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Montazerinezhad S, Solouki M, Emamjomeh A, Kavousi K, Taheri A, Shiri Y. Transcriptomic analysis of alternative splicing events for different stages of growth and development in Sistan Yaghooti grape clusters. Gene 2024; 896:148030. [PMID: 38008270 DOI: 10.1016/j.gene.2023.148030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/31/2023] [Accepted: 11/22/2023] [Indexed: 11/28/2023]
Abstract
Sistan Yaghooti grape variety, despite characteristics such as early ripening, is vulnerable to cluster rot due to small berries and dense clusters. In this regard, AS may serve as a regulatory mechanism during developmental processes and in response to environmental signals. RNA-Seq analysis was performed to measure gene expression and the extent of AS events in the cluster growth and development stages of Sistan Yaghooti grape. The number of AS events increased during stages, suggesting that it contributes to the grapevine's adaptability to various stresses. In addition, DEG and DAS genes showed little overlap in cluster growth stages. Functional analysis of 19,194 DAS -gene sets showed that VIT_06s0004g06670 gene is involved in the activation of calcium channels (Ca2+) through the activation of 5 PLC biosynthetic pathways. Among the 27,229 DEG -sets, VIT_07s0005g05320 gene showed higher expression. Interestingly, this gene is involved in the synthesis of an EF -hand domain-containing protein capable of binding to Ca2+ by activating 4 biochemical pathways. These genes increase cytosolic Ca2+ concentration, enhancing plant stress tolerance and resistance to cracking. These results show that AS can respond independently to different types of stress. Among the other DAS genes, the GA2ox gene (VvGA2ox) showed an increase in AS events during cluster development. This gene is critical for initiating the degradation process of GA and plays a crucial role in different stages of seed development. Therefore, it is very likely that this gene is one of the main factors responsible for the density and seedlessness of Sistan Yaghooti grape.
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Affiliation(s)
- Somayeh Montazerinezhad
- Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Mahmood Solouki
- Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Abbasali Emamjomeh
- Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran; Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Bioinformatics, Faculty of Basic Sciences, University of Zabol, Zabol, Iran.
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics (IBB), Department of Bioinformatics, Laboratory of Complex Biological Systems and Bioinformatics (CBB), University of Tehran, Tehran, Iran
| | - Ali Taheri
- Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, Nashville, Tenn, United States
| | - Yasoub Shiri
- Agronomy and Plant Breeding Department, Agriculture Research Center, Zabol Research Institute, Zabol, Iran; Department of Horticulture, Faculty of Agriculture and Natural Resources, Mohaghegh Ardabili University, Ardabil, Iran
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Ariaeenejad S, Gharechahi J, Foroozandeh Shahraki M, Fallah Atanaki F, Han JL, Ding XZ, Hildebrand F, Bahram M, Kavousi K, Hosseini Salekdeh G. Precision enzyme discovery through targeted mining of metagenomic data. Nat Prod Bioprospect 2024; 14:7. [PMID: 38200389 PMCID: PMC10781932 DOI: 10.1007/s13659-023-00426-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024]
Abstract
Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Javad Gharechahi
- Human Genetics Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Jian-Lin Han
- Livestock Genetics Program, International Livestock Research, Institute (ILRI), Nairobi, 00100, Kenya
- CAAS-ILRI Joint Laboratory On Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Xue-Zhi Ding
- Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, 730050, China
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - Mohammad Bahram
- Department of Ecology, Swedish University of Agricultural Sciences, Ulls Väg 16, 756 51, Uppsala, Sweden
- Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai St, Tartu, Estonia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Motahar SFS, Tiyoula FN, Motamedi E, Zeinalabedini M, Kavousi K, Ariaeenejad S. Computational Insights into the Selecting Mechanism of α-Amylase Immobilized on Cellulose Nanocrystals: Unveiling the Potential of α-Amylases Immobilized for Efficient Poultry Feed Hydrolysis. Bioconjug Chem 2023; 34:2034-2048. [PMID: 37823388 DOI: 10.1021/acs.bioconjchem.3c00304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
The selection of an appropriate amylase for hydrolysis poultry feed is crucial for achieving improved digestibility and high-quality feed. Cellulose nanocrystals (CNCs), which are known for their high surface area, provide an excellent platform for enzyme immobilization. Immobilization greatly enhances the operational stability of α-amylases and the efficiency of starch bioconversion in poultry feeds. In this study, we immobilized two metagenome-derived α-amylases, PersiAmy2 and PersiAmy3, on CNCs and employed computational methods to characterize and compare the degradation efficiencies of these enzymes for poultry feed hydrolysis. Experimental in vitro bioconversion assessments were performed to validate the computational outcomes. Molecular docking studies revealed the superior hydrolysis performance of PersiAmy3, which displayed stronger electrostatic interactions with CNCs. Experimental characterization demonstrated the improved performance of both α-amylases after immobilization at high temperatures (80 °C). A similar trend was observed under alkaline conditions, with α-amylase activity reaching 88% within a pH range of 8.0 to 9.0. Both immobilized α-amylases exhibited halotolerance at NaCl concentrations up to 3 M and retained over 50% of their initial activity after 13 use cycles. Notably, PersiAmy3 displayed more remarkable improvements than PersiAmy2 following immobilization, including a significant increase in activity from 65 to 80.73% at 80 °C, an increase in activity to 156.48% at a high salinity of 3 M NaCl, and a longer half-life, indicating greater thermal stability within the range of 60 to 80 °C. These findings were substantiated by the in vitro hydrolysis of poultry feed, where PersiAmy3 generated 53.53 g/L reducing sugars. This comprehensive comparison underscores the utility of computational methods as a faster and more efficient approach for selecting optimal enzymes for poultry feed hydrolysis, thereby providing valuable insights into enhancing feed digestibility and quality.
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Affiliation(s)
- Seyedeh Fatemeh Sadeghian Motahar
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj 31535-1897, Iran
| | - Fereshteh Noroozi Tiyoula
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Elaheh Motamedi
- Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Research and Extension Organization (AREEO), Karaj 55555, Iran
| | - Mehrshad Zeinalabedini
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj 31535-1897, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj 31535-1897, Iran
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Jahanshahi DA, Ariaeenejad S, Kavousi K. A metagenomic catalog for exploring the plastizymes landscape covering taxa, genes, and proteins. Sci Rep 2023; 13:16029. [PMID: 37749380 PMCID: PMC10519993 DOI: 10.1038/s41598-023-43042-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/18/2023] [Indexed: 09/27/2023] Open
Abstract
There are significant environmental and health concerns associated with the current inefficient plastic recycling process. This study presents the first integrated reference catalog of plastic-contaminated environments obtained using an insilico workflow that could play a significant role in discovering new plastizymes. Here, we combined 66 whole metagenomic data from plastic-contaminated environment samples from four previously collected metagenome data with our new sample. In this study, an integrated plastic-contaminated environment gene, protein, taxa, and plastic degrading enzyme catalog (PDEC) was constructed. These catalogs contain 53,300,583 non-redundant genes and proteins, 691 metagenome-assembled genomes, and 136,654 plastizymes. Based on KEGG and eggNOG annotations, 42% of recognized genes lack annotations, indicating their functions remain elusive and warrant further investigation. Additionally, the PDEC catalog highlights hydrolases, peroxidases, and cutinases as the prevailing plastizymes. Ultimately, following multiple validation procedures, our effort focused on pinpointing enzymes that exhibited the highest similarity to the introduced plastizymes in terms of both sequence and three-dimensional structural aspects. This encompassed evaluating the linear composition of constituent units as well as the complex spatial conformation of the molecule. The resulting catalog is expected to improve the resolution of future multi-omics studies, providing new insights into plastic-pollution related research.
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Affiliation(s)
- Donya Afshar Jahanshahi
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Piryaei Z, Salehi Z, Ebrahimie E, Ebrahimi M, Kavousi K. Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer. BMC Med Genomics 2023; 16:219. [PMID: 37715225 PMCID: PMC10503144 DOI: 10.1186/s12920-023-01655-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression. METHODS In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines. RESULTS Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated. CONCLUSION The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.
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Affiliation(s)
- Zeynab Piryaei
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Zahra Salehi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Ebrahimie
- Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, Australia
| | - Mansour Ebrahimi
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Samadishadlou M, Rahbarghazi R, Piryaei Z, Esmaeili M, Avcı ÇB, Bani F, Kavousi K. Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis. Cardiovasc Diabetol 2023; 22:247. [PMID: 37697288 PMCID: PMC10496209 DOI: 10.1186/s12933-023-01957-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/10/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation. METHODS In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set. RESULTS We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data. CONCLUSIONS Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.
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Affiliation(s)
- Mehrdad Samadishadlou
- Department of Medical Nanotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Rahbarghazi
- Stem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Applied Cell Sciences, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Zeynab Piryaei
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mahdad Esmaeili
- Medical Bioengineering Department, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Çığır Biray Avcı
- Medical Biology Department, School of Medicine, Ege University, İzmir, Türkiye
| | - Farhad Bani
- Department of Medical Nanotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Khoshnejat M, Banaei-Moghaddam AM, Moosavi-Movahedi AA, Kavousi K. A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients. PLoS One 2023; 18:e0287325. [PMID: 37319295 PMCID: PMC10270629 DOI: 10.1371/journal.pone.0287325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated.
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Affiliation(s)
- Maryam Khoshnejat
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Ali Mohammad Banaei-Moghaddam
- The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- Laboratory of Genomics and Epigenomics (LGE), Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Ali Akbar Moosavi-Movahedi
- The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
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Ariaeenejad S, Kavousi K, Han JL, Ding XZ, Hosseini Salekdeh G. Efficiency of an alkaline, thermostable, detergent compatible, and organic solvent tolerant lipase with hydrolytic potential in biotreatment of wastewater. Sci Total Environ 2023; 866:161066. [PMID: 36565882 DOI: 10.1016/j.scitotenv.2022.161066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Discharging the tannery wastewater into the environment is a serious challenge worldwide due to the release of severe recalcitrant pollutants such as oil compounds and organic materials. The biological treatment through enzymatic hydrolysis is a cheap and eco-friendly method for eliminating fatty substances from wastewater. In this context, lipases can be utilized for bio-treatment of wastewater in multifaceted industrial applications. To overcome the limitations in removing pollutants in the effluent, we aimed to identify a novel robust stable lipase (PersiLipase1) from metagenomic data of tannery wastewater for effective bio-degradation of the oily wastewater pollution. The lipase displayed remarkable thermostability and maintained over 81 % of its activity at 60 °C.After prolonged incubation for 35 days at 60°C, the PersiLipase1 still maintained 53.9 % of its activity. The enzyme also retained over 67 % of its activity in a wide range of pH (4.0 to 9.0). In addition, PersiLipase1 demonstrated considerable tolerance toward metal ions and organic solvents (e.g., retaining >70% activity after the addition of 100 mM of chemicals). Hydrolysis of olive oil and sheep fat by this enzyme showed 100 % efficiency. Furthermore, the PersiLipase1 proved to be efficient for biotreatment of oil and grease from tannery wastewater with the hydrolysis efficiency of 90.76 % ± 0.88. These results demonstrated that the metagenome-derived PersiLipase1 from tannery wastewater has a promising potential for the biodegradation and management of oily wastewater pollution.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Jian-Lin Han
- Livestock Genetics Program, International Livestock Research Institute (ILRI), 00100 Nairobi, Kenya; CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Xue-Zhi Ding
- Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou 730050, China
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Ariaeenejad S, Kavousi K, Zolfaghari B, Roy S, Koshiba T, Hosseini Salekdeh G. Efficient bioconversion of lignocellulosic waste by a novel computationally screened hyperthermostable enzyme from a specialized microbiota. Ecotoxicol Environ Saf 2023; 252:114587. [PMID: 36758508 DOI: 10.1016/j.ecoenv.2023.114587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 01/24/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
A large amount of lignocellulosic waste is generated every day in the world, and their accumulation in the agroecosystems, integration in soil compositions, or incineration for energy production has severe environmental pollution effects. Using enzymes as biocatalysts for the biodegradation of lignocellulosic materials, especially in harsh processing conditions, is a practical step towards green energy and environmental biosafety. Hence, the current study focuses on enzyme computationally screened from camel rumen metagenomics data as specialized microbiota that have the capacity to degrade lignocellulosic-rich and recalcitrant materials. The novel hyperthermostable xylanase named PersiXyn10 with the performance at extreme conditions was proper activity within a broad temperature (30-100 ℃) and pH range (4.0-11.0) but showed the maximum xylanolytic activity in severe alkaline and temperature conditions, pH 8.0 and temperature 90 ℃. Also, the enzyme had highly resistant to metals, surfactants, and organic solvents in optimal conditions. The introduced xylanase had unique properties in terms of thermal stability by maintaining over 82% of its activity after 15 days of incubation at 90 ℃. Considering the crucial role of hyperthermostable xylanases in the paper industry, the PersiXyn10 was subjected to biodegradation of paper pulp. The proper performance of hyperthermostable PersiXyn10 on the paper pulp was confirmed by structural analysis (SEM and FTIR) and produced 31.64 g/L of reducing sugar after 144 h hydrolysis. These results proved the applicability of the hyperthermostable xylanase in biobleaching and saccharification of lignocellulosic biomass for declining the environmental hazards.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Behrouz Zolfaghari
- CSE Department, Indian Institute of Technology (IIT) Guwahati, Guwahati, Assam, India; Department of Computer Engineering, Faculty of Engineering, Haliç University Eyüpsultan, Istanbul
| | - Swapnoneel Roy
- School of Computing, University of North Florida, Jacksonville, FL, USA
| | - Takeshi Koshiba
- Department of Mathematics, Faculty of Education and Integrated Arts and Sciences, Waseda University, Japan
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney, 2109 NSW, Australia
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Ghasemi M, Rahgozar M, Kavousi K. Complex Disease Genes Identification Using a Heterogeneous Network Embedding Approach. IEEE/ACM Trans Comput Biol Bioinform 2023; 20:875-882. [PMID: 35594221 DOI: 10.1109/tcbb.2022.3175598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Finding the causal relation between a gene and a disease using experimental approaches is a time-consuming and expensive task. However, computational approaches are cost-efficient methods for identifying candidate genes. This article proposes a new heterogeneous biological network embedding approach, named NetEM, to identify disease-associated genes. To evaluate NetEM, we examine six complex diseases, including peroxisomal disorders, sarcoma, grave's disease, lysosomal storage diseases, blood coagulation disorders, and cardiomyopathy hypertrophic. Our experiments indicate that NetEM outperforms three well-known state-of-the-art algorithms: Cardigan, DIAMOnD and GeneWanderer, in identifying disease genes. We examine TCGA data of Invasive Lobular Breast Cancer and CPTAC data of human glioblastoma as other case studies to evaluate NetEM using real data. This evaluation also indicates the validity of the method. The source codes of NetEM and data are available in the supplementary of this article.
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Kazemi‑Sefat GE, Keramatipour M, Vaezi M, Razavi SM, Kavousi K, Talebi A, Rostami S, Yaghmaie M, Chahardouli B, Talebi S, Mousavizadeh K. Author Correction: Integrated genomic sequencing in myeloid blast crisis chronic myeloid leukemia (MBC-CML), identified potentially important findings in the context of leukemogenesis model. Sci Rep 2023; 13:736. [PMID: 36639723 PMCID: PMC9839765 DOI: 10.1038/s41598-023-27872-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Golnaz Ensieh Kazemi‑Sefat
- grid.411746.10000 0004 4911 7066Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran ,grid.411746.10000 0004 4911 7066Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Keramatipour
- grid.411705.60000 0001 0166 0922Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Vaezi
- grid.411705.60000 0001 0166 0922Hematology, Oncology and Stem Cell Transplantation Research CenterShariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohsen Razavi
- grid.411746.10000 0004 4911 7066Oncopathology Research Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Kaveh Kavousi
- grid.46072.370000 0004 0612 7950Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Amin Talebi
- grid.411583.a0000 0001 2198 6209Department of Medical Genetics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Shahrbano Rostami
- grid.411705.60000 0001 0166 0922Hematology, Oncology and Stem Cell Transplantation Research CenterShariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Yaghmaie
- grid.411705.60000 0001 0166 0922Hematology, Oncology and Stem Cell Transplantation Research CenterShariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Bahram Chahardouli
- grid.411705.60000 0001 0166 0922Hematology, Oncology and Stem Cell Transplantation Research CenterShariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Talebi
- grid.411746.10000 0004 4911 7066Department of Medical Genetics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Kazem Mousavizadeh
- grid.411746.10000 0004 4911 7066Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran ,grid.411746.10000 0004 4911 7066Department of Pharmacology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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Ariaeenejad S, Motamedi E, Kavousi K, Ghasemitabesh R, Goudarzi R, Salekdeh GH, Zolfaghari B, Roy S. Enhancing the ethanol production by exploiting a novel metagenomic-derived bifunctional xylanase/β-glucosidase enzyme with improved β-glucosidase activity by a nanocellulose carrier. Front Microbiol 2023; 13:1056364. [PMID: 36687660 PMCID: PMC9845577 DOI: 10.3389/fmicb.2022.1056364] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/21/2022] [Indexed: 01/06/2023] Open
Abstract
Some enzymes can catalyze more than one chemical conversion for which they are physiologically specialized. This secondary function, which is called underground, promiscuous, metabolism, or cross activity, is recognized as a valuable feature and has received much attention for developing new catalytic functions in industrial applications. In this study, a novel bifunctional xylanase/β-glucosidase metagenomic-derived enzyme, PersiBGLXyn1, with underground β-glucosidase activity was mined by in-silico screening. Then, the corresponding gene was cloned, expressed and purified. The PersiBGLXyn1 improved the degradation efficiency of organic solvent pretreated coffee residue waste (CRW), and subsequently the production of bioethanol during a separate enzymatic hydrolysis and fermentation (SHF) process. After characterization, the enzyme was immobilized on a nanocellulose (NC) carrier generated from sugar beet pulp (SBP), which remarkably improved the underground activity of the enzyme up to four-fold at 80°C and up to two-fold at pH 4.0 compared to the free one. The immobilized PersiBGLXyn1 demonstrated 12 to 13-fold rise in half-life at 70 and 80°C for its underground activity. The amount of reducing sugar produced from enzymatic saccharification of the CRW was also enhanced from 12.97 g/l to 19.69 g/l by immobilization of the enzyme. Bioethanol production was 29.31 g/l for free enzyme after 72 h fermentation, while the immobilized PersiBGLXyn1 showed 51.47 g/l production titre. Overall, this study presented a cost-effective in-silico metagenomic approach to identify novel bifunctional xylanase/β-glucosidase enzyme with underground β-glucosidase activity. It also demonstrated the improved efficacy of the underground activities of the bifunctional enzyme as a promising alternative for fermentable sugars production and subsequent value-added products.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran,*Correspondence: Shohreh Ariaeenejad, ;
| | - Elaheh Motamedi
- Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Rezvaneh Ghasemitabesh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Razieh Goudarzi
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran,Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia,Ghasem Hosseini Salekdeh,
| | - Behrouz Zolfaghari
- Department of Integrated Art and Sciences, Faculty of Education, Waseda University, Tokyo, Japan
| | - Swapnoneel Roy
- School of Computing, University of North Florida, Jacksonville, FL, United States
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Maddi AMA, Kavousi K, Arabfard M, Ohadi H, Ohadi M. Tandem repeats ubiquitously flank and contribute to translation initiation sites. BMC Genom Data 2022; 23:59. [PMID: 35896982 PMCID: PMC9331589 DOI: 10.1186/s12863-022-01075-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/18/2022] [Indexed: 12/31/2022] Open
Abstract
Background While the evolutionary divergence of cis-regulatory sequences impacts translation initiation sites (TISs), the implication of tandem repeats (TRs) in TIS selection remains largely elusive. Here, we employed the TIS homology concept to study a possible link between TRs of all core lengths and repeats with TISs. Methods Human, as reference sequence, and 83 other species were selected, and data was extracted on the entire protein-coding genes (n = 1,611,368) and transcripts (n = 2,730,515) annotated for those species from Ensembl 102. Following TIS identification, two different weighing vectors were employed to assign TIS homology, and the co-occurrence pattern of TISs with the upstream flanking TRs was studied in the selected species. The results were assessed in 10-fold cross-validation. Results On average, every TIS was flanked by 1.19 TRs of various categories within its 120 bp upstream sequence, per species. We detected statistically significant enrichment of non-homologous human TISs co-occurring with human-specific TRs. On the contrary, homologous human TISs co-occurred significantly with non-human-specific TRs. 2991 human genes had at least one transcript, TIS of which was flanked by a human-specific TR. Text mining of a number of the identified genes, such as CACNA1A, EIF5AL1, FOXK1, GABRB2, MYH2, SLC6A8, and TTN, yielded predominant expression and functions in the human brain and/or skeletal muscle. Conclusion We conclude that TRs ubiquitously flank and contribute to TIS selection at the trans-species level. Future functional analyses, such as a combination of genome editing strategies and in vitro protein synthesis may be employed to further investigate the impact of TRs on TIS selection. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-022-01075-5.
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Bayani A, Asadi F, Hosseini A, Hatami B, Kavousi K, Aria M, Zali MR. Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis. Clin Chem Lab Med 2022; 60:1955-1962. [PMID: 36044750 DOI: 10.1515/cclm-2022-0623] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/22/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVES All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model. METHODS Four-hundred-and-ninety data from the Liver and Gastroenterology Research Center of Shahid Beheshti University of Medical Sciences in the period 2014-2021, were analyzed applying models including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression. RESULTS RF and SVM had the best results in general for all grades of EV. RF showed remarkably better results and the highest area under the curve (AUC). After that, SVM and ANN had the AUC of 98%, for grade 3, the SVM algorithm had the highest AUC after RF (89%). CONCLUSIONS The findings may help to better predict EV with high precision and accuracy and also can help reduce the burden of frequent visits to endoscopic centers. It can also help practitioners to manage cirrhosis by predicting EV with lower costs.
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Affiliation(s)
- Azadeh Bayani
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azamossadat Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Behzad Hatami
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mehrad Aria
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Bayani A, Hosseini A, Asadi F, Hatami B, Kavousi K, Aria M, Zali MR. Identifying predictors of varices grading in patients with cirrhosis using ensemble learning. Clin Chem Lab Med 2022; 60:1938-1945. [DOI: 10.1515/cclm-2022-0508] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 06/28/2022] [Indexed: 12/15/2022]
Abstract
Abstract
Objectives
The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis.
Methods
In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed.
Results
The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR).
Conclusions
Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.
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Affiliation(s)
- Azadeh Bayani
- Department of Health Information Technology and Management, School of Allied Medical Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Azamossadat Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Behzad Hatami
- Gastroenterology and Liver Diseases Research Center , Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics , Institute of Biochemistry and Biophysics (IBB), University of Tehran , Tehran , Iran
| | - Mehrdad Aria
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering , Shiraz University , Shiraz , Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center , Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences , Tehran , Iran
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Hatami B, Asadi F, Bayani A, Zali MR, Kavousi K. Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study. Clin Chem Lab Med 2022; 60:1946-1954. [PMID: 35607284 DOI: 10.1515/cclm-2022-0454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. METHODS In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3-5.2.0 64 bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms. RESULTS According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%. CONCLUSIONS We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.
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Affiliation(s)
- Behzad Hatami
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Bayani
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
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Ariaeenejad S, Kavousi K, Mamaghani ASA, Ghasemitabesh R, Hosseini Salekdeh G. Simultaneous hydrolysis of various protein-rich industrial wastes by a naturally evolved protease from tannery wastewater microbiota. Sci Total Environ 2022; 815:152796. [PMID: 34986419 DOI: 10.1016/j.scitotenv.2021.152796] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/23/2021] [Accepted: 12/27/2021] [Indexed: 06/14/2023]
Abstract
Elimination of protein-rich waste materials is one of the vital environmental protection requirements. Using of non-naturally occurring chemicals for their remediation properties can potentially induce new pollutants. Therefore, enzymes encoded in the genomes of microorganisms evolved in the same environment can be considered suitable alternatives to chemicals. Identification of efficient proteases that can hydrolyze recalcitrant, protein-rich wastes produced by various industrial processes has been widely welcomed as an eco-friendly waste management strategy. In this direction, we attempted to screen a thermo-halo-alkali-stable metagenome-derived protease (PersiProtease1) from tannery wastewater. The PersiProtease1 exhibited high pH stability over a wide range and at 1 h in pH 11.0 maintained 87.59% activity. The enzyme possessed high thermal stability while retaining 76.64% activity after 1 h at 90 °C. Moreover, 65.34% of the initial activity of the enzyme remained in the presence of 6 M NaCl, showing tolerance against high salinity. The presence of various metal ions, inhibitors, and organic solvents did not remarkably inhibit the activity of the discovered protease. The PersiProtease1 was extracted from the tannery wastewater microbiota and efficiently applied for biodegradation of real sample tannery wastewater protein, chicken feathers, whey protein, dehairing sheepskins, and waste X-ray films. PersiProtease1 proved its enormous potential in simultaneous biodegradation of solid and liquid protein-rich industrial wastes based on the results.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Atefeh Sheykh Abdollahzadeh Mamaghani
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Rezvaneh Ghasemitabesh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney 2109, NSW, Australia.
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Shahraki MF, Atanaki FF, Ariaeenejad S, Ghaffari MR, Norouzi‐Beirami MH, Maleki M, Salekdeh GH, Kavousi K. A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: a case study of lipase identification. Biotechnol Bioeng 2022; 119:1115-1128. [DOI: 10.1002/bit.28037] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 08/18/2021] [Accepted: 12/01/2021] [Indexed: 11/09/2022]
Affiliation(s)
- Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Mohammad Reza Ghaffari
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Mohammad Hossein Norouzi‐Beirami
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
- Department of Computer Engineering Osku Branch, Islamic Azad University Osku Iran
| | - Morteza Maleki
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
- Department of Molecular Sciences Macquarie University Sydney NSW Australia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
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Mousavi SH, Sadeghian Motahar SF, Salami M, Kavousi K, Sheykh Abdollahzadeh Mamaghani A, Ariaeenejad S, Salekdeh GH. Author Correction: In vitro bioprocessing of corn as poultry feed additive by the influence of carbohydrate hydrolyzing metagenome derived enzyme cocktail. Sci Rep 2022; 12:1176. [PMID: 35039619 PMCID: PMC8763862 DOI: 10.1038/s41598-022-05555-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Seyed Hossein Mousavi
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), P. O. Box, 31535-1897, Karaj, Iran
| | - Seyedeh Fatemeh Sadeghian Motahar
- Department of Food Science and Engineering, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
| | - Maryam Salami
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Atefeh Sheykh Abdollahzadeh Mamaghani
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), P. O. Box, 31535-1897, Karaj, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), P. O. Box, 31535-1897, Karaj, Iran.
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), P. O. Box, 31535-1897, Karaj, Iran. .,Departmen of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.
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Piryaei Z, Salehi Z, Tahsili MR, Ebrahimie E, Ebrahimi M, Kavousi K. Agonist/antagonist compounds' mechanism of action on estrogen receptor-positive breast cancer: A system-level investigation assisted by meta-analysis. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Salami M, Sadeghian Motahar SF, Ariaeenejad S, Sheykh Abdollahzadeh Mamaghani A, Kavousi K, Moosavi-Movahedi AA, Hosseini Salekdeh G. The novel homologue of the human α-glucosidase inhibited by the non-germinated and germinated quinoa protein hydrolysates after in vitro gastrointestinal digestion. J Food Biochem 2021; 46:e14030. [PMID: 34914113 DOI: 10.1111/jfbc.14030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/31/2021] [Accepted: 11/02/2021] [Indexed: 12/14/2022]
Abstract
Quinoa (Chenopodium quinoa Willd) is a potential source of protein with ideal amino acid profiles which its bioactive compounds can be improved during germination and gastrointestinal digestion. The present investigation studies the impact of germination for 24 hr and simulated gastrointestinal digestion on α-glucosidase inhibitory activity of the quinoa protein and bioactive peptides against the novel homologue of human α-glucosidase, PersiAlpha-GL1. The sprouted quinoa after gastroduodenal digestion was the most effective α-glucosidase inhibitor showing 81.10% α-glucosidase inhibition at concentration 4 mg/ml with the half inhibition rate (IC50 ) of 0.07 mg/ml. Based on the kinetic analysis, both the germinated and non-germinated samples before and after digestion were competitive-type inhibitors of α-glucosidase. Results of this study showed the improved α-glucosidase inhibitory activity of the quinoa bioactive peptides after germination and gastrointestinal digestion and highlighted the potential of metagenome-derived PersiAlpha-GL1 as a novel homologue of the human α-glucosidase for developing the future anti-diabetic drugs. PRACTICAL APPLICATIONS: This study aimed to evaluate the effect of germination and gastrointestinal digestion of the quinoa protein and bioactive peptides on α-glucosidase inhibitory activity against the novel PersiAlpha-GL1. Metagenomic data were used to identify the novel α-glucosidase structurally and functionally homologue of human intestinal. The results showed the highest inhibition on PersiAlpha-GL1 by a germinated quinoa after gastroduodenal digestion and confirmed the potential of PersiAlpha-GL1 to enhance the effectiveness of the anti-diabetic drugs for industrial application.
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Affiliation(s)
- Maryam Salami
- Department of Food Science and Engineering, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
| | - Seyedeh Fatemeh Sadeghian Motahar
- Department of Food Science and Engineering, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Atefeh Sheykh Abdollahzadeh Mamaghani
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
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Ariaeenejad S, Kavousi K, Maleki M, Motamedi E, Moosavi-Movahedi AA, Hosseini Salekdeh G. Application of free and immobilized novel bifunctional biocatalyst in biotransformation of recalcitrant lignocellulosic biomass. Chemosphere 2021; 285:131412. [PMID: 34329139 DOI: 10.1016/j.chemosphere.2021.131412] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 04/25/2021] [Accepted: 06/30/2021] [Indexed: 06/13/2023]
Abstract
Herein, an innovative, green, and practical biocatalyst was developed using conjugation of a novel bifunctional mannanase/xylanase biocatalyst (PersiManXyn1) to the modified cellulose nanocrystals (CNCs). Firstly, PersiManXyn1 was multi-stage in-silico screened from rumen macrobiota, and then cloned, expressed, and purified. Next, CNCs were synthesized from sugar beet pulp using enzymatic and acid hydrolysis processes, and then Fe3O4 NPs were anchored on their surface to produce magnetic CNCs (MCNCs). This hybrid was modified by dopamine providing DA/MCNCs nano-carrier. The bifunctional PersiManXyn1 demonstrated the superior hydrolysis activity on corn cob compared with the monofunctional xylanase enzyme (PersiXyn2). Moreover, the immobilization of PersiManXyn1 on the nano-carrier resulted in an improvement of the thermal stability, kinetic parameters (Kcat), and storage stability of the enzyme. Incorporation of the Fe3O4 NPs on the CNCs made magnetic nano-carrier with high magnetization value (25.8 emu/g) which exhibited rapid response toward the external magnetic fields. Hence, the immobilized biocatalyst could be easily separated from the products by a magnet, and reused up to 8 cycles with maintaining more than 50% of its original activity. The immobilized PersiManXyn1 generated 22.2%, 38.7%, and 35.1% more reducing sugars after 168 h hydrolysis of the sugar beet pulp, coffee waste, and rice straw, respectively, compared to the free enzyme. Based on the results, immobilization of the bifunctional PersiManXyn1 exhibited the superb performance of the enzyme to improve the conversion of the lignocellulosic wastes into high value products and develop the cost-competition biomass operations.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Morteza Maleki
- Department of Systems and synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Elaheh Motamedi
- Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | | | - Ghasem Hosseini Salekdeh
- Department of Systems and synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.
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Motamedi E, Kavousi K, Sadeghian Motahar SF, Reza Ghaffari M, Sheykh Abdollahzadeh Mamaghani A, Hosseini Salekdeh G, Ariaeenejad S. Efficient removal of various textile dyes from wastewater by novel thermo-halotolerant laccase. Bioresour Technol 2021; 337:125468. [PMID: 34320748 DOI: 10.1016/j.biortech.2021.125468] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 06/20/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
A novel thermostable/halotolerant metagenome-derived laccase (PersiLac2) from tannery wastewater was purified to remove textile dyes in this study. The enzyme was highly active over a wide temperature and pH range and maintained 73.35% of its initial activity after 30 days, at 50 °C. The effect of various metal and organic-solvent tolerance on PersiLac2 showed, retaining greater than 53% activity at 800 mM of metal ions, 52.12% activity at 6 M NaCl, and greater than 44.09% activity at 20% organic solvents. PersiLac2 manifested effective removal of eight different textile dyes from azo, anthraquinone, and triphenylmethane families. It decolorized 500 mg/L of Alizarin yellow, Carmine, Congo red and Bromothymol blue with 99.74-55.85% efficiency after 15 min, at 50 °C, without mediator. This enzyme could practically remove dyes from a real textile effluent and it displayed significant detoxification in rice seed germination tests. In conclusion, PersiLac2 could be useful in future for decolorization/detoxification of wastewater.
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Affiliation(s)
- Elaheh Motamedi
- Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Seyedeh Fatemeh Sadeghian Motahar
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Mohammad Reza Ghaffari
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Atefeh Sheykh Abdollahzadeh Mamaghani
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney 2109, NSW Australia
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
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24
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Ariaeenejad S, Zolfaghari B, Sadeghian Motahar SF, Kavousi K, Maleki M, Roy S, Hosseini Salekdeh G. Highly Efficient Computationally Derived Novel Metagenome α-Amylase With Robust Stability Under Extreme Denaturing Conditions. Front Microbiol 2021; 12:713125. [PMID: 34526977 PMCID: PMC8437397 DOI: 10.3389/fmicb.2021.713125] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
α-Amylases are among the very critical enzymes used for different industrial purposes. Most α-amylases cannot accomplish the requirement of industrial conditions and easily lose their activity in harsh environments. In this study, a novel α-amylase named PersiAmy1 has been identified through the multistage in silico screening pipeline from the rumen metagenomic data. The long-term storage of PersiAmy1 in low and high temperatures demonstrated 82.13 and 71.01% activities after 36 days of incubation at 4 and 50°C, respectively. The stable α-amylase retained 61.09% of its activity after 180 min of incubation at 90°C and was highly stable in a broad pH range, showing 60.48 and 86.05% activities at pH 4.0 and pH 9.0 after 180 min of incubation, respectively. Also, the enzyme could resist the high-salinity condition and demonstrated 88.81% activity in the presence of 5 M NaCl. PersiAmy1 showed more than 74% activity in the presence of various metal ions. The addition of the detergents, surfactants, and organic solvents did not affect the α-amylase activity considerably. Substrate spectrum analysis showed that PersiAmy1 could act on a wide array of substrates. PersiAmy1 showed high stability in inhibitors and superb activity in downstream conditions, thus useful in detergent and baking industries. Investigating the applicability in detergent formulation, PersiAmy1 showed more than 69% activity after incubation with commercial detergents at different temperatures (30–50°C) and retained more than 56% activity after incubation with commercial detergents for 3 h at 10°C. Furthermore, the results of the wash performance analysis exhibited a good stain removal at 10°C. The power of PersiAmy1 in the bread industry revealed soft, chewable crumbs with improved volume and porosity compared with control. This study highlights the intense power of robust novel PersiAmy1 as a functional bio-additive in many industrial applications.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Behrouz Zolfaghari
- Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India
| | - Seyedeh Fatemeh Sadeghian Motahar
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Morteza Maleki
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Swapnoneel Roy
- School of Computing, University of North Florida, Jacksonville, FL, United States
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran.,Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
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Nasiri F, Atanaki FF, Behrouzi S, Kavousi K, Bagheri M. CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework. ACS Omega 2021; 6:19846-19859. [PMID: 34368571 PMCID: PMC8340416 DOI: 10.1021/acsomega.1c02569] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/13/2021] [Indexed: 05/12/2023]
Abstract
Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).
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Affiliation(s)
- Farid Nasiri
- Peptide
Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry
and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Saman Behrouzi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Kaveh Kavousi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Mojtaba Bagheri
- Peptide
Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry
and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
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Sadeghian Motahar SF, Salami M, Ariaeenejad S, Emam‐Djomeh Z, Sheykh Abdollahzadeh Mamaghani A, Kavousi K, Moghadam M, Hosseini Salekdeh G. Synergistic Effect of Metagenome‐Derived Starch‐Degrading Enzymes on Quality of Functional Bread with Antioxidant Activity. STARCH-STARKE 2021. [DOI: 10.1002/star.202100098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
| | - Maryam Salami
- Department of Food Science and Engineering University College of Agriculture & Natural Resources University of Tehran Karaj Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII) Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Zahra Emam‐Djomeh
- Department of Food Science and Engineering University College of Agriculture & Natural Resources University of Tehran Karaj Iran
| | - Atefeh Sheykh Abdollahzadeh Mamaghani
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII) Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB) Institute of Biochemistry and Biophysics (IBB) University of Tehran Tehran Iran
| | - Maryam Moghadam
- Department of Food Science and Engineering University College of Agriculture & Natural Resources University of Tehran Karaj Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII) Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
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27
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Rezaei Somee M, Dastgheib SMM, Shavandi M, Ghanbari Maman L, Kavousi K, Amoozegar MA, Mehrshad M. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Sci Rep 2021; 11:11316. [PMID: 34059729 PMCID: PMC8166890 DOI: 10.1038/s41598-021-90735-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 05/17/2021] [Indexed: 02/04/2023] Open
Abstract
The Persian Gulf, hosting ca. 48% of the world's oil reserves, has been chronically exposed to natural oil seepage. Oil spill studies show a shift in microbial community composition in response to oil pollution; however, the influence of chronic oil exposure on the microbial community remains unknown. We performed genome-resolved comparative analyses of the water and sediment samples along Persian Gulf's pollution continuum (Strait of Hormuz, Asalouyeh, and Khark Island). Continuous exposure to trace amounts of pollution primed the intrinsic and rare marine oil-degrading microbes such as Oceanospirillales, Flavobacteriales, Alteromonadales, and Rhodobacterales to bloom in response to oil pollution in Asalouyeh and Khark samples. Comparative analysis of the Persian Gulf samples with 106 oil-polluted marine samples reveals that the hydrocarbon type, exposure time, and sediment depth are the main determinants of microbial response to pollution. High aliphatic content of the pollution enriched for Oceanospirillales, Alteromonadales, and Pseudomonadales whereas, Alteromonadales, Cellvibrionales, Flavobacteriales, and Rhodobacterales dominate polyaromatic polluted samples. In chronic exposure and oil spill events, the community composition converges towards higher dominance of oil-degrading constituents while promoting the division of labor for successful bioremediation.
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Affiliation(s)
- Maryam Rezaei Somee
- grid.46072.370000 0004 0612 7950Extremophiles Laboratory, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran
| | - Seyed Mohammad Mehdi Dastgheib
- grid.419140.90000 0001 0690 0331Biotechnology and Microbiology Research Group, Research Institute of Petroleum Industry, Tehran, Iran
| | - Mahmoud Shavandi
- grid.419140.90000 0001 0690 0331Biotechnology and Microbiology Research Group, Research Institute of Petroleum Industry, Tehran, Iran
| | - Leila Ghanbari Maman
- grid.46072.370000 0004 0612 7950Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- grid.46072.370000 0004 0612 7950Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Mohammad Ali Amoozegar
- grid.46072.370000 0004 0612 7950Extremophiles Laboratory, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran
| | - Maliheh Mehrshad
- grid.8993.b0000 0004 1936 9457Department of Ecology and Genetics, Limnology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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Mirsadeghi L, Haji Hosseini R, Banaei-Moghaddam AM, Kavousi K. EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer. BMC Med Genomics 2021; 14:122. [PMID: 33962648 PMCID: PMC8105935 DOI: 10.1186/s12920-021-00974-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/27/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Today, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited. METHODS In this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI. RESULTS This study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some evaluation metrics. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR < 0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA. It includes HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reaches 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case. CONCLUSIONS This research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing. The schematic representation of the proposed model is presented as the Graphic abstract.
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Affiliation(s)
- Leila Mirsadeghi
- Department of Biology, Faculty of Science, Payame Noor University, Tehran, Iran
| | - Reza Haji Hosseini
- Department of Biology, Faculty of Science, Payame Noor University, Tehran, Iran.
| | - Ali Mohammad Banaei-Moghaddam
- Laboratory of Genomics and Epigenomics (LGE), Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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29
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Kazemi-Sefat GE, Keramatipour M, Talebi S, Kavousi K, Sajed R, Kazemi-Sefat NA, Mousavizadeh K. The importance of CDC27 in cancer: molecular pathology and clinical aspects. Cancer Cell Int 2021; 21:160. [PMID: 33750395 PMCID: PMC7941923 DOI: 10.1186/s12935-021-01860-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/01/2021] [Indexed: 12/17/2022] Open
Abstract
Background CDC27 is one of the core components of Anaphase Promoting complex/cyclosome. The main role of this protein is defined at cellular division to control cell cycle transitions. Here we review the molecular aspects that may affect CDC27 regulation from cell cycle and mitosis to cancer pathogenesis and prognosis. Main text It has been suggested that CDC27 may play either like a tumor suppressor gene or oncogene in different neoplasms. Divergent variations in CDC27 DNA sequence and alterations in transcription of CDC27 have been detected in different solid tumors and hematological malignancies. Elevated CDC27 expression level may increase cell proliferation, invasiveness and metastasis in some malignancies. It has been proposed that CDC27 upregulation may increase stemness in cancer stem cells. On the other hand, downregulation of CDC27 may increase the cancer cell survival, decrease radiosensitivity and increase chemoresistancy. In addition, CDC27 downregulation may stimulate efferocytosis and improve tumor microenvironment. Conclusion CDC27 dysregulation, either increased or decreased activity, may aggravate neoplasms. CDC27 may be suggested as a prognostic biomarker in different malignancies. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-01860-9.
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Affiliation(s)
- Golnaz Ensieh Kazemi-Sefat
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Shahid Hemmat Highway, P.O. Box: 14665-354, Tehran, 14496-14535, Iran
| | - Mohammad Keramatipour
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Saeed Talebi
- Department of Medical Genetics, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Roya Sajed
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Shahid Hemmat Highway, P.O. Box: 14665-354, Tehran, 14496-14535, Iran
| | | | - Kazem Mousavizadeh
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Shahid Hemmat Highway, P.O. Box: 14665-354, Tehran, 14496-14535, Iran. .,Cellular and Molecular Research Center, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran.
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Abstract
Perturbation in the normal function of the cell signaling pathways often leads to diseases. One of the factors that help understand the mechanism of diseases is the precise identification and investigation of perturbed signaling pathways. Pathway analysis methods have been developed as their purpose is to identify perturbed signaling pathways in given conditions. Among these methods, some consider the pathways topologies in their analysis, which are referred to as topology-based methods. Most of the topology-based methods used simple graph-based models to incorporate topology in their analysis, which have some limitations. We describe a new Pathway Analysis method using Petri net (PAPet) that uses the Petri net to model the signaling pathways and then propose an algorithm to measure the perturbation on a given pathway under a given condition. Modeling with Petri net has some advantages and could overcome the shortcomings of the simple graph-based models. We illustrate the capabilities of the proposed method using sensitivity, prioritization, mean reciprocal rank, and false-positive rate metrics on 36 real datasets from various diseases. The results of comparing PAPet with five pathway analysis methods FoPA, PADOG, GSEA, CePa and SPIA show that PAPet is the best one that provides a good compromise between all metrics. In addition, the results of applying methods to gene expression profiles in normal and Pancreatic Ductal Adenocarcinoma cancer (PDAC) samples show that the PAPet method achieves the best rank among others in finding the pathways that have been previously reported for PDAC. The PAPet method is available at https://github.com/fmansoori/PAPET.
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Ariaeenejad S, Kavousi K, Mamaghani ASA, Motahar SFS, Nedaei H, Salekdeh GH. In-silico discovery of bifunctional enzymes with enhanced lignocellulose hydrolysis from microbiota big data. Int J Biol Macromol 2021; 177:211-220. [PMID: 33549667 DOI: 10.1016/j.ijbiomac.2021.02.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/01/2021] [Accepted: 02/01/2021] [Indexed: 12/12/2022]
Abstract
Due to the importance of using lignocellulosic biomass, it is always important to find an effective novel enzyme or enzyme cocktail or fusion enzymes. Identification of bifunctional enzymes through a metagenomic approach is an efficient method for converting agricultural residues and a beneficial way to reduce the cost of enzyme cocktail and fusion enzyme production. In this study, a novel stable bifunctional cellulase/xylanase, PersiCelXyn1 was identified from the rumen microbiota by the multi-stage in-silico screening pipeline and computationally assisted methodology. The enzyme exhibited the optimal activity at pH 5 and 50°C. Analyzing the enzyme activity at extreme temperature, pH, long-term storage, and presence of inhibitors and metal ions, confirmed the stability of the bifunctional enzyme under harsh conditions. Hydrolysis of the rice straw by PersiCelXyn1 showed its capability to degrade both cellulose and hemicellulose polymers. Also, the enzyme improved the degradation of various biomass substrates after 168 h of hydrolysis. Our results demonstrated the power of the multi-stage in-silico screening to identify bifunctional enzymes from metagenomic big data for effective bioconversion of lignocellulosic biomass.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Atefeh Sheykh Abdollahzadeh Mamaghani
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Seyedeh Fatemeh Sadeghian Motahar
- Department of Food Science and Engineering, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
| | - Hadi Nedaei
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.
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Norouzi-Beirami MH, Marashi SA, Banaei-Moghaddam AM, Kavousi K. CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data. NAR Genom Bioinform 2021; 3:lqaa107. [PMID: 33575649 PMCID: PMC7787360 DOI: 10.1093/nargab/lqaa107] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 10/29/2020] [Accepted: 12/28/2020] [Indexed: 12/13/2022] Open
Abstract
Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.
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Affiliation(s)
- Mohammad H Norouzi-Beirami
- Laboratory of Complex Biological systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417614335, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, Iran
| | - Ali M Banaei-Moghaddam
- Laboratory of Genomics and Epigenomics (LGE), Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417614335, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417614335, Iran
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Foroozandeh Shahraki M, Farhadyar K, Kavousi K, Azarabad MH, Boroomand A, Ariaeenejad S, Hosseini Salekdeh G. A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study. Biotechnol Bioeng 2020; 118:759-769. [PMID: 33095441 DOI: 10.1002/bit.27608] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 09/23/2020] [Accepted: 10/11/2020] [Indexed: 11/08/2022]
Abstract
Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross-validations as well as holdout tests, and it is freely accessible as a web-service at arimees.com.
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Affiliation(s)
- Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kiana Farhadyar
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mohammad H Azarabad
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Amin Boroomand
- School of Natural Sciences, University of California Merced, Merced, California, USA
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.,Department of Molecular Sciences, Macquarie University, Sydney, New South Wales, Australia
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Behrouzi S, Shafaeipour Sarmoor Z, Hajsadeghi K, Kavousi K. Predicting scientific research trends based on link prediction in keyword networks. J Informetr 2020. [DOI: 10.1016/j.joi.2020.101079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Foroozandeh Shahraki M, Ariaeenejad S, Fallah Atanaki F, Zolfaghari B, Koshiba T, Kavousi K, Salekdeh GH. MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence. Front Microbiol 2020; 11:567863. [PMID: 33193158 PMCID: PMC7645119 DOI: 10.3389/fmicb.2020.567863] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 09/30/2020] [Indexed: 01/03/2023] Open
Abstract
As the availability of high-throughput metagenomic data is increasing, agile and accurate tools are required to analyze and exploit this valuable and plentiful resource. Cellulose-degrading enzymes have various applications, and finding appropriate cellulases for different purposes is becoming increasingly challenging. An in silico screening method for high-throughput data can be of great assistance when combined with the characterization of thermal and pH dependence. By this means, various metagenomic sources with high cellulolytic potentials can be explored. Using a sequence similarity-based annotation and an ensemble of supervised learning algorithms, this study aims to identify and characterize cellulolytic enzymes from a given high-throughput metagenomic data based on optimum temperature and pH. The prediction performance of MCIC (metagenome cellulase identification and characterization) was evaluated through multiple iterations of sixfold cross-validation tests. This tool was also implemented for a comparative analysis of four metagenomic sources to estimate their cellulolytic profile and capabilities. For experimental validation of MCIC’s screening and prediction abilities, two identified enzymes from cattle rumen were subjected to cloning, expression, and characterization. To the best of our knowledge, this is the first time that a sequence-similarity based method is used alongside an ensemble machine learning model to identify and characterize cellulase enzymes from extensive metagenomic data. This study highlights the strength of machine learning techniques to predict enzymatic properties solely based on their sequence. MCIC is freely available as a python package and standalone toolkit for Windows and Linux-based operating systems with several functions to facilitate the screening and thermal and pH dependence prediction of cellulases.
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Affiliation(s)
- Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Behrouz Zolfaghari
- Computer Science and Engineering Department, Indian Institute of Technology Guwahati, Guwahati, India
| | - Takeshi Koshiba
- Department of Mathematics, Faculty of Education and Integrated Arts and Sciences, Waseda University, Tokyo, Japan
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran.,Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
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Kavousi K, Bagheri M, Behrouzi S, Vafadar S, Atanaki FF, Lotfabadi BT, Ariaeenejad S, Shockravi A, Moosavi-Movahedi AA. IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides. J Chem Inf Model 2020; 60:4691-4701. [DOI: 10.1021/acs.jcim.0c00841] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Mojtaba Bagheri
- Peptide Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
| | - Saman Behrouzi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Safar Vafadar
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Bahareh Teimouri Lotfabadi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII) Agricultural Research Education and Extension Organization (AREEO), Karaj 14348-87714, Iran
| | - Abbas Shockravi
- Faculty of Chemistry, Kharazmi University, Tehran 14911-15719, Iran
| | - Ali Akbar Moosavi-Movahedi
- Department of Biophysics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
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Khoshnejat M, Kavousi K, Banaei-Moghaddam AM, Moosavi-Movahedi AA. Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling. BMC Med Genomics 2020; 13:119. [PMID: 32831068 PMCID: PMC7444195 DOI: 10.1186/s12920-020-00767-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 08/12/2020] [Indexed: 11/22/2022] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence worldwide. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake and plays a critical role in T2DM. Here, we sought to provide a better understanding of the abnormalities in this tissue. Methods The muscle gene expression patterns were explored in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification approaches. Moreover, the potential of subtyping T2DM patients was evaluated based on the gene expression patterns. Results A machine-learning technique was applied to identify a set of genes whose expression patterns could discriminate diabetic subjects from healthy ones. A gene set comprising of 26 genes was found that was able to distinguish healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified. Conclusions This study indicates that T2DM is triggered by different cellular/molecular mechanisms, and it can be categorized into different subtypes. Subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of the abnormalities in each group and more effective therapeutic approaches in the future.
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Affiliation(s)
- Maryam Khoshnejat
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.,The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran. .,The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
| | - Ali Mohammad Banaei-Moghaddam
- The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.,Laboratory of Genomics and Epigenomics (LGE), Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Ali Akbar Moosavi-Movahedi
- The UNESCO Chair on Interdisciplinary Research in Diabetes, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.,Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Ariaeenejad S, Nooshi-Nedamani S, Rahban M, Kavousi K, Pirbalooti AG, Mirghaderi S, Mohammadi M, Mirzaei M, Salekdeh GH. A Novel High Glucose-Tolerant β-Glucosidase: Targeted Computational Approach for Metagenomic Screening. Front Bioeng Biotechnol 2020; 8:813. [PMID: 32850705 PMCID: PMC7406677 DOI: 10.3389/fbioe.2020.00813] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/24/2020] [Indexed: 11/24/2022] Open
Abstract
The rate-limiting component of cellulase for efficient degradation of lignocellulosic biomass through the enzymatic route depends on glucosidase’s sensitivity to the end product (glucose). Therefore, there is still a keen interest in finding glucose-tolerant β-glucosidase (BGL) that is active at high glucose concentrations. The main objective of this study was to identify, isolate, and characterize novel highly glucose-tolerant and halotolerant β-glucosidase gene (PersiBGL1) from the mixed genome DNA of sheep rumen metagenome as a suitable environment for efficient cellulase by computationally guided experiments instead of costly functional screening. At first, an in silico screening approach was utilized to find primary candidate enzymes with superior properties. The structure-dependent mechanism of glucose tolerance was investigated for candidate enzymes. Among the computationally selected candidates, PersiBGL1 was cloned, isolated, and structurally characterized, which achieved very high activity in relatively high temperatures and alkaline pH and was successfully used for the hydrolysis of cellobiose. This enzyme exhibits a very high glucose tolerance, with the highest inhibition constant Ki (8.8 M) among BGLs reported so far and retained 75% of its initial activity in the presence of 10 M glucose. Furthermore, a group of multivalent metal, including Mg2+, Mn2+, and Ca2+, as a cofactor, could improve the catalytic efficiency of PersiBGL1. Our results demonstrated the power of computational selected candidates to discover novel glucose tolerance BGL, effective for the bioconversion of lignocellulosic biomass.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Safura Nooshi-Nedamani
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Mahdie Rahban
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Atefeh Ghasemi Pirbalooti
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - SeyedSoheil Mirghaderi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mahsa Mohammadi
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Mehdi Mirzaei
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.,Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
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Ariaeenejad S, Lanjanian H, Motamedi E, Kavousi K, Moosavi-Movahedi AA, Hosseini Salekdeh G. The Stabilizing Mechanism of Immobilized Metagenomic Xylanases on Bio-Based Hydrogels to Improve Utilization Performance: Computational and Functional Perspectives. Bioconjug Chem 2020; 31:2158-2171. [DOI: 10.1021/acs.bioconjchem.0c00361] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, 31359, Iran
| | - Hossein Lanjanian
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, 13145, Iran
| | - Elaheh Motamedi
- Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, 31359, Iran
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, 13145, Iran
| | | | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, 31359, Iran
- Department of Molecular Sciences, Macquarie University, Sydney, 2109, New South Wales, Australia
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Maleknia S, Salehi Z, Rezaei Tabar V, Sharifi-Zarchi A, Kavousi K. An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus. Arthritis Res Ther 2020; 22:156. [PMID: 32576231 PMCID: PMC7310461 DOI: 10.1186/s13075-020-02239-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/05/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. METHODS In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls. RESULTS In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway). CONCLUSIONS Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.
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Affiliation(s)
- Samaneh Maleknia
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Zahra Salehi
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Rezaei Tabar
- Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Ali Sharifi-Zarchi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Fallah Atanaki F, Behrouzi S, Ariaeenejad S, Boroomand A, Kavousi K. BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors. ACS Omega 2020; 5:7290-7297. [PMID: 32280870 PMCID: PMC7144140 DOI: 10.1021/acsomega.9b04119] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/12/2020] [Indexed: 05/26/2023]
Abstract
Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.
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Affiliation(s)
- Fereshteh Fallah Atanaki
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran
| | - Saman Behrouzi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran
| | - Shohreh Ariaeenejad
- Department
of Systems and Synthetic Biology, Agricultural
Biotechnology Research Institute of Iran (ABRII), Agricultural Research,
Education, and Extension Organization (AREEO), Karaj 31535-1897, Iran
| | - Amin Boroomand
- School
of Natural Sciences, University of California
Merced, Merced 95343-5001, California, United States of America
| | - Kaveh Kavousi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran
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Ghanbari Maman L, Palizban F, Fallah Atanaki F, Elmi Ghiasi N, Ariaeenejad S, Ghaffari MR, Hosseini Salekdeh G, Kavousi K. Co-abundance analysis reveals hidden players associated with high methane yield phenotype in sheep rumen microbiome. Sci Rep 2020; 10:4995. [PMID: 32193482 PMCID: PMC7081230 DOI: 10.1038/s41598-020-61942-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 03/05/2020] [Indexed: 12/28/2022] Open
Abstract
Rumen microbial environment hosts a variety of microorganisms that interact with each other to carry out the feed digestion and generation of several by-products especially methane, which plays an essential role in global warming as a greenhouse gas. However, due to its multi-factorial nature, the exact cause of methane production in the rumen has not yet been fully determined. The current study is an attempt to use system modeling to analyze the relationship between interacting components of rumen microbiome and its role in methane production. Metagenomic data of sheep rumen, with equal numbers of high methane yield (HMY) and low methane yield (LMY) samples, were used. As a well-known approach for the systematic comparative study of complex traits, the co-abundance networks were constructed in both operational taxonomic unit (OTU) and gene levels. A gene-catalog of 1,444 different rumen microbial strains was developed as a reference to measure gene abundances. The results from both types of co-abundance networks showed that methanogens, which are the main ruminal source for methanogenesis, need other microbial species to accomplish the task of methane production through producing the main precursor molecules like H2 and acetate for methanogenesis pathway as their byproducts. KEGG Orthology(KO) analysis of the current study shows that the metabolism and growth rate of methanogens will be increased due to the higher rate of the metabolism and carbohydrate/fiber digestion pathways in the hidden elements. This finding proposes that any ruminant methane yield alteration strategy should consider complex interactions of rumen microbiome components as one tightly integrated unit rather than several separate parts.
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Affiliation(s)
- Leila Ghanbari Maman
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Fahimeh Palizban
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Naser Elmi Ghiasi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Mohammad Reza Ghaffari
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Norouzi-Beirami MH, Marashi SA, Banaei-Moghaddam AM, Kavousi K. Beyond Taxonomic Analysis of Microbiomes: A Functional Approach for Revisiting Microbiome Changes in Colorectal Cancer. Front Microbiol 2020; 10:3117. [PMID: 32038558 PMCID: PMC6990412 DOI: 10.3389/fmicb.2019.03117] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 12/24/2019] [Indexed: 01/16/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most prevalent cancers in the world, especially in developed countries. In different studies, the association between CRC and dysbiosis of gut microbiome has been reported. However, most of these works focus on the taxonomic variation of the microbiome, which presents little, if any, functional insight about the reason behind and/or consequences of microbiome dysbiosis. In this study, we used a previously reported metagenome dataset which is obtained by sequencing 156 microbiome samples of healthy individuals as the control group (Co), as well as microbiome samples of patients with advanced colorectal adenoma (Ad) and colorectal carcinoma (Ca). Features of the microbiome samples have been analyzed at the level of species, as well as four functional levels, i.e., gene, KEGG orthology (KO) group, Enzyme Commission (EC) number, and reaction. It was shown that, at each of these levels, certain features exist which show significant changing trends during cancer progression. In the next step, a list of these features were extracted, which were shown to be able to predict the category of Co, Ad, and Ca samples with an accuracy of >85%. When only one group of features (species, gene, KO group, EC number, reaction) was used, KO-related features were found to be the most successful features for classifying the three categories of samples. Notably, species-related features showed the least success in sample classification. Furthermore, by applying an independent test set, we showed that these performance trends are not limited to our original dataset. We determined the most important classification features at each of the four functional levels. We propose that these features can be considered as biomarkers of CRC progression. Finally, we show that the intra-diversity of each sample at the levels of bacterial species and genes is much more than those of the KO groups, EC numbers, and reactions of that sample. Therefore, we conclude that the microbiome diversity at the species level, or gene level, is not necessarily associated with the diversity at the functional level, which again indicates the importance of KO-, EC-, and reaction-based features in metagenome analysis. The source code of proposed method is freely available from https://www.bioinformatics.org/mamed.
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Affiliation(s)
- Mohammad Hossein Norouzi-Beirami
- Laboratory of Complex Biological Systems and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Ali Mohammad Banaei-Moghaddam
- Laboratory of Genomics and Epigenomics, Department of Biochemistry, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Arabfard M, Ohadi M, Rezaei Tabar V, Delbari A, Kavousi K. Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach. BMC Genomics 2019; 20:832. [PMID: 31706268 PMCID: PMC6842548 DOI: 10.1186/s12864-019-6140-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 09/25/2019] [Indexed: 12/11/2022] Open
Abstract
Background Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). Results We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). Conclusion We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.
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Affiliation(s)
- Masoud Arabfard
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran.,Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mina Ohadi
- Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
| | - Vahid Rezaei Tabar
- Department of Statistics, Faculty of Mathematical Sciences and Computer, Allameh Tabataba'i University, Tehran, Iran
| | - Ahmad Delbari
- Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Ariaeenejad S, Maleki M, Hosseini E, Kavousi K, Moosavi-Movahedi AA, Salekdeh GH. Mining of camel rumen metagenome to identify novel alkali-thermostable xylanase capable of enhancing the recalcitrant lignocellulosic biomass conversion. Bioresour Technol 2019; 281:343-350. [PMID: 30831513 DOI: 10.1016/j.biortech.2019.02.059] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/09/2019] [Accepted: 02/11/2019] [Indexed: 06/09/2023]
Abstract
The aim of this study was to isolate and characterize novel alkali-thermostable xylanase genes from the mixed genome DNA of camel rumen metagenome. In this study, a five-stage computational screening procedure was utilized to find the primary candidate enzyme with superior properties from the camel rumen metagenome. This enzyme was subjected to cloning, purification, and structural and functional characterization. It showed high thermal stability, high activity in a broad range of pH (6-11) and temperature (30-90 °C) and effectivity in recalcitrant lignocellulosic biomass degradation. Our results demonstrated the power of in silico analysis to discover novel alkali-thermostable xylanases, effective for the bioconversion of lignocellulosic biomass.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Morteza Maleki
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Elnaz Hosseini
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | | | - Ghasem Hosseini Salekdeh
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran.
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Abstract
:
MicroRNAs have been considered as an endogenous
post-transcriptional gene silencing mechanism for transcription regulation.
Additionally, reports are indicative of their involvement in plant viral
defence. It seems that horizontal miRNA transfer from pathogenic viral and
viroid genomes into plants has evolved into counter-defence mechanism(s)
against their invasions. miRNAs of a green alga and 16 other higher plant
species, with/without endogenous function, were searched for their
energetically favorable target sites in the complete genomes of
plant-specific invading viruses and viroids. Interestingly, 6524 and 250
potent target sites were found in 759 viral and 16 viroid genomes,
respectively. Also, in 5589 viral encoding genes/sequences, 7583 potent
target sites from 638 plant invasing viruses were found to be targeted by
1019 miRNAs (belonging to 636 miRNA families). Some miRNA families including
miR171, miR156 and miR159 had wide target range in viral genomes;
demonstrating possible plant-virus coevolution and at the same time a degree
of conservation within different viruses. Such coevolution proposes the
existence of immune response mechanism against these sub-genomic organisms,
and further suggests adaptive plant-pathogen interactions, something that is
known as host specificity. Herein, it was also speculated that some miRNA
families such as; miR915, miR899 and miR895 with unknown or non-validated
targets in plant genomes are being involved in suppression of invading
viruses/viroids.
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Affiliation(s)
- Behzad Hajieghrari
- Department of Agricultural Biotechnology, College of Agriculture, Jahrom University, Jahrom, P.O.Box 74135-111, Iran
| | - Naser Farrokhi
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University G.C., Evin, Tehran, P.O. Box 19839-4716, Iran
| | - Bahram Goliaei
- Departments of Biophysics and Bioinformatics Laboratories, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, P.O.Box 13145-1365, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, P.O.Box 13145-1365, Iran
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47
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Ariaeenejad S, Hosseini E, Maleki M, Kavousi K, Moosavi-Movahedi AA, Salekdeh GH. Identification and characterization of a novel thermostable xylanase from camel rumen metagenome. Int J Biol Macromol 2019; 126:1295-1302. [DOI: 10.1016/j.ijbiomac.2018.12.041] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/19/2018] [Accepted: 12/02/2018] [Indexed: 11/25/2022]
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Mansoori F, Rahgozar M, Kavousi K. FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods. BMC Bioinformatics 2019; 20:92. [PMID: 30808299 PMCID: PMC6390332 DOI: 10.1186/s12859-019-2635-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 01/17/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways. RESULTS In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes. CONCLUSIONS The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of "Renin-angiotensin system" pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.
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Affiliation(s)
- Fatemeh Mansoori
- Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Maseud Rahgozar
- Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
| | - Kaveh Kavousi
- Complex Biological Systems and Bioinformatics Lab (CBB), Bioinformatics department, University of Tehran, Tehran, Iran.
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Rahiminejad M, Ledari MT, Mirzaei M, Ghorbanzadeh Z, Kavousi K, Ghaffari MR, Haynes PA, Komatsu S, Salekdeh GH. The Quest for Missing Proteins in Rice. Mol Plant 2019; 12:4-6. [PMID: 30543994 DOI: 10.1016/j.molp.2018.11.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 11/19/2018] [Accepted: 11/20/2018] [Indexed: 06/09/2023]
Affiliation(s)
- Mohsen Rahiminejad
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran; Department of Biotechnology, University of Tehran, Tehran, Iran
| | | | - Mehdi Mirzaei
- Department of Molecular Sciences, Macquarie University, Sydney, Australia; Australian Proteome Analysis Facility, Macquarie University, Sydney, Australia
| | - Zahra Ghorbanzadeh
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Kaveh Kavousi
- Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mohammad Reza Ghaffari
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran
| | - Paul A Haynes
- Department of Molecular Sciences, Macquarie University, Sydney, Australia
| | - Setsuko Komatsu
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Ghasem Hosseini Salekdeh
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney, Australia.
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Hajieghrari B, Farrokhi N, Goliaei B, Kavousi K. In Silico Identification of Conserved MiRNAs from Physcomitrella patens ESTs and their Target Characterization. Curr Bioinform 2018. [DOI: 10.2174/1574893612666170530081523] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background:
MicroRNAs (miRNAs) are groups of small non-protein-coding endogenous
single stranded RNAs with approximately 18-24 nucleotides in length. High evolutionary sequence conservation
of miRNAs among plant species and availability of powerful computational tools allow identification
of new orthologs and paralogs.
Methods:
New conserved miRNAs in P. patens were found by EST-based homology search approaches.
All candidates were screened according to a series of miRNA filtering criteria. Unigene, DFCI Gene
Index (PpspGI) databases and psRNATarget algorithm were applied to identify target transcripts using
P. patens putative conserved miRNA sequences.
Results:
Nineteen conserved P. patens miRNAs were identified. The sequences were homologous to
known reference plant mature miRNA from 10 miRNA families. They could be folded into the typical
miRNA secondary structures. RepeatMasker algorithm demonstrated that ppt-miR2919e and pptmiR1533
had simple sequence repeats in their sequences. Target sites (49 genes) were identified for 7
out of 19 miRNAs. GO and KEGG analysis of targets indicated the involvement of some in important
multiple biological and metabolic processes.
Conclusion:
The majority of the registered miRNAs in databases were predicted by computational approaches
while many more have remained unknown. Due to the conserved nature of miRNAs in plant
species from closely to distantly related, homology search-based approaches between plants species
could lead to the identification of novel miRNAs in other plant species providing baseline information
for further search about the biological functions and evolution of miRNAs.
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Affiliation(s)
- Behzad Hajieghrari
- Department of Plant Sciences & Biotechnology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University G.C., Evin, Tehran, P.O. Box 19839-4716, Iran
| | - Naser Farrokhi
- Department of Plant Sciences & Biotechnology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University G.C., Evin, Tehran, P.O. Box 19839-4716, Iran
| | - Bahram Goliaei
- Departments of Biophysics and Bioinformatics laboratories, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, P.O.Box 13145-1365, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, P.O.Box 13145-1365, Iran
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