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Zhao K, Ebrahimie E, Mohammadi-Dehcheshmeh M, Lewsey MG, Zheng L, Hoogenraad NJ. Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis. Comput Biol Med 2024; 172:108233. [PMID: 38452471 DOI: 10.1016/j.compbiomed.2024.108233] [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: 09/10/2023] [Revised: 01/23/2024] [Accepted: 02/25/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies. METHODS Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining. RESULTS Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle. CONCLUSIONS The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.
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Affiliation(s)
- Kening Zhao
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia.
| | - Esmaeil Ebrahimie
- Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, 3086, Australia; School of Animal and Veterinary Science, The University of Adelaide, Adelaide, SA 5371, Australia; School of BioSciences, The University of Melbourne, Melbourne, VIC, 3010, Australia.
| | - Manijeh Mohammadi-Dehcheshmeh
- Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, 3086, Australia; School of Animal and Veterinary Science, The University of Adelaide, Adelaide, SA 5371, Australia.
| | - Mathew G Lewsey
- Australian Research Council Research Hub for Medicinal Agriculture, La Trobe University, AgriBio Building, Bundoora, VIC, 3086, Australia; La Trobe Institute for Sustainable Agriculture and Food, Department of Plant, Animal and Soil Sciences, La Trobe University, AgriBio Building, Bundoora, VIC, 3086, Australia; Australian Research Council Centre of Excellence in Plants for Space, AgriBio Building, La Trobe University, Bundoora, VIC, 3086, Australia.
| | - Lei Zheng
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Nick J Hoogenraad
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia; Tumour Targeting Laboratory, Olivia Newton-John Cancer Research Institute, School of Cancer Medicine, La Trobe University, Melbourne, VIC, 3084, Australia.
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Chaudhury R, Chakraborty A, Rahaman F, Sarkar T, Dey S, Das M. Mycorrhization in trees: ecology, physiology, emerging technologies and beyond. PLANT BIOLOGY (STUTTGART, GERMANY) 2024; 26:145-156. [PMID: 38194349 DOI: 10.1111/plb.13613] [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: 07/02/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Mycorrhization has been an integral part of plants since colonization by the early land plants. Over decades, substantial research has highlighted its potential role in improving nutritional efficiency and growth, development and survival of crop plants. However, the focus of this review is trees. Evidence have been provided to explain ecological and physiological significance of mycorrhization in trees. Advances in recent technologies (e.g., metagenomics, artificial intelligence, machine learning, agricultural drones) may open new windows to apply this knowledge in promoting tree growth in forest ecosystems. Dual mycorrhization relationships in trees and even triple relationships among trees, mycorrhizal fungi and bacteria offer an interesting physiological system to understand how plants interact with other organisms for better survival. Besides, studies indicate additional roles of mycorrhization in learning, memorizing and communication between host trees through a common mycorrhizal network (CMN). Recent observations in trees suggest that mycorrhization may even promote tolerance to multiple abiotic (e.g., drought, salt, heavy metal stress) and biotic (e.g. fungi) stresses. Due to the extent of physiological reliance, local adaptation of trees is heavily impacted by the mycorrhizal community. This knowledge opens the possibility of a non-GMO avenue to promote tree growth and development. Indeed, mycorrhization could impact growth of trees in nurserys and subsequent survival of the inoculated trees in field conditions. Future studies might integrate hyperspectral imaging and drone technologies to identify tree communities that are deficient in nitrogen and spray mycorrhizal spore formulations on them.
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Affiliation(s)
- R Chaudhury
- Department of Life Sciences, Presidency University, Kolkata, India
| | - A Chakraborty
- Department of Life Sciences, Presidency University, Kolkata, India
| | - F Rahaman
- Department of Life Sciences, Presidency University, Kolkata, India
| | - T Sarkar
- Department of Life Sciences, Presidency University, Kolkata, India
| | - S Dey
- Department of Life Sciences, Presidency University, Kolkata, India
| | - M Das
- Department of Life Sciences, Presidency University, Kolkata, India
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Kimotho RN, Maina S. Unraveling plant-microbe interactions: can integrated omics approaches offer concrete answers? JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1289-1313. [PMID: 37950741 PMCID: PMC10901211 DOI: 10.1093/jxb/erad448] [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: 05/26/2023] [Accepted: 11/08/2023] [Indexed: 11/13/2023]
Abstract
Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help provide deeper insights into plant-microbe interactions in a concerted way. Integrating multi-omics datasets can transform our understanding of the plant microbiome from unspecified genetic influences on interacting species to specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies in crop microbiome omics research and review key aspects of how the integration of host and microbial omics-based datasets can be used to provide a comprehensive outline of complex crop-microbe interactions. We describe how these technological advances have helped unravel crucial plant and microbial genes and pathways that control beneficial, pathogenic, and commensal plant-microbe interactions. We identify crucial knowledge gaps and synthesize current limitations in our understanding of crop microbiome omics approaches. We highlight recent studies in which multi-omics-based approaches have led to improved models of crop microbial community structure and function. Finally, we recommend holistic approaches in integrating host and microbial omics datasets to achieve precision and efficiency in data analysis, which is crucial for biotic and abiotic stress control and in understanding the contribution of the microbiota in shaping plant fitness.
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Affiliation(s)
- Roy Njoroge Kimotho
- Hebei Key Laboratory of Soil Ecology, Key Laboratory of Agricultural Water Resources, Centre for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050021, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Solomon Maina
- Elizabeth Macarthur Agricultural Institute, NSW Department of Primary Industries, Menangle, New South Wales 2568, Australia
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4
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Rezaei Z, Tahmasebi A, Pourabbas B. Using meta-analysis and machine learning to investigate the transcriptional response of immune cells to Leishmania infection. PLoS Negl Trop Dis 2024; 18:e0011892. [PMID: 38190401 PMCID: PMC10798641 DOI: 10.1371/journal.pntd.0011892] [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: 03/14/2023] [Revised: 01/19/2024] [Accepted: 12/29/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Leishmaniasis is a parasitic disease caused by the Leishmania protozoan affecting millions of people worldwide, especially in tropical and subtropical regions. The immune response involves the activation of various cells to eliminate the infection. Understanding the complex interplay between Leishmania and the host immune system is crucial for developing effective treatments against this disease. METHODS This study collected extensive transcriptomic data from macrophages, dendritic, and NK cells exposed to Leishmania spp. Our objective was to determine the Leishmania-responsive genes in immune system cells by applying meta-analysis and feature selection algorithms, followed by co-expression analysis. RESULTS As a result of meta-analysis, we discovered 703 differentially expressed genes (DEGs), primarily associated with the immune system and cellular metabolic processes. In addition, we have substantiated the significance of transcription factor families, such as bZIP and C2H2 ZF, in response to Leishmania infection. Furthermore, the feature selection techniques revealed the potential of two genes, namely G0S2 and CXCL8, as biomarkers and therapeutic targets for Leishmania infection. Lastly, our co-expression analysis has unveiled seven hub genes, including PFKFB3, DIAPH1, BSG, BIRC3, GOT2, EIF3H, and ATF3, chiefly related to signaling pathways. CONCLUSIONS These findings provide valuable insights into the molecular mechanisms underlying the response of immune system cells to Leishmania infection and offer novel potential targets for the therapeutic goals.
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Affiliation(s)
- Zahra Rezaei
- Professor Alborzi Clinical Microbiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ahmad Tahmasebi
- Professor Alborzi Clinical Microbiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Shiraz Institute for Cancer Research, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bahman Pourabbas
- Professor Alborzi Clinical Microbiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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Domingo G, Vannini C, Bracale M, Bonfante P. Proteomics as a tool to decipher plant responses in arbuscular mycorrhizal interactions: a meta-analysis. Proteomics 2023; 23:e2200108. [PMID: 36571480 DOI: 10.1002/pmic.202200108] [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: 09/20/2022] [Revised: 11/09/2022] [Accepted: 12/21/2022] [Indexed: 12/27/2022]
Abstract
The beneficial symbiosis between plants and arbuscular mycorrhizal (AM) fungi leads to a deep reprogramming of plant metabolism, involving the regulation of several molecular mechanisms, many of which are poorly characterized. In this regard, proteomics is a powerful tool to explore changes related to plant-microbe interactions. This study provides a comprehensive proteomic meta-analysis conducted on AM-modulated proteins at local (roots) and systemic (shoots/leaves) level. The analysis was implemented by an in-depth study of root membrane-associated proteins and by a comparison with a transcriptome meta-analysis. A total of 4262 differentially abundant proteins were retrieved and, to identify the most relevant AM-regulated processes, a range of bioinformatic studies were conducted, including functional enrichment and protein-protein interaction network analysis. In addition to several protein transporters which are present in higher amounts in AM plants, and which are expected due to the well-known enhancement of AM-induced mineral uptake, our analysis revealed some novel traits. We detected a massive systemic reprogramming of translation with a central role played by the ribosomal translational apparatus. On one hand, these new protein-synthesis efforts well support the root cellular re-organization required by the fungal penetration, and on the other they have a systemic impact on primary metabolism.
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Affiliation(s)
- Guido Domingo
- Biotechnology and Life Science Department, University of Insubria, Varese, Italy
| | - Candida Vannini
- Biotechnology and Life Science Department, University of Insubria, Varese, Italy
| | - Marcella Bracale
- Biotechnology and Life Science Department, University of Insubria, Varese, Italy
| | - Paola Bonfante
- Department of Life Sciences and Systems Biology, Università degli Studi di Torino, Torino, Italy
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Tahmasebi A, Niazi A, Akrami S. Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species. Sci Rep 2023; 13:847. [PMID: 36646724 PMCID: PMC9842770 DOI: 10.1038/s41598-023-27746-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 01/06/2023] [Indexed: 01/18/2023] Open
Abstract
In Populus, drought is a major problem affecting plant growth and development which can be closely reflected by corresponding transcriptomic changes. Nevertheless, how these changes in Populus are not fully understood. Here, we first used meta-analysis and machine learning methods to identify water stress-responsive genes and then performed a systematic approach to discover important gene networks. Our analysis revealed that large transcriptional variations occur during drought stress. These changes were more associated with the response to stress, cellular catabolic process, metabolic pathways, and hormone-related genes. The differential gene coexpression analysis highlighted two acetyltransferase NATA1-like and putative cytochrome P450 genes that have a special contribution in response to drought stress. In particular, the findings showed that MYBs and MAPKs have a prominent role in the drought stress response that could be considered to improve the drought tolerance of Populus. We also suggest ARF2-like and PYL4-like genes as potential markers for use in breeding programs. This study provides a better understanding of how Populus responses to drought that could be useful for improving tolerance to stress in Populus.
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Affiliation(s)
- Ahmad Tahmasebi
- Institute of Biotechnology, Shiraz University, Shiraz, 7144165186, Iran.
| | - Ali Niazi
- Institute of Biotechnology, Shiraz University, Shiraz, 7144165186, Iran.
| | - Sahar Akrami
- Institute of Biotechnology, Shiraz University, Shiraz, 7144165186, Iran
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Shahriari AG, Soltani Z, Tahmasebi A, Poczai P. Integrative System Biology Analysis of Transcriptomic Responses to Drought Stress in Soybean ( Glycine max L.). Genes (Basel) 2022; 13:1732. [PMID: 36292617 PMCID: PMC9602024 DOI: 10.3390/genes13101732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/21/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022] Open
Abstract
Drought is a major abiotic stressor that causes yield losses and limits the growing area for most crops. Soybeans are an important legume crop that is sensitive to water-deficit conditions and suffers heavy yield losses from drought stress. To improve drought-tolerant soybean cultivars through breeding, it is necessary to understand the mechanisms of drought tolerance in soybeans. In this study, we applied several transcriptome datasets obtained from soybean plants under drought stress in comparison to those grown under normal conditions to identify novel drought-responsive genes and their underlying molecular mechanisms. We found 2168 significant up/downregulated differentially expressed genes (DEGs) and 8 core modules using gene co-expression analysis to predict their biological roles in drought tolerance. Gene Ontology and KEGG analyses revealed key biological processes and metabolic pathways involved in drought tolerance, such as photosynthesis, glyceraldehyde-3-phosphate dehydrogenase and cytokinin dehydrogenase activity, and regulation of systemic acquired resistance. Genome-wide analysis of plants' cis-acting regulatory elements (CREs) and transcription factors (TFs) was performed for all of the identified DEG promoters in soybeans. Furthermore, the PPI network analysis revealed significant hub genes and the main transcription factors regulating the expression of drought-responsive genes in each module. Among the four modules associated with responses to drought stress, the results indicated that GLYMA_04G209700, GLYMA_02G204700, GLYMA_06G030500, GLYMA_01G215400, and GLYMA_09G225400 have high degrees of interconnection and, thus, could be considered as potential candidates for improving drought tolerance in soybeans. Taken together, these findings could lead to a better understanding of the mechanisms underlying drought responses in soybeans, which may useful for engineering drought tolerance in plants.
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Affiliation(s)
- Amir Ghaffar Shahriari
- Department of Agriculture and Natural Resources, Higher Education Center of Eghlid, Eghlid 7381943885, Iran
| | - Zahra Soltani
- Institute of Biotechnology, Shiraz University, Shiraz 7144113131, Iran
| | - Aminallah Tahmasebi
- Department of Agriculture, Minab Higher Education Center, University of Hormozgan, Bandar Abbas 7916193145, Iran
- Plant Protection Research Group, University of Hormozgan, Bandar Abbas 7916193145, Iran
| | - Péter Poczai
- Finnish Museum of Natural History, University of Helsinki, P.O. Box 7, FI-00014 Helsinki, Finland
- Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, FI-00065 Helsinki, Finland
- Institute of Advanced Studies Kőszeg (iASK), P.O. Box 4, H-9731 Kőszeg, Hungary
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Shojaee S, Ravash R, Shiran B, Ebrahimie E. Meta-analysis highlights the key drought responsive genes in genes: PEPC and TaSAG7 are hubs response networks. J Genet Eng Biotechnol 2022; 20:127. [PMID: 36053361 PMCID: PMC9468207 DOI: 10.1186/s43141-022-00395-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
Background Wheat is the most important cereal. One of the environmental stresses is drought that harm the production of many cereals and every year due to low rainfall and frequent droughts, the need to produce plants resistant to this stress is felt. Therefore, identification and evaluation of the genes involved in the production of this resistance in plants are of great importance. By identifying these genes and changing their expression, it is possible to produce resistant plants that can tolerate dehydration and drought, with at least a qualitative and quantitative reduction in yield. Results Based on the meta-analysis results obtained in this study, in resistant cultivars ~ 4% (2394/61290) of the probe IDs decreased and ~ 4.5% (2670/61290) increased expression, furthermore in susceptible cultivars ~ 7% (4183/61290) of probe IDs decreased and ~ 6% (3591/61290) increased expression (P value ≤ 0.05). List of up- and downregulated genes was revealed, among the expressed genes of transcription factors Myb3, ethylene-responsive 5a, MIKC-type MADS-box WM24B, and salinity inducible ERF4 in resistant cultivars and transcription factors WRKY15, MADS-box TaAGL8, WRKY39, and Myb in susceptible cultivars, they showed a significant increase in expression, these transcription factors are of great importance in drought stress. Among them, ethylene responsive 5a in resistant cultivars by 3 times and Myb in susceptible cultivars by 2.6 times have shown the highest expression change. Using Cytoscape Hub software, the Phosphoenolpyruvate carboxylase (PEPC) and lyase isocitrate (TaSAG7) genes, which have significantly different expressions in resistant and susceptible wheat cultivars. PEPC and TaSAG7 genes were upregulated in resistant wheat cultivars as well as down regulated in susceptible cultivars. Also, the qPCR results of selected genes were consistent with the outcomes of the meta-analysis. Conclusions All microarray data were collected from the NCBI Gene Expression Omnibus site. Libraries with drought-tolerant and susceptible cultivars for wheat were considered under the stress and control conditions from whole leaf tissue. By meta-analysis combined the purposeful results of multiple experiments, and found list of genes expressed in reverse between the two cultivars. These genes can distinguish between different susceptible and resistant wheat cultivars. Supplementary Information The online version contains supplementary material available at 10.1186/s43141-022-00395-4.
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Affiliation(s)
- Sahar Shojaee
- Department of Plant Breeding and Biotechnology, Shahrekord University, Shahr-e Kord, Iran
| | - Rudabeh Ravash
- Department of Plant Breeding and Biotechnology Faculty of Agriculture, Shahrekord University, Shahr-e Kord, Iran.
| | - Behrouz Shiran
- Department of Plant Breeding and Biotechnology Faculty of Agriculture, Shahrekord University, Shahr-e Kord, Iran
| | - Esmaeil Ebrahimie
- Genomics Research Platform, School of Agriculture, Biomedicine and Environment, Melbourne, La Trobe University, Victoria, 3086, Australia.,Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, South Australia, 5371, Australia
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Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells. Cells 2021; 10:cells10113139. [PMID: 34831362 PMCID: PMC8621084 DOI: 10.3390/cells10113139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/06/2021] [Accepted: 11/09/2021] [Indexed: 12/28/2022] Open
Abstract
Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.
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Ebrahimie E, Zamansani F, Alanazi IO, Sabi EM, Khazandi M, Ebrahimi F, Mohammadi-Dehcheshmeh M, Ebrahimi M. Advances in understanding the specificity function of transporters by machine learning. Comput Biol Med 2021; 138:104893. [PMID: 34598069 DOI: 10.1016/j.compbiomed.2021.104893] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 11/25/2022]
Abstract
Understanding the underlying molecular mechanism of transporter activity is one of the major discussions in structural biology. A transporter can exclusively transport one ion (specific transporter) or multiple ions (general transporter). This study compared categorical and numerical features of general and specific calcium transporters using machine learning and attribute weighting models. To this end, 444 protein features, such as the frequency of dipeptides, organism, and subcellular location, were extracted for general (n = 103) and specific calcium transporters (n = 238). Aliphatic index, subcellular location, organism, Ile-Leu frequency, Glycine frequency, hydrophobic frequency, and specific dipeptides such as Ile-Leu, Phe-Val, and Tyr-Gln were the key features in differentiating general from specific calcium transporters. Calcium transporters in the cell outer membranes were specific, while the inner ones were general; additionally, when the hydrophobic frequency or Aliphatic index is increased, the calcium transporter act as a general transporter. Random Forest with accuracy criterion showed the highest accuracy (88.88% ±5.75%) and high AUC (0.964 ± 0.020), based on 5-fold cross-validation. Decision Tree with accuracy criterion was able to predict the specificity of calcium transporter irrespective of the organism and subcellular location. This study demonstrates the precise classification of transporter function based on sequence-derived physicochemical features.
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Affiliation(s)
- Esmaeil Ebrahimie
- Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne, Victoria, 3086, Australia; School of Animal and Veterinary Sciences, The University of Adelaide, South Australia, 5371, Australia.
| | - Fatemeh Zamansani
- Department of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz, Iran.
| | - Ibrahim O Alanazi
- National Center for Biotechnology, Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 6086, Saudi Arabia.
| | - Essa M Sabi
- Department of Pathology, Clinical Biochemistry Unit, College of Medicine, King Saud University, Riyadh, 11461, Saudi Arabia.
| | - Manouchehr Khazandi
- UniSA Clinical and Health Sciences, The University of South Australia, Adelaide, 5000, Australia.
| | - Faezeh Ebrahimi
- Faculty of Life Sciences and Biotechnology, Department of Microbiology and Microbial Biotechnology, Shahid Beheshti University, Tehran, Iran.
| | | | - Mansour Ebrahimi
- School of Animal and Veterinary Sciences, The University of Adelaide, South Australia, 5371, Australia; Department of Biology, School of Basic Sciences, University of Qom, Qom, Iran.
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Cheng J, Liu HP, Lin WY, Tsai FJ. Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease. Sci Rep 2021; 11:13704. [PMID: 34211065 PMCID: PMC8249453 DOI: 10.1038/s41598-021-93085-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.
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Affiliation(s)
- Jack Cheng
- grid.254145.30000 0001 0083 6092Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402 Taiwan ,grid.411508.90000 0004 0572 9415Department of Medical Research, China Medical University Hospital, Taichung, 40447 Taiwan
| | - Hsin-Ping Liu
- grid.254145.30000 0001 0083 6092Graduate Institute of Acupuncture Science, College of Chinese Medicine, China Medical University, Taichung, 40402 Taiwan
| | - Wei-Yong Lin
- grid.254145.30000 0001 0083 6092Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402 Taiwan ,grid.411508.90000 0004 0572 9415Department of Medical Research, China Medical University Hospital, Taichung, 40447 Taiwan ,grid.254145.30000 0001 0083 6092Brain Diseases Research Center, China Medical University, Taichung, 40402 Taiwan
| | - Fuu-Jen Tsai
- grid.411508.90000 0004 0572 9415Department of Medical Research, China Medical University Hospital, Taichung, 40447 Taiwan ,grid.254145.30000 0001 0083 6092School of Chinese Medicine, China Medical University, Taichung, 40402 Taiwan ,grid.252470.60000 0000 9263 9645Department of Medical Laboratory and Biotechnology, Asia University, Taichung, 41354 Taiwan ,grid.254145.30000 0001 0083 6092Division of Pediatric Genetics, Children’s Hospital of China Medical University, Taichung, 40447 Taiwan
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Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations. Animals (Basel) 2021; 11:ani11061638. [PMID: 34205858 PMCID: PMC8227403 DOI: 10.3390/ani11061638] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/21/2021] [Accepted: 05/28/2021] [Indexed: 12/02/2022] Open
Abstract
Simple Summary Invisible (subclinical) mastitis decreases milk quality and production. Invisible mastitis is linked to an increased use of antimicrobials. The risk of the emergence of antimicrobial-resistant bacteria is a major public health concern worldwide. Therefore, early detection of infected cows is of great importance. Machine learning has opened a new avenue for early mastitis prediction based on simple and accessible milking parameters, such as milk volume, fat, protein, lactose, electrical conductivity (EC), milking time, and milking peak flow. However, farm heterogeneity is a major challenge where multiple patterns can predict mastitis. Here, we employed a classification based on associations and scaling approach for multiple pattern discovery over multiple years. The approach we have developed helps to address farm heterogeneity and generalise machine learning-based diagnosis of mastitis worldwide. Abstract Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence (>70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.
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Survey of artificial intelligence approaches in the study of anthropogenic impacts on symbiotic organisms – a holistic view. Symbiosis 2021. [DOI: 10.1007/s13199-021-00778-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Mohammadi-Dehcheshmeh M, Moghbeli SM, Rahimirad S, Alanazi IO, Shehri ZSA, Ebrahimie E. A Transcription Regulatory Sequence in the 5' Untranslated Region of SARS-CoV-2 Is Vital for Virus Replication with an Altered Evolutionary Pattern against Human Inhibitory MicroRNAs. Cells 2021; 10:cells10020319. [PMID: 33557205 PMCID: PMC7913991 DOI: 10.3390/cells10020319] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/23/2021] [Accepted: 01/27/2021] [Indexed: 12/12/2022] Open
Abstract
Our knowledge of the evolution and the role of untranslated region (UTR) in SARS-CoV-2 pathogenicity is very limited. Leader sequence, originated from UTR, is found at the 5' ends of all encoded SARS-CoV-2 transcripts, highlighting its importance. Here, evolution of leader sequence was compared between human pathogenic and non-pathogenic coronaviruses. Then, profiling of microRNAs that can inactivate the key UTR regions of coronaviruses was carried out. A distinguished pattern of evolution in leader sequence of SARS-CoV-2 was found. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with a stable thermodynamic binding energy. Notably, SARS-CoV-2 had a lower binding stability against microRNAs. hsa-MIR-5004-3p was the only human microRNA able to target the leader sequence of SARS and to a lesser extent, also SARS-CoV-2. However, its binding stability decreased remarkably in SARS-COV-2. We found some plant microRNAs with low and stable binding energy against SARS-COV-2. Meta-analysis documented a significant (p < 0.01) decline in the expression of MIR-5004-3p after SARS-COV-2 infection in trachea, lung biopsy, and bronchial organoids as well as lung-derived Calu-3 and A549 cells. The paucity of the innate human inhibitory microRNAs to bind to leader sequence of SARS-CoV-2 can contribute to its high replication in infected human cells.
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Affiliation(s)
- Manijeh Mohammadi-Dehcheshmeh
- La Trobe Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC 3086, Australia; or
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA 5371, Australia
| | - Sadrollah Molaei Moghbeli
- Department of Animal Science, College of Agricultural and Life Sciences, University of Wisconsin, Madison, WI 1675, USA;
| | - Samira Rahimirad
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, Tehran 1497716316, Iran;
| | - Ibrahim O. Alanazi
- National Center for Biotechnology, Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 6086, Saudi Arabia;
| | - Zafer Saad Al Shehri
- Department of Medical Laboratories, College of Applied Medical Sciences, Shaqra University, KSA, Al dawadmi 1678, Saudi Arabia;
| | - Esmaeil Ebrahimie
- La Trobe Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC 3086, Australia; or
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA 5371, Australia
- School of BioSciences, The University of Melbourne, Melbourne, VIC 3052, Australia
- Correspondence: ; Tel.: +61-4491-213-57
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Lee T, Lee H. Prediction of Alzheimer's disease using blood gene expression data. Sci Rep 2020; 10:3485. [PMID: 32103140 PMCID: PMC7044318 DOI: 10.1038/s41598-020-60595-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 02/11/2020] [Indexed: 12/13/2022] Open
Abstract
Identification of AD (Alzheimer's disease)-related genes obtained from blood samples is crucial for early AD diagnosis. We used three public datasets, ADNI, AddNeuroMed1 (ANM1), and ANM2, for this study. Five feature selection methods and five classifiers were used to curate AD-related genes and discriminate AD patients, respectively. In the internal validation (five-fold cross-validation within each dataset), the best average values of the area under the curve (AUC) were 0.657, 0.874, and 0.804 for ADNI, ANMI, and ANM2, respectively. In the external validation (training and test sets from different datasets), the best AUCs were 0.697 (training: ADNI to testing: ANM1), 0.764 (ADNI to ANM2), 0.619 (ANM1 to ADNI), 0.79 (ANM1 to ANM2), 0.655 (ANM2 to ADNI), and 0.859 (ANM2 to ANM1), respectively. These results suggest that although the classification performance of ADNI is relatively lower than that of ANM1 and ANM2, classifiers trained using blood gene expression can be used to classify AD for other data sets. In addition, pathway analysis showed that AD-related genes were enriched with inflammation, mitochondria, and Wnt signaling pathways. Our study suggests that blood gene expression data are useful in predicting the AD classification.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea.
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea.
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
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Sakamoto K, Ogiwara N, Kaji T, Sugimoto Y, Ueno M, Sonoda M, Matsui A, Ishida J, Tanaka M, Totoki Y, Shinozaki K, Seki M. Transcriptome analysis of soybean (Glycine max) root genes differentially expressed in rhizobial, arbuscular mycorrhizal, and dual symbiosis. JOURNAL OF PLANT RESEARCH 2019; 132:541-568. [PMID: 31165947 DOI: 10.1007/s10265-019-01117-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 05/25/2019] [Indexed: 05/11/2023]
Abstract
Soybean (Glycine max) roots establish associations with nodule-inducing rhizobia and arbuscular mycorrhizal (AM) fungi. Both rhizobia and AM fungi have been shown to affect the activity of and colonization by the other, and their interactions can be detected within host plants. Here, we report the transcription profiles of genes differentially expressed in soybean roots in the presence of rhizobial, AM, or rhizobial-AM dual symbiosis, compared with those in control (uninoculated) roots. Following inoculation, soybean plants were grown in a glasshouse for 6 weeks; thereafter their root transcriptomes were analyzed using an oligo DNA microarray. Among the four treatments, the root nodule number and host plant growth were highest in plants with dual symbiosis. We observed that the expression of 187, 441, and 548 host genes was up-regulated and 119, 1,439, and 1,298 host genes were down-regulated during rhizobial, AM, and dual symbiosis, respectively. The expression of 34 host genes was up-regulated in each of the three symbioses. These 34 genes encoded several membrane transporters, type 1 metallothionein, and transcription factors in the MYB and bHLH families. We identified 56 host genes that were specifically up-regulated during dual symbiosis. These genes encoded several nodulin proteins, phenylpropanoid metabolism-related proteins, and carbonic anhydrase. The nodulin genes up-regulated by the AM fungal colonization probably led to the observed increases in root nodule number and host plant growth. Some other nodulin genes were down-regulated specifically during AM symbiosis. Based on the results above, we suggest that the contribution of AM fungal colonization is crucial to biological N2-fixation and host growth in soybean with rhizobial-AM dual symbiosis.
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Affiliation(s)
- Kazunori Sakamoto
- Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510, Japan.
| | - Natsuko Ogiwara
- Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510, Japan
| | - Tomomitsu Kaji
- JA ZEN-NOH Research and Development Center, 4-18-1 Higashiyawata, Hiratsuka, Kanagawa, 254-0016, Japan
| | - Yurie Sugimoto
- Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510, Japan
| | - Mitsuru Ueno
- Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510, Japan
| | - Masatoshi Sonoda
- Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510, Japan
| | - Akihiro Matsui
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Junko Ishida
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Maho Tanaka
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Yasushi Totoki
- Division of Cancer Genomics, National Cancer Center Research Institute, Chuo-ku, Tokyo, 104-0045, Japan
| | - Kazuo Shinozaki
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Motoaki Seki
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- Kihara Institute for Biological Research, Yokohama City University, 641-12 Maioka-cho, Totsuka-ku, Yokohama, Kanagawa, 244-0813, Japan
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Integrative meta-analysis of transcriptomic responses to abiotic stress in cotton. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 146:112-122. [PMID: 30802474 DOI: 10.1016/j.pbiomolbio.2019.02.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 02/14/2019] [Accepted: 02/15/2019] [Indexed: 01/09/2023]
Abstract
Abiotic environmental stresses are important factors that limit the growth, fiber yield, and quality of cotton. In this study, an integrative meta-analysis and a system-biology analysis were performed to explore the underlying transcriptomic mechanisms that are critical for response to stresses. From the meta-analysis, it was observed that a total of 1465 differentially expressed genes (DEGs) between normal and stress conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that DEGs were significantly enriched in the ubiquitin-dependent process, biosynthesis of secondary metabolites, plant hormone, and signaled transduction. The results also indicated that some of DEGs were assigned to transcription factors (TFs). A total of 148 TFs belonged to 25 conserved families were identified that among them S1Fa-like, ERF, NAC, bZIP families, were the most abundant groups. Moreover, we searched in upstream regions of DEGs for over-represented DNA motifs and were able to identify 11 conserved sequence motifs. The functional analysis of these motifs revealed that they were involved in regulation of transcription, DNA replication, cytoskeleton organization, and translation. Weighted gene co-expression network analysis (WGCNA) uncovered 12 distinct co-expression modules. Four modules were significantly associated with genes involved in response to stress and cell wall organization. The network analysis also identified hub genes such as RTNLB5 and PRA1, which may be involved in regulating stress response. The findings could help to understand the mechanisms of response to abiotic stress and introduce candidate genes that may be beneficial to cotton plant breeding programs.
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Kargarfard F, Sami A, Hemmatzadeh F, Ebrahimie E. Identifying mutation positions in all segments of influenza genome enables better differentiation between pandemic and seasonal strains. Gene 2019; 697:78-85. [PMID: 30769139 DOI: 10.1016/j.gene.2019.01.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Revised: 12/29/2018] [Accepted: 01/17/2019] [Indexed: 01/08/2023]
Abstract
Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I, and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.
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Affiliation(s)
- Fatemeh Kargarfard
- Faculty of Engineering and IT, University of Technology Sydney, New South Wales, Australia; Department of Computer Science and Engineering, School of Electrical Engineering and Computer, Shiraz University, Shiraz, Iran
| | - Ashkan Sami
- Department of Computer Science and Engineering, School of Electrical Engineering and Computer, Shiraz University, Shiraz, Iran
| | - Farhid Hemmatzadeh
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, Australia
| | - Esmaeil Ebrahimie
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, Australia; Genomics Research Platform, La Trobe University, Melbourne, Victoria 3086, Australia; School of Information Technology and Mathematical Sciences, Division of Information Technology Engineering & Environment, University of South Australia, Adelaide, Australia; School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, Australia.
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19
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Tahmasebi A, Ebrahimie E, Pakniyat H, Ebrahimi M, Mohammadi-Dehcheshmeh M. Tissue-specific transcriptional biomarkers in medicinal plants: Application of large-scale meta-analysis and computational systems biology. Gene 2019; 691:114-124. [PMID: 30620887 DOI: 10.1016/j.gene.2018.12.056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 12/01/2018] [Accepted: 12/27/2018] [Indexed: 12/18/2022]
Abstract
Biosynthesis of secondary metabolites in plant is a complex process, regulated by many genes and influenced by several factors. In recent years, the next-generation sequencing (NGS) technology and advanced statistical analysis such as meta-analysis and computational systems biology have provided novel opportunities to overcome biological complexity. Here, we performed a meta-analysis on publicly available transcriptome datasets of twelve economically significant medicinal plants to identify differentially expressed genes (DEGs) between shoot and root tissues and to find the key molecular features which may be effective in the biosynthesis of secondary metabolites. Meta-analysis identified a total of 880 genes with differential expression between two tissues. Functional enrichment and KEGG pathway analysis indicated that the functions of those DEGs are highly associated with the developmental process, starch metabolic process, response to stimulus, porphyrin and chlorophyll metabolism, biosynthesis of secondary metabolites and phenylalanine metabolism. In addition, systems biology analysis of the DEGs was applied to find protein-protein interaction network and discovery of significant modules. The detected modules were associated with hormone signal transduction, transcription repressor activity, response to light stimulus and epigenetic processes. Finally, analysis was extended to search for putative miRNAs that are associated with DEGs. A total of 31 miRNAs were detected which belonged to 16 conserved families. The present study provides a comprehensive view to better understand the tissue-specific expression of genes and mechanisms involved in secondary metabolites synthesis and may provide candidate genes for future researches to improve yield of secondary metabolites.
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Affiliation(s)
- Ahmad Tahmasebi
- Department of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz 7144165186, Iran
| | - Esmaeil Ebrahimie
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide 5005, Australia; Institute of Biotechnology, Shiraz University, Shiraz 7144165186, Iran; Division of Information Technology, Engineering and the Environment, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide 5005, Australia; School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide 5005, Australia.
| | - Hassan Pakniyat
- Department of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz 7144165186, Iran
| | - Mansour Ebrahimi
- Department of Biology, University of Qom, Qom, 371514661, Iran; Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide 5005, Australia
| | - Manijeh Mohammadi-Dehcheshmeh
- Institute of Biotechnology, Shiraz University, Shiraz 7144165186, Iran; Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide 5005, Australia
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