The use of anticancer peptides (ACPs) as an alternative/complementary strategy to conventional ch... more The use of anticancer peptides (ACPs) as an alternative/complementary strategy to conventional chemotherapy treatments has been shown to decrease drug resistance and/or severe side effects. However, the efficacy of the positively‐charged ACP is inhibited by elevated levels of negatively‐charged cell‐surface components which trap the peptides and prevent their contact with the cell membrane. Consequently, this decreases ACP‐mediated membrane pore formation and cell lysis. Negatively‐charged heparan sulphate (HS) and chondroitin sulphate (CS) have been shown to inhibit the cytotoxic effect of ACPs.In this study, we propose a strategy to promote the broad utilization of ACPs. In this context, we developed a drug repositioning pipeline to analyse transcriptomics data generated for four different cancer cell lines (A549, HEPG2, HT29, and MCF7) treated with hundreds of drugs in the LINCS L1000 project. Based on previous studies identifying genes modulating levels of the glycosaminoglycans (GAGs) HS and CS at the cell surface, our analysis aimed at identifying drugs inhibiting genes correlated with high HS and CS levels. As a result, we identified six chemicals as likely repositionable drugs with the potential to enhance the performance of ACPs. The codes in R and Python programming languages are publicly available in https://github.com/ElyasMo/ACPs_HS_HSPGs_CS.As a conclusion, these six drugs are highlighted as excellent targets for synergistic studies with ACPs aimed at lowering the costs associated with ACP‐treatment.
Abstract A large number of genome-wide association studies (GWAS) in livestock, especially in dai... more Abstract A large number of genome-wide association studies (GWAS) in livestock, especially in dairy cows, provide favorable conditions to integrate multiple independent studies. Methods such as meta-analysis provide the identification of effective QTLs with higher precision and power. A meta-analysis for milk production traits between different countries was conducted using the GWAS summary statistics (i.e., P-value, sample size, allele effects, and etc.) in Holstein cows. In the present study, METAL software was used for the weighted Z-score model. Gene network analysis was used as a complementary method to improve our knowledge of the genome structure of milk production traits and was implemented through the STRING plug-in in Cytoscape software. The Cytoscape ClueGO plug-in was also used for GO enrichment in order to identify biological process, cellular component, and molecular function associated with genomic regions. The aim of this study was to improve the power of QTLs detection and identify the biological mechanisms associated with milk production traits. Data were obtained from 26 published studies from 2010 to 2019. A total of 2,072 SNPs were identified for milk production traits, of which 1,583 SNPs were significant ( P 0.05 ). Meta-analysis identified 9 QTLs for milk yield, 36 QTLs for fat percentage, and 10 QTLs for protein percentage. Some QTLs were confirmed on BTA14, e.g., BTA14:1801116 close to the DGAT1 gene (milk yield, P = 2.6 × 10 − 131 ; fat percentage, P = 4.8 × 10 − 347 ; protein percentage, P = 7.6 × 10 − 24 ) and BTA14:1651311 close to the PPP1R16A gene (milk yield, P = 2.3 × 10 − 162 ; fat percentage, P = 3.5 × 10 − 153 ). We identified pleiotropic effects of lead SNPs for milk production traits, e.g., one SNP (rs109421300) at BTA14 had pleiotropic effects on milk yield, fat percentage, and protein percentage traits. The most important SNPs for studied traits across countries implicated to network scoring and visualization were including: rs109421300 (DGAT1 gene) for milk yield, fat percentage, and protein percentage; rs109146371 (PPP1R16A gene) for milk yield and fat percentage; rs109968515 (CYHR1 gene) for milk yield and fat percentage; rs134432442 (CPSF1 gene) for fat percentage; rs111018678 (TRAPPC9 gene) for protein percentage. Significant pathways involved in milk production traits through GO term enrichment analysis for biological process, cellular component, and molecular function included: regulation of cation channel activity ( P = 1.6 × 10 − 2 ), ion channel complex ( P = 1.4 × 10 − 2 ), and phosphoric diester hydrolase activity ( P = 1.1 × 10 − 3 ) for milk yield; negative regulation of organ growth ( P = 8.2 × 10 − 3 ), transmembrane transporter complex ( P = 1.6 × 10 − 3 ), and potassium ion transmembrane transporter activity ( P = 8.8 × 10 − 3 ) for fat percentage; mRNA polyadenylation ( P = 1.2 × 10 − 2 ), mRNA cleavage factor complex ( P = 9.8 × 10 − 4 ), and phosphoric diester hydrolase activity ( P = 5.3 × 10 − 3 ) for protein percentage, respectively. Thus, the combination of GWAS summary statistics through a powerful methodology such as meta-analysis will assist us to accurately identify QTLs, potential candidate genes, and biological mechanisms. This kind of studies will help us to have better understanding of QTL regions and genome structure for milk production traits and improve genomic evaluations in Holstein cows. To the best of our knowledge, this is the first meta-analysis of GWAS and GO enrichment across countries for milk production traits in Holstein cows.
Identification of selection signatures may provide a better understanding of domestication proces... more Identification of selection signatures may provide a better understanding of domestication process and candidate genes contributing to this process. In this study, two populations of domestic and wild goats from Iran were analyzed to identify selection signatures. RSB, iHS, and XP‐EHH statistics were used in order to identify robust selection signatures in the goat genome. Genotype data of domestic and wild goats from the NextGen project was used. The data was related to 18 Capra aegagrus (wild goat) and 20 Capra hircus (domestic goat) from Iran. The iHS method indicated 675 and 441 selection signatures in C. aegagrus and C. hircus, respectively. RSB and XP‐EHH methods showed about 370 and 447 selection signatures in C. aegagrus and C. hircus, respectively. These selection signatures were mainly associated with milk production, fleece trait, mammary epithelial cells, reproduction, and immune system.
Objectives: Early, specific, and sensitive detection methods of COVID-19 are essential for force ... more Objectives: Early, specific, and sensitive detection methods of COVID-19 are essential for force stopping its worldwide infection. Although CT images of the lung and/or viral RNA extraction followed by real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) are widely used; they have some limitations. Here, we developed a highly sensitive magnetic bead-based viral RNA extraction assay followed by rRT-PCR. Materials and methods: Case group included oropharyngeal/nasopharyngeal and blood samples from 30 patients diagnosed positive by PCR test for COVID-19 and control group included 30 same samples from COVID-19 negative PCR test individuals. RNA was extracted, using viral RNA extraction kit as well as using our hand-made capture bead-based technique. A one-step cDNA synthesis and Real Time PCR was conducted. A two-step comparison of the different viral RNA extraction methods for oropharyngeal/nasopharyngeal and blood samples was performed. Student t-test was applied with a P<0.05 considered statistically significant. Results: In the case group, all 30 mucosal samples extracted either with viral RNA extraction kit or with beads-based assay were COVID-19 positive although in the latter category, Cqs were much lower. Although 43% of plasma samples extracted by bead-based method were found to be positive but no plasma samples extracted with column-based kit were detected positive by Real Time PCR. Conclusion: Bead-based RNA extraction method can reduce RNA loss by its single-tube performance and enhance the test sensitivity. It is also more sensitive to lower viral loads as shown in the detection of blood samples and the lower Cqs of mucosal samples.
International Journal of Peptide Research and Therapeutics, Feb 6, 2020
Computational prediction of signal peptides is one of the most important steps in genetic enginee... more Computational prediction of signal peptides is one of the most important steps in genetic engineering experiments. The periplasmic expression cause the reducing in the inherent destructive behavior of Bofurin I against its host and also reducing its susceptibility to proteolytic degradation. In order to predict the best signal peptides for expression of Buforin I in E. coli , 103 signal sequences were retired from signal peptide databases. Since the purpose of this study was to introduce the optimal signal peptides for periplasmic expression, first, sub-cellular localization site of signal peptides was analyzed. Then, n, h, and c regions of signal peptide, signal peptide probability and physico-chemical features were investigated. Base on the results, MalE, hofQ, papK, ugpB, zraP, and sfmC were introduced as the best signal peptides. For increasing the half-life of mRNA and the increasing the stability of the mRNA against exonuclease activity, secondary structures of mRNA including Shine-Dalgarno, untranslated region of ompA , start codon, signal peptide and sequences of Buforin I were analyzed. Based on the total free energy pilot evaluated and mRNA conformations, papK seemed more appropriate than the rest of the signal peptides. The obtained result of this study can be used for design the periplasmic expression constructs.
The use of anticancer peptides (ACPs) as an alternative/complementary strategy to conventional ch... more The use of anticancer peptides (ACPs) as an alternative/complementary strategy to conventional chemotherapy treatments has been shown to decrease drug resistance and/or severe side effects. However, the efficacy of the positively‐charged ACP is inhibited by elevated levels of negatively‐charged cell‐surface components which trap the peptides and prevent their contact with the cell membrane. Consequently, this decreases ACP‐mediated membrane pore formation and cell lysis. Negatively‐charged heparan sulphate (HS) and chondroitin sulphate (CS) have been shown to inhibit the cytotoxic effect of ACPs.In this study, we propose a strategy to promote the broad utilization of ACPs. In this context, we developed a drug repositioning pipeline to analyse transcriptomics data generated for four different cancer cell lines (A549, HEPG2, HT29, and MCF7) treated with hundreds of drugs in the LINCS L1000 project. Based on previous studies identifying genes modulating levels of the glycosaminoglycans (GAGs) HS and CS at the cell surface, our analysis aimed at identifying drugs inhibiting genes correlated with high HS and CS levels. As a result, we identified six chemicals as likely repositionable drugs with the potential to enhance the performance of ACPs. The codes in R and Python programming languages are publicly available in https://github.com/ElyasMo/ACPs_HS_HSPGs_CS.As a conclusion, these six drugs are highlighted as excellent targets for synergistic studies with ACPs aimed at lowering the costs associated with ACP‐treatment.
Abstract A large number of genome-wide association studies (GWAS) in livestock, especially in dai... more Abstract A large number of genome-wide association studies (GWAS) in livestock, especially in dairy cows, provide favorable conditions to integrate multiple independent studies. Methods such as meta-analysis provide the identification of effective QTLs with higher precision and power. A meta-analysis for milk production traits between different countries was conducted using the GWAS summary statistics (i.e., P-value, sample size, allele effects, and etc.) in Holstein cows. In the present study, METAL software was used for the weighted Z-score model. Gene network analysis was used as a complementary method to improve our knowledge of the genome structure of milk production traits and was implemented through the STRING plug-in in Cytoscape software. The Cytoscape ClueGO plug-in was also used for GO enrichment in order to identify biological process, cellular component, and molecular function associated with genomic regions. The aim of this study was to improve the power of QTLs detection and identify the biological mechanisms associated with milk production traits. Data were obtained from 26 published studies from 2010 to 2019. A total of 2,072 SNPs were identified for milk production traits, of which 1,583 SNPs were significant ( P 0.05 ). Meta-analysis identified 9 QTLs for milk yield, 36 QTLs for fat percentage, and 10 QTLs for protein percentage. Some QTLs were confirmed on BTA14, e.g., BTA14:1801116 close to the DGAT1 gene (milk yield, P = 2.6 × 10 − 131 ; fat percentage, P = 4.8 × 10 − 347 ; protein percentage, P = 7.6 × 10 − 24 ) and BTA14:1651311 close to the PPP1R16A gene (milk yield, P = 2.3 × 10 − 162 ; fat percentage, P = 3.5 × 10 − 153 ). We identified pleiotropic effects of lead SNPs for milk production traits, e.g., one SNP (rs109421300) at BTA14 had pleiotropic effects on milk yield, fat percentage, and protein percentage traits. The most important SNPs for studied traits across countries implicated to network scoring and visualization were including: rs109421300 (DGAT1 gene) for milk yield, fat percentage, and protein percentage; rs109146371 (PPP1R16A gene) for milk yield and fat percentage; rs109968515 (CYHR1 gene) for milk yield and fat percentage; rs134432442 (CPSF1 gene) for fat percentage; rs111018678 (TRAPPC9 gene) for protein percentage. Significant pathways involved in milk production traits through GO term enrichment analysis for biological process, cellular component, and molecular function included: regulation of cation channel activity ( P = 1.6 × 10 − 2 ), ion channel complex ( P = 1.4 × 10 − 2 ), and phosphoric diester hydrolase activity ( P = 1.1 × 10 − 3 ) for milk yield; negative regulation of organ growth ( P = 8.2 × 10 − 3 ), transmembrane transporter complex ( P = 1.6 × 10 − 3 ), and potassium ion transmembrane transporter activity ( P = 8.8 × 10 − 3 ) for fat percentage; mRNA polyadenylation ( P = 1.2 × 10 − 2 ), mRNA cleavage factor complex ( P = 9.8 × 10 − 4 ), and phosphoric diester hydrolase activity ( P = 5.3 × 10 − 3 ) for protein percentage, respectively. Thus, the combination of GWAS summary statistics through a powerful methodology such as meta-analysis will assist us to accurately identify QTLs, potential candidate genes, and biological mechanisms. This kind of studies will help us to have better understanding of QTL regions and genome structure for milk production traits and improve genomic evaluations in Holstein cows. To the best of our knowledge, this is the first meta-analysis of GWAS and GO enrichment across countries for milk production traits in Holstein cows.
Identification of selection signatures may provide a better understanding of domestication proces... more Identification of selection signatures may provide a better understanding of domestication process and candidate genes contributing to this process. In this study, two populations of domestic and wild goats from Iran were analyzed to identify selection signatures. RSB, iHS, and XP‐EHH statistics were used in order to identify robust selection signatures in the goat genome. Genotype data of domestic and wild goats from the NextGen project was used. The data was related to 18 Capra aegagrus (wild goat) and 20 Capra hircus (domestic goat) from Iran. The iHS method indicated 675 and 441 selection signatures in C. aegagrus and C. hircus, respectively. RSB and XP‐EHH methods showed about 370 and 447 selection signatures in C. aegagrus and C. hircus, respectively. These selection signatures were mainly associated with milk production, fleece trait, mammary epithelial cells, reproduction, and immune system.
Objectives: Early, specific, and sensitive detection methods of COVID-19 are essential for force ... more Objectives: Early, specific, and sensitive detection methods of COVID-19 are essential for force stopping its worldwide infection. Although CT images of the lung and/or viral RNA extraction followed by real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) are widely used; they have some limitations. Here, we developed a highly sensitive magnetic bead-based viral RNA extraction assay followed by rRT-PCR. Materials and methods: Case group included oropharyngeal/nasopharyngeal and blood samples from 30 patients diagnosed positive by PCR test for COVID-19 and control group included 30 same samples from COVID-19 negative PCR test individuals. RNA was extracted, using viral RNA extraction kit as well as using our hand-made capture bead-based technique. A one-step cDNA synthesis and Real Time PCR was conducted. A two-step comparison of the different viral RNA extraction methods for oropharyngeal/nasopharyngeal and blood samples was performed. Student t-test was applied with a P<0.05 considered statistically significant. Results: In the case group, all 30 mucosal samples extracted either with viral RNA extraction kit or with beads-based assay were COVID-19 positive although in the latter category, Cqs were much lower. Although 43% of plasma samples extracted by bead-based method were found to be positive but no plasma samples extracted with column-based kit were detected positive by Real Time PCR. Conclusion: Bead-based RNA extraction method can reduce RNA loss by its single-tube performance and enhance the test sensitivity. It is also more sensitive to lower viral loads as shown in the detection of blood samples and the lower Cqs of mucosal samples.
International Journal of Peptide Research and Therapeutics, Feb 6, 2020
Computational prediction of signal peptides is one of the most important steps in genetic enginee... more Computational prediction of signal peptides is one of the most important steps in genetic engineering experiments. The periplasmic expression cause the reducing in the inherent destructive behavior of Bofurin I against its host and also reducing its susceptibility to proteolytic degradation. In order to predict the best signal peptides for expression of Buforin I in E. coli , 103 signal sequences were retired from signal peptide databases. Since the purpose of this study was to introduce the optimal signal peptides for periplasmic expression, first, sub-cellular localization site of signal peptides was analyzed. Then, n, h, and c regions of signal peptide, signal peptide probability and physico-chemical features were investigated. Base on the results, MalE, hofQ, papK, ugpB, zraP, and sfmC were introduced as the best signal peptides. For increasing the half-life of mRNA and the increasing the stability of the mRNA against exonuclease activity, secondary structures of mRNA including Shine-Dalgarno, untranslated region of ompA , start codon, signal peptide and sequences of Buforin I were analyzed. Based on the total free energy pilot evaluated and mRNA conformations, papK seemed more appropriate than the rest of the signal peptides. The obtained result of this study can be used for design the periplasmic expression constructs.
Uploads
Papers by Ali Javadmanesh