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The Effect of Lactobacillus acidophilus PTCC 1643 on Cultured Intestinal Epithelial Cells Infected with Salmonella enterica serovar Enteritidis
Mona Moshiri, Mohammad Mehdi Soltan Dallal, Farhad Rezaei, Masoumeh Douraghi, Laleh Sharifi, Zahra Noroozbabaei, Mehrdad Gholami, Abbas Mirshafiey
Osong Public Health Res Perspect. 2017;8(1):54-60.   Published online February 28, 2017
  • 4,639 View
  • 26 Download
  • 10 Crossref
AbstractAbstract PDF

Gastrointestinal disorders caused by Salmonella enterica serovar Enteritidis (SesE) are a significant health problem around the globe. Probiotic bacteria have been shown to have positive effects on the immune responses. Lactobacillus acidophilus was examined for its capability to influence the innate immune response of HT29 intestinal epithelial cells towards SesE. The purpose of this work was to assess the effect of L. acidophilus PTCC 1643 on cultured intestinal epithelial cells infected with SesE.


HT29 cells were cultured in Roswell Park Memorial Institute medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. The cells were treated with L. acidophilus PTCC 1643 after or before challenge with SesE. At 2 and 4 hours post-infection, we measured changes in the expression levels of TLR2 and TLR4 via real-time polymerase chain reaction.


Treatment with L. acidophilus inhibited SesE-induced increases in TLR2 and TLR4 expression in the infected HT29 cells. Moreover, the expression of TLR2 and TLR4 in cells that were pretreated with L. acidophilus and then infected with SesE was significantly higher than that in cells infected with SesE without pretreatment. Taken together, the results indicated that L. acidophilus had an anti-inflammatory effect and modulated the innate immune response to SesE by influencing TLR2 and TLR4 expression.


Our findings suggested that L. acidophilus PTCC 1643 was able to suppress inflammation caused by SesE infection in HT29 cells and reduce TLR2 and TLR4 expression. Additional in vivo and in vitro studies are required to further elucidate the mechanisms underlying this anti-inflammatory effect.


Citations to this article as recorded by  
  • Ligilactobacillus salivarius 7247 Strain: Probiotic Properties and Anti-Salmonella Effect with Prebiotics
    Vyacheslav M. Abramov, Igor V. Kosarev, Andrey V. Machulin, Evgenia I. Deryusheva, Tatiana V. Priputnevich, Alexander N. Panin, Irina O. Chikileva, Tatiana N. Abashina, Ashot M. Manoyan, Anna A. Ahmetzyanova, Olga E. Ivanova, Tigran T. Papazyan, Ilia N. N
    Antibiotics.2023; 12(10): 1535.     CrossRef
  • Anti-Salmonella Defence and Intestinal Homeostatic Maintenance In Vitro of a Consortium Containing Limosilactobacillus fermentum 3872 and Ligilactobacillus salivarius 7247 Strains in Human, Porcine, and Chicken Enterocytes
    Vyacheslav M. Abramov, Igor V. Kosarev, Andrey V. Machulin, Evgenia I. Deryusheva, Tatiana V. Priputnevich, Alexander N. Panin, Irina O. Chikileva, Tatiana N. Abashina, Ashot M. Manoyan, Anna A. Akhmetzyanova, Dmitriy A. Blumenkrants, Olga E. Ivanova, Tig
    Antibiotics.2023; 13(1): 30.     CrossRef
  • Lactobacillus acidophilus ATCC 4356 Exopolysaccharides Suppresses Mediators of Inflammation through the Inhibition of TLR2/STAT-3/P38-MAPK Pathway in DEN-Induced Hepatocarcinogenesis in Rats
    Ola M. S. Khedr, Sawsan M. El-Sonbaty, Fatma S. M. Moawed, Eman I. Kandil, Basma E. Abdel-Maksoud
    Nutrition and Cancer.2022; 74(3): 1037.     CrossRef
  • Osmoporation is a versatile technique to encapsulate fisetin using the probiotic bacteria Lactobacillus acidophilus
    Eduardo Wagner Vasconcelos de Andrade, Sebastien Dupont, Laurent Beney, Roberta Targino Hoskin, Márcia Regina da Silva Pedrini
    Applied Microbiology and Biotechnology.2022; 106(3): 1031.     CrossRef
  • The Game for Three: Salmonella–Host–Microbiota Interaction Models
    Krzysztof Grzymajlo
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • The Functional Roles of Lactobacillus acidophilus in Different Physiological and Pathological Processes
    Huijuan Gao, Xin Li, Xiatian Chen, Deng Hai, Chuang Wei, Lei Zhang, Peifeng Li
    Journal of Microbiology and Biotechnology.2022; 32(10): 1226.     CrossRef
  • Improving bioactive properties of peach juice using Lactobacillus strains fermentation: Antagonistic and anti-adhesion effects, anti-inflammatory and antioxidant properties, and Maillard reaction inhibition
    Seyed Mohammad Bagher Hashemi, Dornoush Jafarpour, Mohammad Jouki
    Food Chemistry.2021; 365: 130501.     CrossRef
  • The immune regulatory role of Lactobacillus acidophilus: An updated meta-analysis of randomized controlled trials
    Wei Zhao, Yangshuo Liu, Lai-Yu Kwok, Tiequan Cai, Wenyi Zhang
    Food Bioscience.2020; 36: 100656.     CrossRef
  • Gene expression changes as predictors of the immune-modulatory effects of probiotics: Towards a better understanding of strain-disease specific interactions
    Frida Gorreja
    NFS Journal.2019; 14-15: 1.     CrossRef
  • A review on anti-adhesion therapies of bacterial diseases
    Arezoo Asadi, Shabnam Razavi, Malihe Talebi, Mehrdad Gholami
    Infection.2019; 47(1): 13.     CrossRef
Cloning, Expression, and Purification of Hyperthermophile α-Amylase from Pyrococcus woesei
Amir Ghasemi, Sobhan Ghafourian, Sedighe Vafaei, Reza Mohebi, Maryam Farzi, Morovat Taherikalani, Nourkhoda Sadeghifard
Osong Public Health Res Perspect. 2015;6(6):336-340.   Published online December 31, 2015
  • 3,047 View
  • 27 Download
  • 3 Crossref
AbstractAbstract PDF
In an attempt α-amylase gene from Pyrococcus woesei was amplified and cloned into a pTYB2 vector to generate the recombinant plasmid pTY- α-amylase.
Escherichia coli BL21 used as a host and protein expression was applied using IPTG. SDS-PAGE assay demonstrated the 100 kDa protein. Amylolytic activity of proteins produced by transformed E. coli cells was detected by zymography, and the rate of active α-amylase with and without the intein tag in both soluble conditions and as inclusion bodies solubilized by 4M urea were measured.
Amylolytic activity of ∼185,000 U/L of bacterial culture was observed from the soluble form of the protein using this system.
These results indicate that this expression system was appropriate for the production of thermostable α-amylase.


Citations to this article as recorded by  
  • Escherichia coli expression and characterization of α-amylase from Geobacillus thermodenitrificans DSM-465
    A. Al-Amri, M. A. Al-Ghamdi, J. A. Khan, H. N. Altayeb, H. Alsulami, M. Sajjad, O. A. Baothman, M. S. Nadeem
    Brazilian Journal of Biology.2022;[Epub]     CrossRef
  • Glycoside Hydrolases and Glycosyltransferases from Hyperthermophilic Archaea: Insights on Their Characteristics and Applications in Biotechnology
    Khadija Amin, Sylvain Tranchimand, Thierry Benvegnu, Ziad Abdel-Razzak, Hala Chamieh
    Biomolecules.2021; 11(11): 1557.     CrossRef
  • Optimization, Purification, and Starch Stain Wash Application of Two Newα-Amylases Extracted from Leaves and Stems ofPergularia tomentosa
    Imen Lahmar, Hanen El Abed, Bassem Khemakhem, Hafedh Belghith, Ferjani Ben Abdallah, Karima Belghith
    BioMed Research International.2017; 2017: 1.     CrossRef
A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs
Feifei Li, Minghao Piao, Yongjun Piao, Meijing Li, Keun Ho Ryu
Osong Public Health Res Perspect. 2014;5(5):279-285.   Published online October 31, 2014
  • 2,916 View
  • 16 Download
  • 7 Crossref
AbstractAbstract PDF
Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification.
We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification.
The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods.
Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.


Citations to this article as recorded by  
  • Cancer hallmark analysis using semantic classification with enhanced topic modelling on biomedical literature
    Supriya Gupta, Aakanksha Sharaff, Naresh Kumar Nagwani
    Multimedia Tools and Applications.2024;[Epub]     CrossRef
  • Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision
    Erdenebileg Batbaatar, Van-Huy Pham, Keun Ho Ryu
    Applied Sciences.2020; 10(3): 834.     CrossRef
  • Microarray cancer feature selection: Review, challenges and research directions
    Moshood A. Hambali, Tinuke O. Oladele, Kayode S. Adewole
    International Journal of Cognitive Computing in En.2020; 1: 78.     CrossRef
  • Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale
    Jnanendra Prasad Sarkar, Indrajit Saha, Adrian Lancucki, Nimisha Ghosh, Michal Wlasnowolski, Grzegorz Bokota, Ashmita Dey, Piotr Lipinski, Dariusz Plewczynski
    Frontiers in Genetics.2020;[Epub]     CrossRef
  • Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification
    Erdenebileg Batbaatar, Kwang Ho Park, Tsatsral Amarbayasgalan, Khishigsuren Davagdorj, Lkhagvadorj Munkhdalai, Van-Huy Pham, Keun Ho Ryu
    IEEE Access.2020; 8: 210800.     CrossRef
  • MicroRNA-449a enhances radiosensitivity by downregulation of c-Myc in prostate cancer cells
    Aihong Mao, Qiuyue Zhao, Xin Zhou, Chao Sun, Jing Si, Rong Zhou, Lu Gan, Hong Zhang
    Scientific Reports.2016;[Epub]     CrossRef
  • Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data
    Peipei Li, Yongjun Piao, Ho Sun Shon, Keun Ho Ryu
    BMC Bioinformatics.2015;[Epub]     CrossRef

PHRP : Osong Public Health and Research Perspectives