immense scientific interest as they act as transporters of epigenetic information in intercellular communication. This review summarises findings from recent scientific reports that have evaluated the utility of miRNA molecules as biomarkers for effective diagnostics or even as start-points for new therapeutic strategies in neutrophil-mediated immune reactions. In addition, this review describes the current state of knowledge on miRNA molecules, which are endogenous regulators of gene expression besides being involved in the regulation of the immune response.
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Affiliation(s)
- Marzena Garley
- Department of Immunology, Medical University of Bialystok, Waszyngtona 15A, Bialystok, 15-269, Poland
| | - Karolina Nowak
- Department of Obstetrics and Gynecology, C.S. Mott Center for Human Growth and Development, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Ewa Jabłońska
- Department of Immunology, Medical University of Bialystok, Waszyngtona 15A, Bialystok, 15-269, Poland
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205
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Talukdar S, Mal S, Kundu P. Physico-chemical features and functional relevance of tomato rhomboid proteases. Int J Biol Macromol 2024; 272:132681. [PMID: 38806088 DOI: 10.1016/j.ijbiomac.2024.132681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/13/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
In plants, regulated intramembrane proteolysis (RIP) is crucial for proper growth, development, and stress management. Rhomboid proteases (RPs) residing in the membrane play a vital role in orchestrating RIP. Although RPs can be found in most sequenced genomes, tomato rhomboids (SlRPs) have not yet been studied. Using alternative and comprehensive strategies, we found ten SlRPs encoded in the tomato genome. These SlRPs possess signature motifs and transmembrane domains, showing structural similarity to other members of the RP family. Also, SlRPs are genetically related to other known RPs of the Solanaceae family. Seven of the SlRPs retain serine-histidine catalytic dyads, making them proteolytically active, while three iRhoms lack the dyad and other structural motifs. Although SlRPs could have functional redundancy, their distribution and expression pattern indicate tissue specificity and responsiveness to specific external stimuli. The presence of development and stress-response-related cis-elements in the promoters of SlRPs supports this view. Furthermore, our strategically designed substrate-reporter assay shows that SlRPs have proteolytic activity similar to that of known RPs. This study provides a detailed understanding of all SlRPs and their physico-chemical features, shedding light on their involvement in physiological processes.
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Affiliation(s)
- Sushmita Talukdar
- Department of Biological Sciences, Bose Institute, EN80, Sector V, Bidhannagar, Kolkata 700091, India
| | - Sayan Mal
- Department of Biological Sciences, Bose Institute, EN80, Sector V, Bidhannagar, Kolkata 700091, India
| | - Pallob Kundu
- Department of Biological Sciences, Bose Institute, EN80, Sector V, Bidhannagar, Kolkata 700091, India.
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206
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Qi Z, Liu C, Wang N, Cui J, Hu J, Gu R, Meng L, Wang P, Zhai J, Shui G, Cui S. The dehydration-responsive protein PpFAS1.3 in moss Physcomitrium patens plays a regulatory role in lipid metabolism. JOURNAL OF PLANT PHYSIOLOGY 2024; 297:154253. [PMID: 38703549 DOI: 10.1016/j.jplph.2024.154253] [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: 02/20/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 05/06/2024]
Abstract
Moss plants appear in the early stages of land colonization and possess varying degrees of dehydration tolerance. In this study, a protein called PpFAS1.3 was identified, which contains a fasciclin 1-like domain and is essential for the moss Physcomitrium patens' response to short-term rapid dehydration. When the FAS1.3 protein was knocked out, leafyshoots showed a significant decrease in tolerance to rapid dehydration, resulting in accelerated water loss and increased membrane leakage. Phylogenetic analysis suggests that PpFAS1.3 and its homologous proteins may have originated from bacteria and are specifically found in non-vascular plants like mosses and liverworts. As a dehydration-related protein, FAS1.3 plays a significant role in regulating lipid metabolism, particularly in the synthesis of free fatty acids (FFA) and the metabolism of two phospholipids, PC and PA. This discovery highlights the close connection between PpFAS1.3 and lipid metabolism, providing new insights into the molecular mechanisms underlying plant adaptation to stresses.
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Affiliation(s)
- Zhenyu Qi
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China
| | - Chen Liu
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China
| | - Ning Wang
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China
| | - Jipeng Cui
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China
| | - Jia Hu
- Central Laboratory, Capital Medical University, Beijing, 100029, China
| | - Ruoqing Gu
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China
| | - Le Meng
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China
| | - Pan Wang
- College of Life Sciences, Capital Normal University, Beijing, 100048, China
| | - Jianan Zhai
- College of Life Sciences, Capital Normal University, Beijing, 100048, China
| | - Guanghou Shui
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Suxia Cui
- College of Life Sciences, Capital Normal University, Beijing, 100048, China; Beijing Key Laboratory of Plant Gene Resources and Biotechnology for Carbon Reduction and Environmental Improvement, Beijing, 100048, China.
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207
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Nakamura Y, Yasuike M, Fuji T, Suyama S, Mekuchi M. Draft genome sequence and tissue expression panel of Pacific saury (Cololabis saira). DNA Res 2024; 31:dsae010. [PMID: 38566577 PMCID: PMC11077904 DOI: 10.1093/dnares/dsae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/08/2024] [Accepted: 04/02/2024] [Indexed: 04/04/2024] Open
Abstract
Pacific saury (Cololabis saira) is an important fish in several countries. Notably, the catch of this fish has markedly decreased recently, which might be due to environmental changes, including feeding habitat changes. However, no clear correlation has been observed. Therefore, the physiological basis of Pacific saury in relation to possible environmental factors must be understood. We sequenced the genome of Pacific saury and extracted RNA from nine tissues (brain, eye, gill, anterior/posterior guts, kidney, liver, muscle, and ovary). In 1.09 Gb assembled genome sequences, a total of 26,775 protein-coding genes were predicted, of which 26,241 genes were similar to known genes in a public database. Transcriptome analysis revealed that 24,254 genes were expressed in at least one of the nine tissues, and 7,495 were highly expressed in specific tissues. Based on the similarity of the expression profiles to those of model organisms, the transcriptome obtained was validated to reflect the characteristics of each tissue. Thus, the present genomic and transcriptomic data serve as useful resources for molecular studies on Pacific saury. In particular, we emphasize that the gene expression data, which serve as the tissue expression panel of this species, can be employed in comparative transcriptomics on marine environmental responses.
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Affiliation(s)
- Yoji Nakamura
- Bioinformatics and Biosciences Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 2-12-4 Fuku-ura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan
| | - Motoshige Yasuike
- Bioinformatics and Biosciences Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 2-12-4 Fuku-ura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan
| | - Taiki Fuji
- Highly Migratory Resources Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 2-12-4 Fuku-ura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan
| | - Satoshi Suyama
- Highly Migratory Resources Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 25-259 Shimomekurakubo, Same, Hachinohe, Aomori 031-0841, Japan
| | - Miyuki Mekuchi
- Bioinformatics and Biosciences Division, Fisheries Stock Assessment Center, Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 2-12-4 Fuku-ura, Kanazawa, Yokohama, Kanagawa 236-8648, Japan
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208
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Cao Z, Zhan G, Qin J, Cupertino RB, Ottino-Gonzalez J, Murphy A, Pancholi D, Hahn S, Yuan D, Callas P, Mackey S, Garavan H. Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics. Neuroimage 2024; 293:120622. [PMID: 38648869 PMCID: PMC11132826 DOI: 10.1016/j.neuroimage.2024.120622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.
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Affiliation(s)
- Zhipeng Cao
- Shanghai Xuhui Mental Health Center, Shanghai 200232, China; Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA.
| | - Guilai Zhan
- Shanghai Xuhui Mental Health Center, Shanghai 200232, China
| | - Jinmei Qin
- Shanghai Xuhui Mental Health Center, Shanghai 200232, China
| | - Renata B Cupertino
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Jonatan Ottino-Gonzalez
- Division of Endocrinology, The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Alistair Murphy
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Devarshi Pancholi
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Sage Hahn
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Dekang Yuan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Peter Callas
- Department of Mathematics and Statistics, University of Vermont College of Engineering and Mathematical Sciences, Burlington VT, 05401, USA
| | - Scott Mackey
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
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Ma X, Han X, Zhang L. An Improved k-Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:1025-1036. [PMID: 38400537 DOI: 10.1002/jum.16429] [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: 07/22/2023] [Revised: 01/17/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced. METHODS An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure. RESULTS A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results. CONCLUSIONS A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.
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Affiliation(s)
- Xuesi Ma
- School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, China
| | - Xiang Han
- School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, China
| | - Lina Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
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210
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Li J, Fan Y, Li N, Guo Y, Wang W, Feng K, He W, Li F, Huang J, Xu Y, Xiao L, Feng Y. Comparative genomics analysis reveals sequence characteristics potentially related to host preference in Cryptosporidium xiaoi. Int J Parasitol 2024; 54:379-390. [PMID: 38492779 DOI: 10.1016/j.ijpara.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/26/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024]
Abstract
Cryptosporidium spp. are important diarrhea-associated pathogens in humans and livestock. Among the known species, Cryptosporidium xiaoi, which causes cryptosporidiosis in sheep and goats, was previously recognized as a genotype of the bovine-specific Cryptosporidium bovis based on their high sequence identity in the ssrRNA gene. However, the lack of genomic data has limited characterization of the genetic differences between the two closely related species. In this study, we sequenced the genomes of two C. xiaoi isolates and performed comparative genomic analysis to identify the sequence uniqueness of this ovine-adapted species compared with other Cryptosporidium spp. Our results showed that C. xiaoi is genetically related to C. bovis as shown by their 95.8% genomic identity and similar gene content. Consistent with this, both C. xiaoi and C. bovis appear to have fewer genes encoding mitochondrial metabolic enzymes and invasion-related protein families. However, they appear to possess several species-specific genes. Further analysis indicates that the sequence differences between these two Cryptosporidium spp. are mainly in 24 highly polymorphic genes, half of which are located in the subtelomeric regions. Some of these subtelomeric genes encode secretory proteins that have undergone positive selection. In addition, the genomes of two C. xiaoi isolates, identified as subtypes XXIIIf and XXIIIh, share 99.9% nucleotide sequence identity, with six highly divergent genes encoding putative secretory proteins. Therefore, these species-specific genes and sequence polymorphism in subtelomeric genes probably contribute to the different host preference of C. xiaoi and C. bovis.
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Affiliation(s)
- Jiayu Li
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Yingying Fan
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Na Li
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Yaqiong Guo
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Weijian Wang
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Kangli Feng
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Wei He
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Falei Li
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Jianbo Huang
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Yanhua Xu
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China
| | - Lihua Xiao
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China.
| | - Yaoyu Feng
- State Key Laboratory for Animal Disease Control and Prevention, South China Agricultural University, Guangzhou 510642, China.
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Huangfu X, Zhang C, Li H, Li S, Li Y. SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations. Comput Biol Chem 2024; 110:108054. [PMID: 38522389 DOI: 10.1016/j.compbiolchem.2024.108054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/22/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.
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Affiliation(s)
- Xiaosheng Huangfu
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Chengwei Zhang
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Hualong Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China; School of mathematics and statistics, Guangdong University of Technology, Guangzhou 510520, China
| | - Sile Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China.
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212
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Wang B, Ma F, Du X, Zhang G, Li J. Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest. Front Microbiol 2024; 15:1394302. [PMID: 38881658 PMCID: PMC11176502 DOI: 10.3389/fmicb.2024.1394302] [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: 03/01/2024] [Accepted: 05/10/2024] [Indexed: 06/18/2024] Open
Abstract
Introduction The identification of microbe-drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them ignore the combination of abundant sequence, structural information, and microbe-drug network topology. Methods In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe-drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features. Results The experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively. Discussion We conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations.
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Affiliation(s)
- Bo Wang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
- Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, China
| | - Fangjian Ma
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Xiaoxin Du
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Guangda Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Jingyou Li
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
- Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, China
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213
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Acharjee A, Okyere D, Nath D, Nagar S, Gkoutos GV. Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer. Heliyon 2024; 10:e31437. [PMID: 38803850 PMCID: PMC11128524 DOI: 10.1016/j.heliyon.2024.e31437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that typically manifests late patient presentation and poor outcomes. Furthermore, PDAC recurrence is a common challenge. Distinct patterns of PDAC recurrence have been associated with differential activation of immune pathway-related genes and specific inflammatory responses in their tumour microenvironment. However, the molecular associations between and within cellular components that underpin PDAC recurrence require further development, especially from a multi-omics integration perspective. In this study, we identified stable molecular associations across multiple PDAC recurrences and utilised integrative analytics to identify stable and novel associations via simultaneous feature selection. Spatial transcriptome and proteome datasets were used to perform univariate analysis, Spearman partial correlation analysis, and univariate analyses by Machine Learning methods, including regularised canonical correlation analysis and sparse partial least squares. Furthermore, networks were constructed for reported and new stable associations. Our findings revealed gene and protein associations across multiple PDAC recurrence groups, which can provide a better understanding of the multi-layer disease mechanisms that contribute to PDAC recurrence. These findings may help to provide novel association targets for clinical studies for constructing precision medicine and personalised surveillance tools for patients with PDAC recurrence.
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Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- MRC Health Data Research UK (HDR UK), Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, United Kingdom
- Centre for Health Data Research, University of Birmingham, B15 2TT, United Kingdom
| | - Daniella Okyere
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Dipanwita Nath
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Shruti Nagar
- Eureka Tutorials, Muzaffarnagar, U.P., 251201, India
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- MRC Health Data Research UK (HDR UK), Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, United Kingdom
- Centre for Health Data Research, University of Birmingham, B15 2TT, United Kingdom
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214
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Yi M, Zhou F, Deng Y. STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention. Front Genet 2024; 15:1408688. [PMID: 38873109 PMCID: PMC11169723 DOI: 10.3389/fgene.2024.1408688] [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: 04/05/2024] [Accepted: 05/14/2024] [Indexed: 06/15/2024] Open
Abstract
N4-acetylcysteine (ac4C) is a chemical modification in mRNAs that alters the structure and function of mRNA by adding an acetyl group to the N4 position of cytosine. Researchers have shown that ac4C is closely associated with the occurrence and development of various cancers. Therefore, accurate prediction of ac4C modification sites on human mRNA is crucial for revealing its role in diseases and developing new diagnostic and therapeutic strategies. However, existing deep learning models still have limitations in prediction accuracy and generalization ability, which restrict their effectiveness in handling complex biological sequence data. This paper introduces a deep learning-based model, STM-ac4C, for predicting ac4C modification sites on human mRNA. The model combines the advantages of selective kernel convolution, temporal convolutional networks, and multi-head self-attention mechanisms to effectively extract and integrate multi-level features of RNA sequences, thereby achieving high-precision prediction of ac4C sites. On the independent test dataset, STM-ac4C showed improvements of 1.81%, 3.5%, and 0.37% in accuracy, Matthews correlation coefficient, and area under the curve, respectively, compared to the existing state-of-the-art technologies. Moreover, its performance on additional balanced and imbalanced datasets also confirmed the model's robustness and generalization ability. Various experimental results indicate that STM-ac4C outperforms existing methods in predictive performance. In summary, STM-ac4C excels in predicting ac4C modification sites on human mRNA, providing a powerful new tool for a deeper understanding of the biological significance of mRNA modifications and cancer treatment. Additionally, the model reveals key sequence features that influence the prediction of ac4C sites through sequence region impact analysis, offering new perspectives for future research. The source code and experimental data are available at https://github.com/ymy12341/STM-ac4C.
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Affiliation(s)
- Mengyue Yi
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Fenglin Zhou
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
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215
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Kim C, Kwon JM, Lee J, Jo H, Gwon D, Jang JH, Sung MK, Park SW, Kim C, Oh MY. Deep learning model integrating radiologic and clinical data to predict mortality after ischemic stroke. Heliyon 2024; 10:e31000. [PMID: 38826743 PMCID: PMC11141274 DOI: 10.1016/j.heliyon.2024.e31000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 06/04/2024] Open
Abstract
Objective Most prognostic indexes for ischemic stroke mortality lack radiologic information. We aimed to create and validate a deep learning-based mortality prediction model using brain diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), and clinical factors. Methods Data from patients with ischemic stroke who admitted to tertiary hospital during acute periods from 2013 to 2019 were collected and split into training (n = 1109), validation (n = 437), and internal test (n = 654). Data from patients from secondary cardiovascular center was used for external test set (n = 507). The algorithm for predicting mortality, based on DWI and ADC (DLP_DWI), was initially trained. Subsequently, important clinical factors were integrated into this model to create the integrated model (DLP_INTG). The performance of DLP_DWI and DLP_INTG was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) and Harrell concordance index (C-index) at one-year mortality. Results The TD AUC of DLP_DWI was 0.643 in internal test set, and 0.785 in the external dataset. DLP_INTG had a higher performance at predicting one-year mortality than premise score in internal dataset (TD- AUC: 0.859 vs. 0.746; p = 0.046), and in external dataset (TD- AUC: 0.876 vs. 0.808; p = 0.007). DLP_DWI and DLP_INTG exhibited strong discrimination for the high-risk group for one-year mortality. Interpretation A deep learning model using brain DWI, ADC and the clinical factors was capable of predicting mortality in patients with ischemic stroke.
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Affiliation(s)
- Changi Kim
- Department of Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Joon-myoung Kwon
- Medical Research Team, Medical AI Inc, DC, USA
- Department of Critical Care Emergency Medicine, Incheon Sejong Hospital, Incheon, Republic of Korea
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
| | - Jiyeong Lee
- Department of Neurology, Bucheon Sejong Hospital, Bucheon, Republic of Korea
| | | | - Dowan Gwon
- Department of Digital&Biohealth, Group of AI/DX Business, KT, Seoul, Republic of Korea
| | - Jae Hoon Jang
- Department of Family Medicine, College of Medicine, KyungHee University, Seoul, Republic of Korea
| | - Min Kyu Sung
- Department of Family Medicine, College of Medicine, KyungHee University, Seoul, Republic of Korea
| | - Sang Won Park
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
- Institute of Medical Science, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Chulho Kim
- Department of Neurology, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Mi-Young Oh
- Department of Neurology, Bucheon Sejong Hospital, Bucheon, Republic of Korea
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216
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Pratiwi NKC, Tayara H, Chong KT. An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction. Int J Mol Sci 2024; 25:5957. [PMID: 38892144 PMCID: PMC11172808 DOI: 10.3390/ijms25115957] [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: 04/22/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.
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Affiliation(s)
- Nor Kumalasari Caecar Pratiwi
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Department of Electrical Engineering, Telkom University, Bandung 40257, West Java, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju 54896, Republic of Korea
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217
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Kotlyarov R, Papachristos K, Wood GPF, Goodman JM. Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity. J Chem Inf Model 2024; 64:4286-4297. [PMID: 38708520 PMCID: PMC11134489 DOI: 10.1021/acs.jcim.4c00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/05/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024]
Abstract
C-H borylation is a high-value transformation in the synthesis of lead candidates for the pharmaceutical industry because a wide array of downstream coupling reactions is available. However, predicting its regioselectivity, especially in drug-like molecules that may contain multiple heterocycles, is not a trivial task. Using a data set of borylation reactions from Reaxys, we explored how a language model originally trained on USPTO_500_MT, a broad-scope set of patent data, can be used to predict the C-H borylation reaction product in different modes: product generation and site reactivity classification. Our fine-tuned T5Chem multitask language model can generate the correct product in 79% of cases. It can also classify the reactive aromatic C-H bonds with 95% accuracy and 88% positive predictive value, exceeding purpose-developed graph-based neural networks.
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Affiliation(s)
- Ruslan Kotlyarov
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
| | | | - Geoffrey P. F. Wood
- Exscientia
Plc, The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
| | - Jonathan M. Goodman
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, U.K.
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218
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Casaro S, Prim JG, Gonzalez TD, Cunha F, Bisinotto RS, Chebel RC, Santos JEP, Nelson CD, Jeon SJ, Bicalho RC, Driver JP, Galvão KN. Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis. Anim Microbiome 2024; 6:30. [PMID: 38802977 PMCID: PMC11131188 DOI: 10.1186/s42523-024-00314-7] [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: 01/25/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Metritis is a prevalent uterine disease that affects the welfare, fertility, and survival of dairy cows. The uterine microbiome from cows that develop metritis and those that remain healthy do not differ from calving until 2 days postpartum, after which there is a dysbiosis of the uterine microbiome characterized by a shift towards opportunistic pathogens such as Fusobacteriota and Bacteroidota. Whether these opportunistic pathogens proliferate and overtake the uterine commensals could be determined by the type of substrates present in the uterus. The objective of this study was to integrate uterine microbiome and metabolome data to advance the understanding of the uterine environment in dairy cows that develop metritis. Holstein cows (n = 104) had uterine fluid collected at calving and at the day of metritis diagnosis. Cows with metritis (n = 52) were paired with cows without metritis (n = 52) based on days after calving. First, the uterine microbiome and metabolome were evaluated individually, and then integrated using network analyses. RESULTS The uterine microbiome did not differ at calving but differed on the day of metritis diagnosis between cows with and without metritis. The uterine metabolome differed both at calving and on the day of metritis diagnosis between cows that did and did not develop metritis. Omics integration was performed between 6 significant bacteria genera and 153 significant metabolites on the day of metritis diagnosis. Integration was not performed at calving because there were no significant differences in the uterine microbiome. A total of 3 bacteria genera (i.e. Fusobacterium, Porphyromonas, and Bacteroides) were strongly correlated with 49 metabolites on the day of metritis diagnosis. Seven of the significant metabolites at calving were among the 49 metabolites strongly correlated with opportunistic pathogenic bacteria on the day of metritis diagnosis. The main metabolites have been associated with attenuation of biofilm formation by commensal bacteria, opportunistic pathogenic bacteria overgrowth, tissue damage and inflammation, immune evasion, and immune dysregulation. CONCLUSIONS The data integration presented herein helps advance the understanding of the uterine environment in dairy cows with metritis. The identified metabolites may provide a competitive advantage to the main uterine pathogens Fusobacterium, Porphyromonas and Bacteroides, and may be promising targets for future interventions aiming to reduce opportunistic pathogenic bacteria growth in the uterus.
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Affiliation(s)
- S Casaro
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - J G Prim
- Department of Clinical Sciences, Auburn University, Auburn, AL, USA
| | - T D Gonzalez
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - F Cunha
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - R S Bisinotto
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - R C Chebel
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA
| | - J E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
- D. H. Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, FL, USA
| | - C D Nelson
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
| | - S J Jeon
- Department of Veterinary Biomedical Sciences, Long Island University, Brookville, NY, USA
| | - R C Bicalho
- FERA Diagnostics and Biologicals, College Station, TX, USA
| | - J P Driver
- Division of Animals Sciences, University of Missouri, Columbia, MO, USA
| | - Klibs N Galvão
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, USA.
- D. H. Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, FL, USA.
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D'Alonzo BA, Bretzin AC, Schneider AL, Morse RB, Canelón SP, Wiebe DJ, Boland MR. Comparison of different definitions of traumatic brain injury: implications for cohort characteristics and survival in women, Philadelphia, USA. Inj Prev 2024:ip-2023-045069. [PMID: 38802243 DOI: 10.1136/ip-2023-045069] [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: 08/04/2023] [Accepted: 04/29/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Traumatic brain injury (TBI) is an acute injury that is understudied in civilian cohorts, especially among women, as TBI has historically been considered to be largely a condition of athletes and military service people. Both the Centres for Disease Control and Prevention (CDC) and Department of Defense (DOD)/Veterans Affairs (VA) have developed case definitions to identify patients with TBI from medical records; however, their definitions differ. We sought to re-examine these definitions to construct an expansive and more inclusive definition among a cohort of women with TBI. METHODS In this study, we use electronic health records (EHR) from a single healthcare system to study the impact of using different case definitions to identify patients with TBI. Specifically, we identified adult female patients with TBI using the CDC definition, DOD/VA definition and a combined and expanded definition herein called the Penn definition. RESULTS We identified 4446 adult-female TBI patients meeting the CDC definition, 3619 meeting the DOD/VA definition, and together, 6432 meeting our expanded Penn definition that includes the CDC ad DOD/VA definitions. CONCLUSIONS Using the expanded definition identified almost two times as many patients, enabling investigations to more fully characterise these patients and related outcomes. Our expanded TBI case definition is available to other researchers interested in employing EHRs to investigate TBI.
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Affiliation(s)
- Bernadette A D'Alonzo
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Abigail C Bretzin
- Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrea Lc Schneider
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Rebecca B Morse
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Silvia P Canelón
- Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Douglas J Wiebe
- Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, USA
- University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Mary Regina Boland
- Department of Mathematics, Saint Vincent College, Latrobe, Pennsylvania, USA
- Department of Marketing, Analytics and Global Commerce, Saint Vincent College, Latrobe, Pennsylvania, USA
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220
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Verma P, Sánchez Alvarado A, Duncan EM. Chromatin remodeling protein BPTF regulates transcriptional stability in planarian stem cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595819. [PMID: 38826365 PMCID: PMC11142235 DOI: 10.1101/2024.05.24.595819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Trimethylation of histone H3 lysine 4 (H3K4me3) correlates strongly with gene expression in many different organisms, yet the question of whether it plays a causal role in transcriptional activity remains unresolved. Although H3K4me3 does not directly affect chromatin accessibility, it can indirectly affect genome accessibility by recruiting the ATP-dependent chromatin remodeling complex NuRF (Nucleosome Remodeling Factor). The largest subunit of NuRF, BPTF/NURF301, binds H3K4me3 specifically and recruits the NuRF complex to loci marked by this modification. Studies have shown that the strength and duration of BPTF binding likely also depends on additional chromatin features at these loci, such as lysine acetylation and variant histone proteins. However, the exact details of this recruitment mechanism vary between studies and have largely been tested in vitro. Here, we use stem cells isolated directly from live planarian animals to investigate the role of BPTF in regulating chromatin accessibility in vivo. We find that BPTF operates at gene promoters and is most effective at facilitating transcription at genes marked by Set1-dependent H3K4me3 peaks, which are significantly broader than those added by the lysine methyltransferase MLL1/2. Moreover, BPTF is essential for planarian stem cell biology and its loss of function phenotype mimics that of Set1 knockdown. Together, these data suggest that BPTF and H3K4me3 are important mediators of both transcription and in vivo stem cell function.
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221
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Luo G, Gao M, Lin Q. Integration of bioinformatics analysis, molecular docking and animal experiments to study the therapeutic mechanisms of berberine against allergic rhinitis. Sci Rep 2024; 14:11999. [PMID: 38796469 PMCID: PMC11127925 DOI: 10.1038/s41598-024-60871-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/29/2024] [Indexed: 05/28/2024] Open
Abstract
Allergic rhinitis is a prevalent inflammatory condition that impacts individuals of all age groups. Despite reports indicating the potential of berberine in alleviating allergic rhinitis symptoms, the specific molecular mechanisms and therapeutic targets of berberine remain unclear. This research aims to explore the pharmacological mechanism of berberine in the treatment of allergic rhinitis through bioinformatic analyses and experimental validation. The research utilized public databases to identify potential targets of berberine. Furthermore, differentially expressed genes (DEGs) related to allergic rhinitis were pinpointed from the GSE52804 dataset. Through bioinformatics techniques, the primary targets were discovered and key KEGG and GO-BP pathways were established. To confirm the therapeutic mechanisms of berberine on allergic rhinitis, an OVA-induced allergic rhinitis model was developed using guinea pigs. We identified 32 key genes responsible for the effectiveness of berberine in treating allergic rhinitis. In addition, five central genes (Alb, Il6, Tlr4, Ptas2, and Il1b) were pinpointed. Further examination using KEGG and GO-BP pathways revealed that the main targets were primarily involved in pathways such as NF-kappa B, IL-17, TNF, and inflammatory response. Molecular docking analysis demonstrated that berberine exhibited strong affinity towards these five key targets. Furthermore, the expression levels of IL-6, TLR4, PTGS2, and IL-1β were significantly upregulated in the model group but downregulated following berberine treatment. This research has revealed the mechanism through which berberine combats allergic rhinitis and has identified its potential to regulate pathways linked to inflammation. These discoveries provide valuable insights for the development of novel medications for the treatment of allergic rhinitis.
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Affiliation(s)
- Gaoqing Luo
- Department of Otolaryngology, Fujian Provincial Governmental Hospital, Fuzhou, 350003, China.
| | - Ming Gao
- Department of Otolaryngology, People's Hospital of Changji Hui Autonomous Prefecture, Changji, 831100, China
| | - Qinghua Lin
- Department of Otolaryngology, Fujian Provincial Governmental Hospital, Fuzhou, 350003, China
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Gabud R, Lapitan P, Mariano V, Mendoza E, Pampolina N, Clariño MAA, Batista-Navarro R. Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species. Front Artif Intell 2024; 7:1371411. [PMID: 38845683 PMCID: PMC11153722 DOI: 10.3389/frai.2024.1371411] [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: 01/16/2024] [Accepted: 05/10/2024] [Indexed: 06/09/2024] Open
Abstract
Introduction Fine-grained, descriptive information on habitats and reproductive conditions of plant species are crucial in forest restoration and rehabilitation efforts. Precise timing of fruit collection and knowledge of species' habitat preferences and reproductive status are necessary especially for tropical plant species that have short-lived recalcitrant seeds, and those that exhibit complex reproductive patterns, e.g., species with supra-annual mass flowering events that may occur in irregular intervals. Understanding plant regeneration in the way of planning for effective reforestation can be aided by providing access to structured information, e.g., in knowledge bases, that spans years if not decades as well as covering a wide range of geographic locations. The content of such a resource can be enriched with literature-derived information on species' time-sensitive reproductive conditions and location-specific habitats. Methods We sought to develop unsupervised approaches to extract relationships pertaining to habitats and their locations, and reproductive conditions of plant species and corresponding temporal information. Firstly, we handcrafted rules for a traditional rule-based pattern matching approach. We then developed a relation extraction approach building upon transformer models, i.e., the Text-to-Text Transfer Transformer (T5), casting the relation extraction problem as a question answering and natural language inference task. We then propose a novel unsupervised hybrid approach that combines our rule-based and transformer-based approaches. Results Evaluation of our hybrid approach on an annotated corpus of biodiversity-focused documents demonstrated an improvement of up to 15 percentage points in recall and best performance over solely rule-based and transformer-based methods with F1-scores ranging from 89.61 to 96.75% for reproductive condition - temporal expression relations, and ranging from 85.39% to 89.90% for habitat - geographic location relations. Our work shows that even without training models on any domain-specific labeled dataset, we are able to extract relationships between biodiversity concepts from literature with satisfactory performance.
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Affiliation(s)
- Roselyn Gabud
- Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines
- Institute of Computer Science, College of Arts and Sciences, University of the Philippines Los Baños, Laguna, Philippines
| | - Portia Lapitan
- Department of Forest Biological Sciences, College of Forestry and Natural Resources, University of the Philippines Los Baños, Laguna, Philippines
| | - Vladimir Mariano
- Young Southeast Asian Leaders Initiative (YSEALI) Academy, Fulbright University Vietnam, Ho Chi Minh City, Vietnam
| | - Eduardo Mendoza
- Institute of Computer Science, College of Arts and Sciences, University of the Philippines Los Baños, Laguna, Philippines
- Mathematics and Statistics Department, De la Salle University, Manila, Philippines
- Center for Natural Science and Environmental Research, De la Salle University, Manila, Philippines
- Max Planck Institute of Biochemistry, Munich, Germany
| | - Nelson Pampolina
- Department of Forest Biological Sciences, College of Forestry and Natural Resources, University of the Philippines Los Baños, Laguna, Philippines
| | - Maria Art Antonette Clariño
- Institute of Computer Science, College of Arts and Sciences, University of the Philippines Los Baños, Laguna, Philippines
| | - Riza Batista-Navarro
- Institute of Computer Science, College of Arts and Sciences, University of the Philippines Los Baños, Laguna, Philippines
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
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Castanho EN, Aidos H, Madeira SC. Biclustering data analysis: a comprehensive survey. Brief Bioinform 2024; 25:bbae342. [PMID: 39007596 PMCID: PMC11247412 DOI: 10.1093/bib/bbae342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 05/16/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.
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Affiliation(s)
- Eduardo N Castanho
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Campo Grande 16, P-1749-016 Lisbon, Portugal
| | - Helena Aidos
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Campo Grande 16, P-1749-016 Lisbon, Portugal
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Campo Grande 16, P-1749-016 Lisbon, Portugal
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de Oliveira IP, Schaaf C, de Setta N. Drought Responses in Poaceae: Exploring the Core Components of the ABA Signaling Pathway in Setaria italica and Setaria viridis. PLANTS (BASEL, SWITZERLAND) 2024; 13:1451. [PMID: 38891260 PMCID: PMC11174756 DOI: 10.3390/plants13111451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/14/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
Drought severely impacts plant development and reproduction, reducing biomass and seed number, and altering flowering patterns. Drought-tolerant Setaria italica and Setaria viridis species have emerged as prominent model species for investigating water deficit responses in the Poaceae family, the most important source of food and biofuel biomass worldwide. In higher plants, abscisic acid (ABA) regulates environmental stress responses, and its signaling entails interactions between PYR/PYL/RCAR receptors and clade A PP2C phosphatases, which in turn modulate SnRK2 kinases via reversible phosphorylation to activate ABA-responsive genes. To compare the diversity of PYR/PYL/RCAR, PP2C, and SnRK2 between S. italica and S. viridis, and their involvement in water deficit responses, we examined gene and regulatory region structures, investigated orthology relationships, and analyzed their gene expression patterns under water stress via a meta-analysis approach. Results showed that coding and regulatory sequences of PYR/PYL/RCARs, PP2Cs, and SnRK2s are highly conserved between Setaria spp., allowing us to propose pairs of orthologous genes for all the loci identified. Phylogenetic relationships indicate which clades of Setaria spp. sequences are homologous to the functionally well-characterized Arabidopsis thaliana PYR/PYL/RCAR, PP2C, and SnRK2 genes. Gene expression analysis showed a general downregulation of PYL genes, contrasting with upregulation of PP2C genes, and variable expression modulation of SnRK2 genes under drought stress. This complex network implies that ABA core signaling is a diverse and multifaceted process. Through our analysis, we identified promising candidate genes for further functional characterization, with great potential as targets for drought resistance studies, ultimately leading to advances in Poaceae biology and crop-breeding strategies.
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Affiliation(s)
| | | | - Nathalia de Setta
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, São Bernardo do Campo 09606-045, SP, Brazil; (I.P.d.O.); (C.S.)
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225
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Kojima S, Kanoh S. An auditory brain-computer interface based on selective attention to multiple tone streams. PLoS One 2024; 19:e0303565. [PMID: 38781127 PMCID: PMC11115270 DOI: 10.1371/journal.pone.0303565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/27/2024] [Indexed: 05/25/2024] Open
Abstract
In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user's right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject's selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.
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Affiliation(s)
- Simon Kojima
- Graduate School of Engineering and Science, Shibaura Institute of Technology, Koto-ku, Tokyo, Japan
| | - Shin’ichiro Kanoh
- Graduate School of Engineering and Science, Shibaura Institute of Technology, Koto-ku, Tokyo, Japan
- College of Engineering, Shibaura Institute of Technology, Koto-ku, Tokyo, Japan
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Li Y, Jin Y, Wang Y, Liu W, Jia W, Wang J. MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study. Cancer Imaging 2024; 24:65. [PMID: 38773634 PMCID: PMC11110398 DOI: 10.1186/s40644-024-00709-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/11/2024] [Indexed: 05/24/2024] Open
Abstract
OBJECTIVES Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence. METHODS A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort's support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set. RESULTS For contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set. CONCLUSIONS This study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans. CLINICAL RELEVANCE STATEMENT We propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.
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Affiliation(s)
- Yanran Li
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Yong Jin
- Department of Radiology, Changzhi People's Hospital, Changzhi, 046000, Shanxi Province, China
| | - Yunling Wang
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Wenya Liu
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Wenxiao Jia
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Jian Wang
- Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China.
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227
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Tadese ZB, Nigatu AM, Yehuala TZ, Sebastian Y. Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach. Sci Rep 2024; 14:11529. [PMID: 38773175 PMCID: PMC11109113 DOI: 10.1038/s41598-024-62641-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/20/2024] [Indexed: 05/23/2024] Open
Abstract
The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate declined from 59% in 1990 to 38% in 2019, due to high immunization coverage. Despite the significant improvements in immunization coverage, about 20 million children were either unvaccinated or had incomplete immunization, making them more susceptible to mortality and morbidity. This study aimed to identify predictors of incomplete vaccination among children under-5 years in East Africa. An analysis of secondary data from six east African countries using Demographic and Health Survey dataset from 2016 to the recent 2021 was performed. A total weighted sample of 27,806 children aged (12-35) months was included in this study. Data were extracted using STATA version 17 statistical software and imported to a Jupyter notebook for further analysis. A supervised machine learning algorithm was implemented using different classification models. All analysis and calculations were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, XGBoost, and shap packages. XGBoost classifier demonstrated the best performance with accuracy (79.01%), recall (89.88%), F1-score (81.10%), precision (73.89%), and AUC 86%. Predictors of incomplete immunization are identified using XGBoost models with help of Shapely additive eXplanation. This study revealed that the number of living children during birth, antenatal care follow-up, maternal age, place of delivery, birth order, preceding birth interval and mothers' occupation were the top predicting factors of incomplete immunization. Thus, family planning programs should prioritize the number of living children during birth and the preceding birth interval by enhancing maternal education. In conclusion promoting institutional delivery and increasing the number of antenatal care follow-ups by more than fourfold is encouraged.
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Affiliation(s)
- Zinabu Bekele Tadese
- Department of Health Informatics, School of Public Health, College of Medicine and Health Science, Samara University, Samara, Ethiopia.
| | - Araya Mesfin Nigatu
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Tirualem Zeleke Yehuala
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Yakub Sebastian
- Department of Information Technology, Faculty of Science and Technology, Charles Darwin University, Darwin, Australia
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228
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Shore SFH, Leinberger FH, Fozo EM, Berghoff BA. Type I toxin-antitoxin systems in bacteria: from regulation to biological functions. EcoSal Plus 2024:eesp00252022. [PMID: 38767346 DOI: 10.1128/ecosalplus.esp-0025-2022] [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: 09/29/2023] [Accepted: 04/11/2024] [Indexed: 05/22/2024]
Abstract
Toxin-antitoxin systems are ubiquitous in the prokaryotic world and widely distributed among chromosomes and mobile genetic elements. Several different toxin-antitoxin system types exist, but what they all have in common is that toxin activity is prevented by the cognate antitoxin. In type I toxin-antitoxin systems, toxin production is controlled by an RNA antitoxin and by structural features inherent to the toxin messenger RNA. Most type I toxins are small membrane proteins that display a variety of cellular effects. While originally discovered as modules that stabilize plasmids, chromosomal type I toxin-antitoxin systems may also stabilize prophages, or serve important functions upon certain stress conditions and contribute to population-wide survival strategies. Here, we will describe the intricate RNA-based regulation of type I toxin-antitoxin systems and discuss their potential biological functions.
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Affiliation(s)
- Selene F H Shore
- Department of Microbiology, University of Tennessee, Knoxville, Tennessee, USA
| | - Florian H Leinberger
- Institute for Microbiology and Molecular Biology, Justus-Liebig University, Giessen, Germany
| | - Elizabeth M Fozo
- Department of Microbiology, University of Tennessee, Knoxville, Tennessee, USA
| | - Bork A Berghoff
- Institute for Microbiology and Molecular Biology, Justus-Liebig University, Giessen, Germany
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229
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Sajed T, Sayeeda Z, Lee BL, Berjanskii M, Wang F, Gautam V, Wishart DS. Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning. Metabolites 2024; 14:290. [PMID: 38786767 PMCID: PMC11123270 DOI: 10.3390/metabo14050290] [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: 04/16/2024] [Revised: 05/11/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.
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Affiliation(s)
- Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Zinat Sayeeda
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian L. Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Fei Wang
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
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Abdel-Maksoud MA, Askar MA, Abdel-rahman IY, Gharib M, Aufy M. Integrating Network Pharmacology and Molecular Docking Approach to Elucidate the Mechanism of Commiphora wightii for the Treatment of Rheumatoid Arthritis. Bioinform Biol Insights 2024; 18:11779322241247634. [PMID: 38765022 PMCID: PMC11102677 DOI: 10.1177/11779322241247634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 03/28/2024] [Indexed: 05/21/2024] Open
Abstract
Background Rheumatoid arthritis (RA) is considered a notable prolonged inflammatory condition with no proper cure. Synovial inflammation and synovial pannus are crucial in the onset of RA. The "tumor-like" invading proliferation of new arteries is a keynote of RA. Commiphora wightii (C wightii) is a perennial, deciduous, and trifoliate plant used in several areas of southeast Asia to cure numerous ailments, including arthritis, diabetes, obesity, and asthma. Several in vitro investigations have indicated C wightii's therapeutic efficacy in the treatment of arthritis. However, the precise molecular action is yet unknown. Material and methods In this study, a network pharmacology approach was applied to uncover potential targets, active therapeutic ingredients and signaling pathways in C wightii for the treatment of arthritis. In the groundwork of this research, we examined the active constituent-compound-target-pathway network and evaluated that (Guggulsterol-V, Myrrhahnone B, and Campesterol) decisively donated to the development of arthritis by affecting tumor necrosis factor (TNF), PIK3CA, and MAPK3 genes. Later on, docking was employed to confirm the active components' efficiency against the potential targets. Results According to molecular-docking research, several potential targets of RA bind tightly with the corresponding key active ingredient of C wightii. With the aid of network pharmacology techniques, we conclude that the signaling pathways and biological processes involved in C wightii had an impact on the prevention of arthritis. The outcomes of molecular docking also serve as strong recommendations for future research. In the context of this study, network pharmacology combined with molecular docking analysis showed that C wightii acted on arthritis-related signaling pathways to exhibit a promising preventive impact on arthritis. Conclusion These results serve as the basis for grasping the mechanism of the antiarthritis activity of C wightii. However, further in vivo/in vitro study is needed to verify the reliability of these targets for the treatment of arthritis.
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Affiliation(s)
- Mostafa A Abdel-Maksoud
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Mostafa A Askar
- Radiation Biology Department, National Centre for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt
| | - Ibrahim Y Abdel-rahman
- Radiation Biology Department, National Centre for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt
| | - Mustafa Gharib
- Radiation Biology Department, National Centre for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt
| | - Mohammed Aufy
- Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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231
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Taiwo OR, Onyeaka H, Oladipo EK, Oloke JK, Chukwugozie DC. Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models. Int J Microbiol 2024; 2024:6612162. [PMID: 38799770 PMCID: PMC11126350 DOI: 10.1155/2024/6612162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.
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Affiliation(s)
| | - Helen Onyeaka
- School of Chemical Engineering, University of Birmingham, Edgbaston B15 2TT, Birmingham, UK
| | - Elijah K. Oladipo
- Genomics Unit, Helix Biogen Institute, Ogbomosho, Oyo, Nigeria
- Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun, Nigeria
| | - Julius Kola Oloke
- Department of Natural Science, Microbiology Unit, Precious Cornerstone University, Ibadan, Oyo, Nigeria
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Ribeiro-Dantas MDC, Li H, Cabeli V, Dupuis L, Simon F, Hettal L, Hamy AS, Isambert H. Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients. iScience 2024; 27:109736. [PMID: 38711452 PMCID: PMC11070693 DOI: 10.1016/j.isci.2024.109736] [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: 04/25/2023] [Revised: 10/26/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024] Open
Abstract
Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data are available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-world healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.
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Affiliation(s)
| | - Honghao Li
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
| | - Vincent Cabeli
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
| | - Louise Dupuis
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
| | - Franck Simon
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
| | - Liza Hettal
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
| | - Anne-Sophie Hamy
- INSERM U932, Institut Curie, Paris, France
- Department of Medical Oncology, Institut Curie, Saint-Cloud, France
- Department of Surgery, Institut Curie, Université Paris, Paris, France
| | - Hervé Isambert
- CNRS UMR168, Institut Curie, Université PSL, Sorbonne Université, Paris, France
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233
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Chen D, Li Z, Shi J, Suen H, Zheng X, Zhang C, Chen Y, Xue T. Genomics and transcriptomics reveal β-carotene synthesis mechanism in Dunaliella salina. Front Microbiol 2024; 15:1389224. [PMID: 38827145 PMCID: PMC11140103 DOI: 10.3389/fmicb.2024.1389224] [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: 02/21/2024] [Accepted: 05/07/2024] [Indexed: 06/04/2024] Open
Abstract
Dunaliella salina is by far the most salt-tolerant organism and contains many active substances, including β-carotene, glycerol, proteins, and vitamins, using in the production of dried biomass or cell extracts for the biofuels, pharmaceutical formulations, food additives, and fine chemicals, especially β-carotene. We report a high-quality genome sequence of D. Salina FACHB435, which has a 472 Mb genome size, with a contig N50 of 458 Kb. A total of 30,752 protein-coding genes were predicted. The annotation results evaluated by BUSCO was shown that completeness was 91.0% and replication was 53.1%. The fragments were 6.3% and the deletions were 2.6%. Phylogenomic and comparative genomic analyses revealed that A. thaliana diverged from Volvocales about 448 million years ago, then Volvocales C. eustigma, D. salina, and other species diverged about 250 million years ago. High light could promote the accumulation of β-carotene in D. salina at a 13 d stage of culture. The enrichment of DEGs in KEGG, it notes that the predicted up-regulated genes of carotenoid metabolic pathway include DsCrtB, DsPDS, DsZ-ISO, DsZDS, DsCRTISO, DsLUT5, DsCrtL-B, and DsCCD8, while the predicted down-regulated genes include DsCrtF, and DsLUT1. The four genes that were both up-regulated and down-regulated were DsZEP, DsCrtR-b, DsCruA/P and DsCrtZ 4. The research results can provide scientific basis for the industrialization practice of D. salina.
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Affiliation(s)
- Duo Chen
- The Public Service Platform for Industrialization Development Technology of Marine Biological Medicine and Products of the State Oceanic Administration, Fujian Key Laboratory of Special Marine Bioresource Sustainable Utilization, Center of Engineering Technology Research for Microalga Germplasm Improvement of Fujian, Southern Institute of Oceanography, Fujian Normal University, Fuzhou, China
| | | | | | | | | | | | | | - Ting Xue
- The Public Service Platform for Industrialization Development Technology of Marine Biological Medicine and Products of the State Oceanic Administration, Fujian Key Laboratory of Special Marine Bioresource Sustainable Utilization, Center of Engineering Technology Research for Microalga Germplasm Improvement of Fujian, Southern Institute of Oceanography, Fujian Normal University, Fuzhou, China
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Candia J, Ferrucci L. Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks. PLoS One 2024; 19:e0302696. [PMID: 38753612 PMCID: PMC11098418 DOI: 10.1371/journal.pone.0302696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.
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Affiliation(s)
- Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
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Mignon J, Leyder T, Mottet D, Uversky VN, Michaux C. In-depth investigation of the effect of pH on the autofluorescence properties of DPF3b and DPF3a amyloid fibrils. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 313:124156. [PMID: 38508075 DOI: 10.1016/j.saa.2024.124156] [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: 12/12/2023] [Revised: 03/10/2024] [Accepted: 03/12/2024] [Indexed: 03/22/2024]
Abstract
Double PHD fingers 3 (DPF3) protein exists as two splicing variants, DPF3b and DPF3a, the involvement of which in human cancer and neurodegeneration is beginning to be increasingly recognised. Both isoforms have recently been identified as intrinsically disordered proteins able to undergo amyloid fibrillation. Upon their aggregation, DPF3 proteins exhibit an intrinsic fluorescence in the visible range, referred to as deep-blue autofluorescence (dbAF). Comprehension of such phenomenon remaining elusive, we investigated in the present study the influence of pH on the optical properties of DPF3b and DPF3a fibrils. By varying the excitation wavelength and the pH condition, the two isoforms were revealed to display several autofluorescence modes that were defined as violet, deep-blue, and blue-green according to their emission range. Complementarily, analysis of excitation spectra and red edge shift plots allowed to better decipher their photoselection mechanism and to highlight isoform-specific excitation-emission features. Furthermore, the observed violation to Kasha-Vavilov's rule was attributed to red edge excitation shift effects, which were impacted by pH-mediated H-bond disruption, leading to changes in intramolecular charge and proton transfer, or π-electrons delocalisation. Finally, emergence of different autofluorescence emitters was likely related to structurally distinct fibrillar assemblies between isoforms, as well as to discrepancies in the amino acid composition of their aggregation prone regions.
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Affiliation(s)
- Julien Mignon
- Laboratoire de Chimie Physique des Biomolécules, UCPTS, University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium; Namur Institute of Structured Matter (NISM), University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium; Namur Research Institute for Life Sciences (NARILIS), University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium.
| | - Tanguy Leyder
- Laboratoire de Chimie Physique des Biomolécules, UCPTS, University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium.
| | - Denis Mottet
- Gene Expression and Cancer Laboratory, GIGA-Molecular Biology of Diseases, University of Liège, B34, Avenue de l'Hôpital, 4000 Liège, Belgium.
| | - Vladimir N Uversky
- Department of Molecular Medicine, USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, United States.
| | - Catherine Michaux
- Laboratoire de Chimie Physique des Biomolécules, UCPTS, University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium; Namur Institute of Structured Matter (NISM), University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium; Namur Research Institute for Life Sciences (NARILIS), University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium.
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Niu Y, Li Z, Chen Z, Huang W, Tan J, Tian F, Yang T, Fan Y, Wei J, Mu J. Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy. Heliyon 2024; 10:e30373. [PMID: 38765108 PMCID: PMC11101728 DOI: 10.1016/j.heliyon.2024.e30373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/21/2024] Open
Abstract
In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.
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Affiliation(s)
- Yupeng Niu
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Zhenghao Li
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Ziao Chen
- College of Law, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Wenyuan Huang
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Jingxuan Tan
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Fa Tian
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
| | - Tao Yang
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Yamin Fan
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Jiangshu Wei
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
| | - Jiong Mu
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
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Prodromou SI, Chatzopoulou F, Saiti A, Giannopoulos-Dimitriou A, Koudoura LA, Pantazaki AA, Chatzidimitriou D, Vasiliou V, Vizirianakis IS. Hepatotoxicity assessment of innovative nutritional supplements based on olive-oil formulations enriched with natural antioxidants. Front Nutr 2024; 11:1388492. [PMID: 38812942 PMCID: PMC11133736 DOI: 10.3389/fnut.2024.1388492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/25/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction This study focuses on the assessment of extra virgin olive-oil and olive fruit-based formulations enriched with natural antioxidants as potential nutritional supplements for alleviating symptoms and long-term consequences of illnesses whose molecular pathophysiology is affected by oxidative stress and inflammation, such as Alzheimer's disease (AD). Methods Besides evaluating cell viability and proliferation capacity of human hepatocellular carcinoma HepG2 cells exposed to formulations in culture, hepatotoxicity was also considered as an additional safety measure using quantitative real-time PCR on RNA samples isolated from the cell cultures and applying approaches of targeted molecular analysis to uncover potential pathway effects through gene expression profiling. Furthermore, the formulations investigated in this work contrast the addition of natural extract with chemical forms and evaluate the antioxidant delivery mode on cell toxicity. Results The results indicate minimal cellular toxicity and a significant beneficial impact on metabolic molecular pathways in HepG2 cell cultures, thus paving the way for innovative therapeutic strategies using olive-oil and antioxidants in dietary supplements to minimize the long-term effects of oxidative stress and inflammatory signals in individuals being suffered by disorders like AD. Discussion Overall, the experimental design and the data obtained support the notion of applying innovative molecular methodologies and research techniques to evidently advance the delivery, as well as the scientific impact and validation of nutritional supplements and dietary products to improve public health and healthcare outcomes.
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Affiliation(s)
- Sofia I. Prodromou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Fani Chatzopoulou
- Laboratory of Microbiology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Labnet Laboratories, Department of Molecular Biology and Genetics, Thessaloniki, Greece
| | - Aikaterini Saiti
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Loukia A. Koudoura
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasia A. Pantazaki
- Laboratory of Biochemistry, Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Chatzidimitriou
- Laboratory of Microbiology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, United States
| | - Ioannis S. Vizirianakis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Health Sciences, School of Health and Life Sciences, University of Nicosia, Nicosia, Cyprus
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238
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Cao S, Hu Y. Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index. Nutr Metab (Lond) 2024; 21:25. [PMID: 38745171 PMCID: PMC11092237 DOI: 10.1186/s12986-024-00802-2] [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: 01/25/2024] [Accepted: 05/01/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. METHODS Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. RESULTS An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798-0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. CONCLUSION The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.
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Affiliation(s)
- Shunshun Cao
- Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yangyang Hu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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He S, Gubin MM, Rafei H, Basar R, Dede M, Jiang X, Liang Q, Tan Y, Kim K, Gillison ML, Rezvani K, Peng W, Haymaker C, Hernandez S, Solis LM, Mohanty V, Chen K. Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593433. [PMID: 38798470 PMCID: PMC11118452 DOI: 10.1101/2024.05.10.593433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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240
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Veyssiere M, Rodriguez Ordonez MDP, Chalabi S, Michou L, Cornelis F, Boland A, Olaso R, Deleuze JF, Petit-Teixeira E, Chaudru V. MYLK* FLNB and DOCK1* LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway. Front Genet 2024; 15:1375036. [PMID: 38803542 PMCID: PMC11128622 DOI: 10.3389/fgene.2024.1375036] [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: 01/23/2024] [Accepted: 04/18/2024] [Indexed: 05/29/2024] Open
Abstract
Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.
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Affiliation(s)
- Maëva Veyssiere
- Institut National de la Santé et de la Recherche Médicale, Université de Paris, Paris, France
| | | | - Smahane Chalabi
- GenHotel—Univ Evry, University of Paris Saclay, Evry, France
| | - Laetitia Michou
- Division of Rheumatology, Department of Medicine, CHU de Québec-Université Laval, Québec City, QC, Canada
| | - François Cornelis
- Génétiqe-Oncogénétique Adulte-Prévention, Institut National de la Santé et de la Recherche Médicale, Clermont-Auvergne University and CHU, Clermont-Ferrand, France
| | - Anne Boland
- Commissariat à l'Energie Atomique, Centre National de Recherche en Génomique Humaine (CNRGH), Université Paris-Saclay, Evry, France
| | - Robert Olaso
- Commissariat à l'Energie Atomique, Centre National de Recherche en Génomique Humaine (CNRGH), Université Paris-Saclay, Evry, France
| | - Jean-François Deleuze
- Commissariat à l'Energie Atomique, Centre National de Recherche en Génomique Humaine (CNRGH), Université Paris-Saclay, Evry, France
| | | | - Valérie Chaudru
- Institut National de la Santé et de la Recherche Médicale, Université de Paris, Paris, France
- GenHotel—Univ Evry, University of Paris Saclay, Evry, France
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Zheng HY, Wang L, Zhang R, Ding R, Yang CX, Du ZQ. Valine induces inflammation and enhanced adipogenesis in lean mice by multi-omics analysis. Front Nutr 2024; 11:1379390. [PMID: 38803448 PMCID: PMC11128663 DOI: 10.3389/fnut.2024.1379390] [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: 01/31/2024] [Accepted: 04/12/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction The branched-chain amino acids (BCAAs) are essential to mammalian growth and development but aberrantly elevated in obesity and diabetes. Each BCAA has an independent and specific physio-biochemical effect on the host. However, the exact molecular mechanism of the detrimental effect of valine on metabolic health remains largely unknown. Methods and results This study showed that for lean mice treated with valine, the hepatic lipid metabolism and adipogenesis were enhanced, and the villus height and crypt depth of the ileum were significantly increased. Transcriptome profiling on white and brown adipose tissues revealed that valine disturbed multiple signaling pathways (e.g., inflammation and fatty acid metabolism). Integrative cecal metagenome and metabolome analyses found that abundances of Bacteroidetes decreased, but Proteobacteria and Helicobacter increased, respectively; and 87 differential metabolites were enriched in several molecular pathways (e.g., inflammation and lipid and bile acid metabolism). Furthermore, abundances of two metabolites (stercobilin and 3-IAA), proteins (AMPK/pAMPK and SCD1), and inflammation and adipogenesis-related genes were validated. Discussion Valine treatment affects the intestinal microbiota and metabolite compositions, induces gut inflammation, and aggravates hepatic lipid deposition and adipogenesis. Our findings provide novel insights into and resources for further exploring the molecular mechanism and biological function of valine on lipid metabolism.
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Affiliation(s)
- Hui-Yi Zheng
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
- Center of Animal Breeding Technology Innovation of Hubei Province, Wuhan, China
| | - Li Wang
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
- Center of Animal Breeding Technology Innovation of Hubei Province, Wuhan, China
| | - Rong Zhang
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
- Center of Animal Breeding Technology Innovation of Hubei Province, Wuhan, China
| | - Ran Ding
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
- Center of Animal Breeding Technology Innovation of Hubei Province, Wuhan, China
| | - Cai-Xia Yang
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
- Center of Animal Breeding Technology Innovation of Hubei Province, Wuhan, China
| | - Zhi-Qiang Du
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
- Center of Animal Breeding Technology Innovation of Hubei Province, Wuhan, China
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Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [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: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
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Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
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Tang DY, Mao YJ, Zhao J, Yang J, Li SY, Ren FX, Zheng J. SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions. BMC Genomics 2024; 25:462. [PMID: 38735952 DOI: 10.1186/s12864-024-10373-4] [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: 12/29/2023] [Accepted: 05/03/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Detecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge. RESULTS Here, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs. CONCLUSIONS Here, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP-SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer. AVAILABILITY AND IMPLEMENTATION https://github.com/scutdy/SSO/blob/master/SEEI.zip .
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Affiliation(s)
- De-Yu Tang
- Department of Computer Science, School of Mathematics and Informatics, School of Software Engineering, South China Agricultural University, Guangzhou, 510642, PR China.
- School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, PR China.
| | - Yi-Jun Mao
- Department of Computer Science, School of Mathematics and Informatics, School of Software Engineering, South China Agricultural University, Guangzhou, 510642, PR China.
| | - Jie Zhao
- School of Management, Guangdong University of Technology, Guangzhou, 510006, PR China
| | - Jin Yang
- School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, PR China.
| | - Shi-Yin Li
- School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, PR China
| | - Fu-Xiang Ren
- School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, PR China
| | - Junxi Zheng
- School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, PR China.
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244
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Zhao L, Vidwans A, Bearnot CJ, Rayner J, Lin T, Baird J, Suner S, Jay GD. Prediction of anemia in real-time using a smartphone camera processing conjunctival images. PLoS One 2024; 19:e0302883. [PMID: 38739605 PMCID: PMC11090304 DOI: 10.1371/journal.pone.0302883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
Anemia is defined as a low hemoglobin (Hb) concentration and is highly prevalent worldwide. We report on the performance of a smartphone application (app) that records images in RAW format of the palpebral conjunctivae and estimates Hb concentration by relying upon computation of the tissue surface high hue ratio. Images of bilateral conjunctivae were obtained prospectively from a convenience sample of 435 Emergency Department patients using a dedicated smartphone. A previous computer-based and validated derivation data set associating estimated conjunctival Hb (HBc) and the actual laboratory-determined Hb (HBl) was used in deriving Hb estimations using a self-contained mobile app. Accuracy of HBc was 75.4% (95% CI 71.3, 79.4%) for all categories of anemia, and Bland-Altman plot analysis showed a bias of 0.10 and limits of agreement (LOA) of (-4.73, 4.93 g/dL). Analysis of HBc estimation accuracy around different anemia thresholds showed that AUC was maximized at transfusion thresholds of 7 and 9 g/dL which showed AUC values of 0.92 and 0.90 respectively. We found that the app is sufficiently accurate for detecting severe anemia and shows promise as a population-sourced screening platform or as a non-invasive point-of-care anemia classifier.
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Affiliation(s)
- Leon Zhao
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
| | - Alisa Vidwans
- Rhode Island Hospital, Providence, Rhode Island, United States of America
| | - Courtney J. Bearnot
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
| | - James Rayner
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
| | - Timmy Lin
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
| | - Janette Baird
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
| | - Selim Suner
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
| | - Gregory D. Jay
- The Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States of America
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245
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Ferreira T, Rodriguez S. Mitochondrial DNA: Inherent Complexities Relevant to Genetic Analyses. Genes (Basel) 2024; 15:617. [PMID: 38790246 PMCID: PMC11121663 DOI: 10.3390/genes15050617] [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: 04/17/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Mitochondrial DNA (mtDNA) exhibits distinct characteristics distinguishing it from the nuclear genome, necessitating specific analytical methods in genetic studies. This comprehensive review explores the complex role of mtDNA in a variety of genetic studies, including genome-wide, epigenome-wide, and phenome-wide association studies, with a focus on its implications for human traits and diseases. Here, we discuss the structure and gene-encoding properties of mtDNA, along with the influence of environmental factors and epigenetic modifications on its function and variability. Particularly significant are the challenges posed by mtDNA's high mutation rate, heteroplasmy, and copy number variations, and their impact on disease susceptibility and population genetic analyses. The review also highlights recent advances in methodological approaches that enhance our understanding of mtDNA associations, advocating for refined genetic research techniques that accommodate its complexities. By providing a comprehensive overview of the intricacies of mtDNA, this paper underscores the need for an integrated approach to genetic studies that considers the unique properties of mitochondrial genetics. Our findings aim to inform future research and encourage the development of innovative methodologies to better interpret the broad implications of mtDNA in human health and disease.
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Affiliation(s)
- Tomas Ferreira
- Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SL, UK
| | - Santiago Rodriguez
- Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1QU, UK
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246
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Wang X, Dong J, Hu Y, Huang Q, Lu X, Huang Y, Sheng M, Cao L, Xu B, Li Y, Zong Y, Guo W. Identification and Characterization of the Glutathione S-Transferase Gene Family in Blueberry ( Vaccinium corymbosum) and Their Potential Roles in Anthocyanin Intracellular Transportation. PLANTS (BASEL, SWITZERLAND) 2024; 13:1316. [PMID: 38794388 PMCID: PMC11125127 DOI: 10.3390/plants13101316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024]
Abstract
The glutathione S-transferases (GSTs, EC 2.5.1.18) constitute a versatile enzyme family with pivotal roles in plant stress responses and detoxification processes. Recent discoveries attributed the additional function of facilitating anthocyanin intracellular transportation in plants to GSTs. Our study identified 178 VcGST genes from 12 distinct subfamilies in the blueberry genome. An uneven distribution was observed among these genes across blueberry's chromosomes. Members within the same subfamily displayed homogeneity in gene structure and conserved protein motifs, whereas marked divergence was noted among subfamilies. Functional annotations revealed that VcGSTs were significantly enriched in several gene ontology and KEGG pathway categories. Promoter regions of VcGST genes predominantly contain light-responsive, MYB-binding, and stress-responsive elements. The majority of VcGST genes are subject to purifying selection, with whole-genome duplication or segmental duplication serving as key processes that drive the expansion of the VcGST gene family. Notably, during the ripening of the blueberry fruit, 100 VcGST genes were highly expressed, and the expression patterns of 24 of these genes demonstrated a strong correlation with the dynamic content of fruit anthocyanins. Further analysis identified VcGSTF8, VcGSTF20, and VcGSTF22 as prime candidates of VcGST genes involved in the anthocyanin intracellular transport. This study provides a reference for the exploration of anthocyanin intracellular transport mechanisms and paves the way for investigating the spectrum of GST functions in blueberries.
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Affiliation(s)
- Xuxiang Wang
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Jiajia Dong
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Yiting Hu
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Qiaoyu Huang
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Xiaoying Lu
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Yilin Huang
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Mingyang Sheng
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Lijun Cao
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Buhuai Xu
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
| | - Yongqiang Li
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
- Zhejiang Provincial Key Laboratory of Biotechnology on Specialty Economic Plants, Zhejiang Normal University, Jinhua 321004, China
| | - Yu Zong
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
- Zhejiang Provincial Key Laboratory of Biotechnology on Specialty Economic Plants, Zhejiang Normal University, Jinhua 321004, China
| | - Weidong Guo
- College of Life Sciences, Zhejiang Normal University, Jinhua 321004, China; (X.W.); (J.D.); (Y.H.); (Q.H.); (X.L.); (Y.H.); (M.S.); (L.C.); (B.X.); (Y.L.)
- Zhejiang Provincial Key Laboratory of Biotechnology on Specialty Economic Plants, Zhejiang Normal University, Jinhua 321004, China
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Isaacson JR, Berg MD, Yeung W, Villén J, Brandl CJ, Moehring AJ. Impact of tRNA-induced proline-to-serine mistranslation on the transcriptome of Drosophila melanogaster. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593249. [PMID: 38766246 PMCID: PMC11100759 DOI: 10.1101/2024.05.08.593249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Mistranslation is the misincorporation of an amino acid into a polypeptide. Mistranslation has diverse effects on multicellular eukaryotes and is implicated in several human diseases. In Drosophila melanogaster, a serine transfer RNA (tRNA) that misincorporates serine at proline codons (P→S) affects male and female flies differently. The mechanisms behind this discrepancy are currently unknown. Here, we compare the transcriptional response of male and female flies to P→S mistranslation to identify genes and cellular processes that underlie sex-specific differences. Both males and females downregulate genes associated with various metabolic processes in response to P→S mistranslation. Males downregulate genes associated with extracellular matrix organization and response to negative stimuli such as wounding, whereas females downregulate aerobic respiration and ATP synthesis genes. Both sexes upregulate genes associated with gametogenesis, but females also upregulate cell cycle and DNA repair genes. These observed differences in the transcriptional response of male and female flies to P→S mistranslation have important implications for the sex-specific impact of mistranslation on disease and tRNA therapeutics.
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Affiliation(s)
| | - Matthew D. Berg
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195
| | - William Yeung
- Department of Biology, Western University, N6A 5B7, London, Canada
| | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195
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248
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Cao B, Wang X, Yin W, Gao Z, Xia B. The human microbiota is a beneficial reservoir for SARS-CoV-2 mutations. mBio 2024; 15:e0318723. [PMID: 38530031 PMCID: PMC11237538 DOI: 10.1128/mbio.03187-23] [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: 12/17/2023] [Accepted: 02/14/2024] [Indexed: 03/27/2024] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are rapidly emerging. In particular, beneficial mutations in the spike (S) protein, which can either make a person more infectious or enable immunological escape, are providing a significant obstacle to the prevention and treatment of pandemics. However, how the virus acquires a high number of beneficial mutations in a short time remains a mystery. We demonstrate here that variations of concern may be mutated due in part to the influence of the human microbiome. We searched the National Center for Biotechnology Information database for homologous fragments (HFs) after finding a mutation and the six neighboring amino acids in a viral mutation fragment. Among the approximate 8,000 HFs obtained, 61 mutations in S and other outer membrane proteins were found in bacteria, accounting for 62% of all mutation sources, which is 12-fold higher than the natural variable proportion. A significant proportion of these bacterial species-roughly 70%-come from the human microbiota, are mainly found in the lung or gut, and share a composition pattern with COVID-19 patients. Importantly, SARS-CoV-2 RNA-dependent RNA polymerase replicates corresponding bacterial mRNAs harboring mutations, producing chimeric RNAs. SARS-CoV-2 may collectively pick up mutations from the human microbiota that change the original virus's binding sites or antigenic determinants. Our study clarifies the evolving mutational mechanisms of SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are rapidly emerging, in particular advantageous mutations in the spike (S) protein, which either increase transmissibility or lead to immune escape and are posing a major challenge to pandemic prevention and treatment. However, how the virus acquires a high number of advantageous mutations in a short time remains a mystery. Here, we provide evidence that the human microbiota is a reservoir of advantageous mutations and aids mutational evolution and host adaptation of SARS-CoV-2. Our findings demonstrate a conceptual breakthrough on the mutational evolution mechanisms of SARS-CoV-2 for human adaptation. SARS-CoV-2 may grab advantageous mutations from the widely existing microorganisms in the host, which is undoubtedly an "efficient" manner. Our study might open a new perspective to understand the evolution of virus mutation, which has enormous implications for comprehending the trajectory of the COVID-19 pandemic.
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Affiliation(s)
- Birong Cao
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Guangdong Guangya High School, Guangzhou, China
| | - Xiaoxi Wang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wanchao Yin
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China
| | - Zhaobing Gao
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, China
| | - Bingqing Xia
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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249
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Van R, Alvarez D, Mize T, Gannavarapu S, Chintham Reddy L, Nasoz F, Han MV. A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies. BMC Bioinformatics 2024; 25:181. [PMID: 38720247 PMCID: PMC11080237 DOI: 10.1186/s12859-024-05801-x] [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: 01/31/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.
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Affiliation(s)
- Richard Van
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Daniel Alvarez
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Travis Mize
- Icahn School of Medicine at Mount Sinai, Institute for Genomic Health, New York City, NY, USA
| | - Sravani Gannavarapu
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Lohitha Chintham Reddy
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Fatma Nasoz
- Department of Computer Science, University of Nevada Las Vegas, Las Vegas, NV, USA
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA
| | - Mira V Han
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
- Nevada Institute of Personalized Medicine, Las Vegas, NV, USA.
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250
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Zhang X, Wang H, Sun C. BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation. J Cancer Res Clin Oncol 2024; 150:237. [PMID: 38713378 PMCID: PMC11076393 DOI: 10.1007/s00432-024-05740-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/03/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies. METHODS Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports. RESULTS In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers. CONCLUSION The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.
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Affiliation(s)
- Xin Zhang
- Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, 100176, China
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Huiyu Wang
- Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, 100176, China
| | - Chunyun Sun
- Beijing Engineering Research Center of Protein and Antibody, Sinocelltech Ltd., Beijing, 100176, China.
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