فهرست مطالب

Iranian Journal of Biotechnology
Volume:22 Issue: 4, Autumn 2024
- تاریخ انتشار: 1403/07/10
- تعداد عناوین: 9
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Pages 1-13
Breast cancer (BC) is still a major global health concern, and a key factor in its pathophysiology is epigenetic abnormalities, specifically DNA methylation and histone modifications. This review offers a thorough examination of current research on the effects of these epigenetic changes in BC, emphasizing significant discoveries in the fields of prognosis, diagnostics, and treatment strategies. In particular, the advancement of breast cancer and patient survival have been connected to promoter methylation of genes including BRCA1, DAPK1, and RASSF1A. Furthermore, there is a correlation between tumor size and grade and the methylation state of APAF1, GSTP1, and ERThere is no text provided. Histone modifications, such as acetylation and methylation, are essential for controlling gene expression in breast cancer. Changes in these modifications are associated with the advancement of tumors and resistance to therapy.The analysis highlights the potential of methylation-targeting medicines to improve the effectiveness of traditional chemotherapy and reveals particular methylation indicators that differentiate malignant tissues from normal ones. Further clinical validation is necessary to confirm the efficacy of DNMT and HMT inhibitors in mitigating hormone resistance and epigenetic modifications in BC, despite encouraging outcomes. Large-scale trials are necessary to validate these results, and investigating combination therapy, including those targeting histone modifications, to enhance patient outcomes is one of the main recommendations.
Keywords: Biomarker, Breast Cancer, DNA Methylation, Treatment -
Pages 14-26
Antibiotic resistance has become a major public health concern worldwide. Treatment of humans and animals is becoming increasingly challenging due to antibiotic resistance. Antibiotic-resistant bacteria can be transmitted from animals to humans by several routes, including direct contact, contaminated food or water, or environmental exposure. Various factors contribute to the rising problem, such as the widespread and indiscriminate exploitation of antimicrobials in both human and animal healthcare, over-prescription, misuse of antibiotics, the role of agriculture in spreading antibiotic resistance, and poor animal husbandry practices. According to the preliminary findings, recombinant antimicrobial peptides are an interesting novel area of biotechnology and medical innovation that might be employed as a secure and effective substitute for antibiotics. In this review study, we briefly examine the factors contributing to the rise of antibiotic resistance. We then introduce and discuss recombinant antimicrobial peptides as a promising strategy to address this growing problem.
Keywords: Antimicrobial Peptides, Antibiotic Resistance, Recombinant, Veterinary Medicine -
Pages 27-37BackgroundPotato cultivation ranks among the world’s major crops, yet it is vulnerable to numerous diseases. The integration of glyphosate-resistance genes into potato plants allows for the direct application of glyphosate, simplifying weed and disease management. This innovation reduces the need for complex control methods. Additionally, various biotechnological strategies have been adopted to tackle disease challenges in potato farming.ObjectiveAn efficient protocol was developed via the Agrobacterium-mediated transformation method with the plasmid, p485, harboring the aroA gene from the bacterial species Dickeya dadantii, to improve resistance to potato bacterial soft rot disease. The study aimed to investigate the relationship between glyphosate application and the enhancement of potatoes’ resistance to two bacterial pathogens affecting the plants.Materials and methodsAn optimal concentration of 1.8 mg.L-1 of glyphosate was applied to transgenic potato varieties. The leaves of the Odyssey cultivar demonstrated resistance to two pathogenic strains, Pectobacterium atrosepticum 21A and D. dadantii ENA49. Polymerase chain reaction (PCR) and reverse transcription-PCR (RT-PCR) validation demonstrated the successful integration and heterologous expression of the aroA gene in the potato genome. Additionally, the transcriptional analysis revealed the expression of pathogenesis-related genes and genes associated with the potato defence response.ResultsThe study revealed a significant increase in the expression of pathogenesis-related genes (PR-2, PR-3, and PR-5) and defence response genes (HSR-203j and HIN1 in transgenic potato leaves after glyphosate treatment and subsequent exposure to pathogenic bacterial infection, with a particular emphasis on the upregulation of HSR-203j. A comparative analysis assessed the average expression levels of these genes in both experimental and control samples. In contrast, minimal changes in gene expression were observed in plants infected with bacteria but not treated with glyphosate.ConclusionThe study suggests that glyphosate treatment in potatoes can enhance systemic acquired resistance to bacterial pathogens by upregulating pathogenesis-related and defence response genes. This approach shows potential for addressing bacterial diseases in potatoes, including soft bacterial rot.Keywords: Agrobacterium-Mediated Transformation Method, Aroa Gene, Glyphosate, Odyssey Cultivar, Systemic Acquired Resistance
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Pages 38-53BackgroundBiodegradable polyhydroxyalkanoates (PHAs) hold promises for various applications in industries ranging from packaging to biomedical engineering, highlighting the importance of this pioneering research in sustainable materials synthesis.ObjectivesThe objective of this investigation was to present the successful production of polyhydroxyalkanoate (PHA) copolymer P(3HB-co-3HHx) from glucose utilizing a newly mutated strain of Cupriavidus necator. This mutant strain carries the pBPP-ccrMeJAc-emd plasmid which harbors a short-chain-length-specific PhaJ enzyme. The primary aim is to demonstrate the enhanced production efficiency and specificity of P(3HB-co-3HHx) through genetic manipulation and enzyme engineering, thereby advancing the feasibility and sustainability of PHA-based bioplastic production.Materials and MethodsTo design the inputs conditions,a central composite factorial design (CCFD) based on a one-variable-at-a-time (OVAT) experiment was conducted. This experiment aimed to identify key chemical factors and their operational ranges affecting PHBHHx production by the mutant strain. Later, batch and repeated fed-batch (RFB) culture were run in a stirred tank bioreactor (STBR) with a working volume of 2 L which was inoculated by 200 ml (10% v/v) of freshly grown seed culture (18 h). This methodology ensured controlled exploration of individual variables, facilitating the selection of optimal conditions for PHBHHx production. Total glucose concentrations during fermentation were assessed through the phenol-sulfuric acid assay.ResultsThe study demonstrates the effectiveness of the designed model in predicting PHBHHx production during fermentation runs with predicted values closely aligning with experimental results. This underscores the model satisfactory fitness with the experimental design. Additionally, a surprising enhancement was observed in the fermentation process with repeated fed-batch (RFB) leading to a substantial increase in cell dry weight (CDW), PHBHHX concentration, and 3HHx fraction, approximately 7 times, 7 times and 4.5 times, respectively. Confirmation of copolymer production was further validated through analytical techniques including FTIR spectroscopy, NMR, and TEM analysis. These findings collectively highlight the promising potential of RFB as a method to significantly improve PHBHHx production covering the way for further advancements in biopolymer manufacturing processes.ConclusionsOur study reveals the potential of newly engineered C. necator NSDG˗GGΔB1/pBPP-ccrMeJAc-emd mutant strain for efficient PHBHHx copolymer production. Process parameters such as glucose and urea concentration, and agitation rate significantly influenced PHBHHx yield. This research stands out by utilizing a novel strain for PHBHHx synthesis. Characterization confirmed high-quality polymer production. Our findings offer a sustainable approach for converting inexpensive carbon sources into valuable PHBHHx though further optimization for scale-up is warranted.Keywords: Biodegradable Plastic, Cupriavidus Necator NSDG-GGΔB1, Pbpp-Ccrmejac-Emd, Glucose, Phbhhx
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Pages 54-63BackgroundGene manipulation has a wide array of applications in microorganisms. We can construct multifunctional bacterial strains by gene manipulation and gene editing in order to produce several industrial biomaterials including enzymes at the same time.ObjectiveAccording to the importance of cellulase in various industries, including food industry, the purpose of this study was aimed to produce cellulase in an indigenous Bacillus cereus EG296 strain through gene manipulation.Materials and MethodsThe Bacillus subtilis 168 cellulase gene, located between the regulatory upstream and downstream regions of Bacillus cereus protease gene (aprE), was amplified by SOEing PCR and transformed into the Bacillus cereus EG296 by natural transformation. After selection of the strains with cellulase activity, the scoC gene (Negative transcriptional regulator of aprE gene) was also deleted from the genome of the transformant by homologous recombination in order to increase the cellulase and protease activities simultaneously.ResultsThe Bacillus cereus cells were acquired the cellulase gene into their genome with cellulase activity of about 0.61 u.mL-1. By scoC gene deletion, the protease activity reached to 363.14 u.mL-1 from 230 u.mL-1. Meanwhile, the cellulaseactivity under the control of the protease promoter was also increased to 0.78 u.mL-1 from 0.61 u.mL-1. The cellulase and protease expressed in B. cereus have an instability index of 26.16 and 20.18 respectively, which is much lower than threshold of 40. Accordingly, it can be concluded that both enzymes are considered to be stable.ConclusionAs a result, we obtained a genetically engineered strain that had the ability to produce and secrete two important industrial extracellular enzymes (cellulase and protease), with easy downstream purification processes..Keywords: Bacillus Cereus, Cellulase, Homologous Recombination, Heterologous Expression, Metabolic Engineering, Protease
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Pages 64-77BackgroundClear cell renal cell carcinoma (ccRCC, KIRC) is the most prevalent subtype of RCC, and even with different available therapies, the average progression-free survival is worse. Therefore, the identification of new molecular targets could be helpful for its therapeutic purposes.Materials and MethodsWe used the Cancer Genome Atlas to perform bioinformatic analyses for genes with possible tumor suppressor roles in KIRC.ObjectiveThis research aims to identify new prognostic biomarkers and potential therapeutic targets for this type of cancer.ResultsWe identified 14 down-regulated genes in KIRC that had not previously been studied or poorly studied, with the majority of them impacted by increased promoter methylation. Eight genes showed shorter overall survival and worse prognosis, indicating their function as tumor suppressors, and six genes revealed good prognosis. From the 8 genes, C7ORF41 and CTXN3 revealed only downregulation in most cancers, proposing them as highly potential tumor suppressors. Among these 8 genes, the function of CTXN3 in cancers is unknown. Moreover, we identified the CWH43 gene as the major signature of KIRC. In addition, we found different genes as signatures of KIRC tumor stages and grades.ConclusionsOur results may shed light on identifying KIRC pathogenesis and developing effective therapeutic targets for renal cancers, mainly KIRCKeywords: Tumor Suppressor, Renal Clear Cell Carcinoma, The Cancer Genome Atlas (TCGA), Methylation, Prognostic Biomarkers
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Pages 78-91BackgroundGlioblastoma (GBM) is the most aggressive form of brain cancer, with poor prognosis despite treatments like temozolomide (TMZ). Resistance to TMZ is a significant clinical challenge, and understanding the genes involved is crucial for developing new therapies and prognostic markers. This study aims to identify key genes associated with TMZ resistance in GBM, which could serve as valuable biomarkers for predicting patient outcomes and potential targets for treatment.ObjectivesThis study aimed to identify genes involved in TMZ resistance in GBM and to assess the value of these genes in GBM treatment and prognosis evaluation.Materials and MethodsBioinformatics analysis of Gene Expression Omnibus (GEO) datasets (GSE113510 and GSE199689) and The Chinese Glioblastoma Genome Atlas (CGGA) database was performed to identify differentially expressed genes (DEGs) between GBM cell lines with and without TMZ resistance. Subsequently, the key modules associated with GBM patient prognosis were identified by weighted gene coexpression network analysis (WGCNA). Furthermore, hub genes related to TMZ resistance were accurately screened and confirmed using three machine learning algorithms. In addition, immune cell infiltration analysis, TF-miRNA coregulatory network analysis, drug sensitivity prediction, and gene set enrichment analysis (GSEA) were also performed for temozolomide resistance-specific genes. Finally, the expression levels of key genes were validated in our constructed TMZ-resistant cell lines by real-time quantitative polymerase chain reaction (RT–qPCR) and Western blotting (WB).ResultsIntegrated analysis of the GEO and CGGA datasets revealed 769 differentially expressed genes (DEGs), comprising 350 downregulated and 419 upregulated genes, between GBM patients and normal controls. Among these DEGs, three key genes, namely, PITX1, TNFRSF11B, and IGFBP2, exhibited significant differences in expression between groups and were prioritized via machine learning algorithms. The expression levels of these genes were found to be closely related to adverse clinical features and immune cell infiltration levels in GBM patients. These genes were also found to participate in several biological pathways and processes. RT‒qPCR and WB confirmed the differential expression of these genes in vitro, indicating that they play vital roles in GBM patients with TMZ resistance.ConclusionsPITX1, TNFRSF11B, and IGFBP2 are key genes associated with the prognosis of GBM patients with TMZ resistance. The differential expression of these genes correlates with adverse outcomes in GBM patients, suggesting that they are valuable biomarkers for predicting patient prognosis and that they could serve as diagnostic biomarkers or treatment targets.Keywords: Biomarkers, GEO Database, Glioblastoma, Machine Learning Algorithm, Temozolomide Resistance
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Pages 92-105BackgroundHuman leukocyte antigens (HLAs) play a pivotal role in orchestrating the host’s immune response, offering a promising avenue with reduced adverse effects compared to conventional treatments. Cancer immunotherapies use HLA class I molecules for T cells to recognize tumor antigens, emphasizing the importance of identifying peptides that bind effectively to HLAs. Computer modeling of HLA-peptide binding speeds up the search for immunogenic epitopes, which enhances the prospect of personalized medicine and targeted therapies. The Immune Epitope Database (IEDB) is a vital repository, housing curated immune epitope data and prediction tools for HLA-peptide binding. It can be challenging for immunologists to choose the best tool from the IEDB for predicting HLA-peptide binding. This has led to the creation of consensus-based methods that combine the results of several predictors. One of the major challenges in these methods is how to effectively integrate the results from multiple predictors.ObjectivesPrevious consensus-based methods integrate at most three tools by relying on simple strategies, such as selecting prediction methods based on their proximity to HLA in training data. In this study, we introduce HLAPepBinder, a novel consensus approach using ensemble machine learning methods to predict HLA-peptide binding, addressing the challenges biologists face in model selection.Materials and MethodsThe key contribution is the development of an automatic pipeline named HLAPepBinder that integrates the predictions of multiple models using a random forest approach. Unlike previous approaches, HLAPepBinder seamlessly integrates results from all nine predictors, providing a comprehensive and accurate predictive framework. By combining the strengths of these models, HLAPepBinder eliminates the need for manual model selection, providing a streamlined and reliable solution for biologists.ResultsHLAPepBinder offers a practical and high-performing alternative for HLA-peptide binding predictions, outperforming both traditional methods and complex deep learning models. Compared to the recently introduced transformer-based model, TranspHLA, which requires substantial computational resources, HLAPepBinder demonstrates superior performance in both prediction accuracy and resource efficiency. Notably, it operates effectively in limited computational environments, making it accessible to researchers with minimal resources. The codes are available online at https://github.com/CBRC-lab/HLAPepBinder.ConclusionOur study introduces a novel ensemble-learning model designed to enhance the accuracy and efficiency of HLA-peptide binding predictions. Due to the lack of reliable negative data and the typical assumption of unknown interactions being negative, we focus on analyzing the unknown HLA-peptide bindings in the test set that our model predicts with 100% certainty as positive bindings. Using HLAPepBinder, we identify 26 HLA-peptide pairs with absolute prediction confidence. These predictions are validated through a multi-step pipeline involving literature review, BLAST sequence similarity analysis, and molecular docking studies. This comprehensive validation process highlights HLAPepBinder’s ability to make accurate and reliable predictions, contributing significantly to advancements in immunotherapy and vaccine development.Keywords: HLA Class I, HLA-Peptide Binding, Immunotherapy, Random Forest, T Cell Epitope
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Pages 106-111Background
Genome walking has contributed to life science-related areas. Herein, we detailed a new genome walking method, nominated as arbitrarily suffixed sequence-specific primer PCR (ASP-PCR).
ObjectivesThis study aimed to construct an efficient random PCR-based genome walking method.
Materials and MethodsThe key for this method is the use of a hybrid primer (HP) in primary ASP-PCR. This HP is fabricated by suffixing an arbitrary sequence to outmost sequence-specific primer. The relaxed cycle in primary ASP-PCR facilitates the partial annealing of HP to genome, creating many single-stranded DNAs. In the next stringent cycles, target single-strand is exponentially amplified, because it also has a site complementary to the sequence-specific part of HP; while nontarget cannot be further processed due to lacking such a site. Nested secondary/tertiary ASP-PCR further selectively enriches target DNA.
ResultsThe practicability of ASP-PCR was confirmed by obtaining unknown DNAs adjacent to Oryza sativa hygromycin gene and Levilactobacillus brevis CD0817 L-glutamic acid decarboxylase gene. The results showed that each secondary or tertiary ASP-PCR exhibited 1 – 2 clear target amplicon(s) with size from 1.5 to 3.5 kb, and a weak background.
ConclusionsThe ASP-PCR is a promising genome walking scheme, and may have a potential use in life science-related areas..
Keywords: Arbitrary Primer, Genome Walking, Hybrid Primer, Nested PCR, Random Annealing, Sequence-Specific Primer