<sec><title>Objectives</title><p>Gastrointestinal disorders caused by <italic>Salmonella enterica</italic> serovar Enteritidis (<italic>Se</italic>sE) are a significant health problem around the globe. Probiotic bacteria have been shown to have positive effects on the immune responses. <italic>Lactobacillus acidophilus</italic> was examined for its capability to influence the innate immune response of HT29 intestinal epithelial cells towards <italic>Se</italic>sE. The purpose of this work was to assess the effect of <italic>L. acidophilus</italic> PTCC 1643 on cultured intestinal epithelial cells infected with <italic>Se</italic>sE.</p></sec><sec><title>Methods</title><p>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 <italic>L. acidophilus</italic> PTCC 1643 after or before challenge with <italic>Se</italic>sE. At 2 and 4 hours post-infection, we measured changes in the expression levels of <italic>TLR2</italic> and <italic>TLR4</italic> via real-time polymerase chain reaction.</p></sec><sec><title>Results</title><p>Treatment with <italic>L. acidophilus</italic> inhibited <italic>Se</italic>sE-induced increases in <italic>TLR2</italic> and <italic>TLR4</italic> expression in the infected HT29 cells. Moreover, the expression of <italic>TLR2</italic> and <italic>TLR4</italic> in cells that were pretreated with <italic>L. acidophilus</italic> and then infected with <italic>Se</italic>sE was significantly higher than that in cells infected with <italic>Se</italic>sE without pretreatment. Taken together, the results indicated that <italic>L. acidophilus</italic> had an anti-inflammatory effect and modulated the innate immune response to <italic>Se</italic>sE by influencing <italic>TLR2</italic> and <italic>TLR4</italic> expression.</p></sec><sec><title>Conclusion</title><p>Our findings suggested that <italic>L. acidophilus</italic> PTCC 1643 was able to suppress inflammation caused by <italic>Se</italic>sE infection in HT29 cells and reduce <italic>TLR2</italic> and <italic>TLR4</italic> expression. Additional in vivo and in vitro studies are required to further elucidate the mechanisms underlying this anti-inflammatory effect.</p></sec>
Citations
Citations to this article as recorded by
In silico
analysis of bacteriocins from
Lactobacillus acidophilus
membrane vesicles against
Streptococcus mutans
GtfB protein
Venkatramanan Mahendrarajan, Easwaran Nalini Journal of Biomolecular Structure and Dynamics.2025; 43(18): 10854. CrossRef
Atopic Dermatitis and Atopic Keratoconjunctivitis: New Insights in the Analyses of Microbiota and Probiotic Effect Francesco Petrillo, Annalisa Buonanno, Ludovica Fedi, Marilena Galdiero, Michele Reibaldi, Bruno Tamburini, Emilia Galdiero International Journal of Molecular Sciences.2025; 26(4): 1463. CrossRef
Probiotic significance of
Lactobacillus
strains: a comprehensive review on health impacts, research gaps, and future prospects
Abdul Bari Shah, Aizhamal Baiseitova, Muhammad Zahoor, Ishaq Ahmad, Muhammad Ikram, Allah Bakhsh, Murad Ali Shah, Imdad Ali, Muhammad Idress, Riaz Ullah, Fahd A. Nasr, Mohammed Al-Zharani Gut Microbes.2024;[Epub] CrossRef
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
Objectives
In an attempt α-amylase gene from <i>Pyrococcus woesei</i> was amplified and cloned into a pTYB2 vector to generate the recombinant plasmid pTY- α-amylase. Methods
<i>Escherichia coli</i> 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 <i>E. coli</i> 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. Results
Amylolytic activity of ∼185,000 U/L of bacterial culture was observed from the soluble form of the protein using this system. Conclusion
These results indicate that this expression system was appropriate for the production of thermostable α-amylase.
Citations
Citations to this article as recorded by
Computational screening of selected bioactive phytochemicals against α-Amylase with drug-likeness evaluation Sikandar I Mulla, Rudresh Madapathy, Kuruvalli Gouthami, Vaddi Damodara Reddy, Zabin K Bagewadi, Aejaz Abdullatif Khan, Ibrahim Ahmed Shaikh In Silico Research in Biomedicine.2026; 2: 100286. CrossRef
Overcoming cellular secretion bottlenecks: advanced secretion engineering and molecular tailoring for next-generation microbial α-amylases with enhanced industrial performance Saleem Ahmad, Muhammad Nauman Aftab, Muhammad Ghalib, Muhammad Shahbaz Aslam World Journal of Microbiology and Biotechnology.2026;[Epub] CrossRef
Recombinant Expression and Bioprocess Optimization of Priestia megaterium α‐Amylase and Its Impact on Dough Fermentation Efficiency Atacan Erdem, Kübra Akbulut, Mustafa Türker, Barış Binay Chemistry & Biodiversity.2025;[Epub] CrossRef
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
Objectives
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. Methods
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. Results
The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Conclusion
Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.
Citations
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; 83(31): 76429. 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
First
Prev
