Horizontal gene transfer in the human gastrointestinal tract: potential spread of antibiotic resistance genes

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Abstract

Bacterial infections are becoming increasingly difficult to treat due to widespread antibiotic resistance among pathogens. This review aims to give an overview of the major horizontal transfer mechanisms and their evolution and then demonstrate the human lower gastrointestinal tract as an environment in which horizontal gene transfer of resistance determinants occurs. Finally, implications for antibiotic usage and the development of resistant infections and persistence of antibiotic resistance genes in populations as a result of horizontal gene transfer in the large intestine will be discussed.

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Most cited references 107

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Persister cells, dormancy and infectious disease.

Kim Lewis (2007)

Several well-recognized puzzles in microbiology have remained unsolved for decades. These include latent bacterial infections, unculturable microorganisms, persister cells and biofilm multidrug tolerance. Accumulating evidence suggests that these seemingly disparate phenomena result from the ability of bacteria to enter into a dormant (non-dividing) state. The molecular mechanisms that underlie the formation of dormant persister cells are now being unravelled and are the focus of this Review.

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In Silico Approach for Predicting Toxicity of Peptides and Proteins

Sudheer Gupta, Pallavi Kapoor, Kumardeep Chaudhary … (2013)

Background Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins. Description We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins. Conclusion ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

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ARDB—Antibiotic Resistance Genes Database

Bo Liu, Mihai Pop (2009)

The treatment of infections is increasingly compromised by the ability of bacteria to develop resistance to antibiotics through mutations or through the acquisition of resistance genes. Antibiotic resistance genes also have the potential to be used for bio-terror purposes through genetically modified organisms. In order to facilitate the identification and characterization of these genes, we have created a manually curated database—the Antibiotic Resistance Genes Database (ARDB)—unifying most of the publicly available information on antibiotic resistance. Each gene and resistance type is annotated with rich information, including resistance profile, mechanism of action, ontology, COG and CDD annotations, as well as external links to sequence and protein databases. Our database also supports sequence similarity searches and implements an initial version of a tool for characterizing common mutations that confer antibiotic resistance. The information we provide can be used as compendium of antibiotic resistance factors as well as to identify the resistance genes of newly sequenced genes, genomes, or metagenomes. Currently, ARDB contains resistance information for 13 293 genes, 377 types, 257 antibiotics, 632 genomes, 933 species and 124 genera. ARDB is available at http://ardb.cbcb.umd.edu/.

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Author and article information

Journal

Journal ID (nlm-ta): Infect Drug Resist

Journal ID (iso-abbrev): Infect Drug Resist

Title: Infection and Drug Resistance

Publisher: Dove Medical Press

ISSN (Electronic): 1178-6973

Publication date Collection: 2014

Publication date (Electronic): 20 June 2014

Volume: 7

Pages: 167-176

Affiliations

Biology Department, Abilene Christian University, Abilene, TX, USA

Author notes

Correspondence: Jennifer R Huddleston, Biology Department, Abilene Christian University, ACU Box 27868, Abilene, TX 79699, USA, Tel +1 325 674 2010, Fax +1 325 674 2009, Email jennifer.huddleston@ 123456acu.edu

Article

Publisher ID: idr-7-167

DOI: 10.2147/IDR.S48820

PMC ID: 4073975

PubMed ID: 25018641

SO-VID: 285cfa0d-2749-4878-ac9e-90d2ce1118e3

License:

The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

Horizontal gene transfer in the human gastrointestinal tract: potential spread of antibiotic resistance genes

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China CDC Weekly: COVID19

Most cited references 107

Persister cells, dormancy and infectious disease.

In Silico Approach for Predicting Toxicity of Peptides and Proteins

ARDB—Antibiotic Resistance Genes Database

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