scholarly journals Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Syed Shujaat Ali Zaidi ◽  
Masood Ur Rehman Kayani ◽  
Xuegong Zhang ◽  
Younan Ouyang ◽  
Imran Haider Shamsi
Keyword(s):  
Type 2 Diabetes ◽  
Secondary Metabolites ◽  
Metagenomic Data ◽  
Whole Genome ◽  
Functional Information ◽  
Human Gut ◽  
E Coli ◽  
Operon Prediction ◽  
Gut Metagenome

Abstract Background Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions. Results In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101). Conclusion With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

2020 ◽  
Author(s):  
Syed Shujaat Ali Zaidi ◽  
Masood Ur Rehman Kayani ◽  
Xuegong Zhang ◽  
Imran Haider Shamsi
Keyword(s):  
Draft Genome ◽  
Gene Clusters ◽  
Transcriptional Unit ◽  
Metagenomic Data ◽  
Functional Information ◽  
Data Set ◽  
E Coli ◽  
Operon Prediction ◽  
K 12 ◽  
Gut Metagenome

Abstract Background: Efficient regulation of bacterial genes against the environmental stimulus results in unique operonic organizations. Lack of complete reference and functional information makes metagenomic operon prediction challenging and therefore opens new perspectives on the interpretation of the host-microbe interactions. Methods: Here we present MetaRon (pipeline for the prediction of Metagenomic operons), an open-source pipeline explicitly designed for the metagenomic shotgun sequencing data. It recreates the operonic structure without functional information. MetaRon identifies closely packed co-directional gene clusters with a promoter upstream and downstream of the first and last gene, respectively. Promoter prediction marks the transcriptional unit boundary (TUB) of closely packed co-directional gene clusters.Results: Escherichia coli (E. coli) K-12 MG1655 presents a gold standard for operon prediction. Therefore, MetaRon was initially implemented on two simulated illumina datasets: (1) E. coli MG1655 genome (2) a mixture of E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 168 genomes. Operons were predicted in the single genome and mixture of genomes with a sensitivity of 97.8% and 93.7%, respectively. In the next phase, operons predicted from E. coli c20 draft genome isolated from chicken gut metagenome achieved a sensitivity of 94.1%. Lastly, the application of MetaRon on 145 paired-end gut metagenome samples identified 1,232,407 unique operons. Conclusion: MetaRon removes two notable limitations of existing methods: (1) dependency on functional information, and (2) liberates the users from enormous metagenomic data management. Current study showed the idea of using operons as subset to represent the whole-metagenome in terms of secondary metabolites and demonstrated its effectiveness in explaining the occurrence of a disease condition. This will significantly reduce the hefty whole-metagenome data to a small more precise data set. Furthermore, metabolic pathways from the operonic sequences were identified in association with the occurrence of type 2 diabetes (T2D). Presumably, this is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case T2D. The application of MetaRon to metagenome data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

scholarly journals Synergy in Polymicrobial Infections in a Mouse Model of Type 2 Diabetes

2005 ◽  
Vol 73 (9) ◽  
pp. 6055-6063 ◽  
Author(s):  
Matthew D. Mastropaolo ◽  
Nicholas P. Evans ◽  
Meghan K. Byrnes ◽  
Ann M. Stevens ◽  
John L. Robertson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
White Blood Cells ◽  
In Vivo Model ◽  
Diabetic Patients ◽  
Diabetic Mice ◽  
E Coli ◽  
Polymicrobial Infections ◽  
Young Mice

ABSTRACT Human diabetics frequently suffer delayed wound healing, increased susceptibility to localized and systemic infections, and limb amputations as a consequence of the disease. Lower-limb infections in diabetic patients are most often polymicrobial, involving mixtures of aerobic, facultative anaerobic, and anaerobic bacteria. The purpose of this study is to determine if these organisms contribute to synergy in polymicrobial infections by using diabetic mice as an in vivo model. The model was the obese diabetic mouse strain BKS.Cg-m +/+ Lepr db /J, a model of human type 2 diabetes. Young (5- to 6-week-old) prediabetic mice and aged (23- to 24-week-old) diabetic mice were compared. The mice were injected subcutaneously with mixed cultures containing Escherichia coli, Bacteroides fragilis, and Clostridium perfringens. Progression of the infection (usually abscess formation) was monitored by examining mice for bacterial populations and numbers of white blood cells at 1, 8, and 22 days postinfection. Synergy in the mixed infections was defined as a statistically significant increase in the number of bacteria at the site of injection when coinfected with a second bacterium, compared to when the bacterium was inoculated alone. E. coli provided strong synergy to B. fragilis but not to C. perfringens. C. perfringens and B. fragilis provided moderate synergy to each other but only in young mice. B. fragilis was anergistic (antagonistic) to E. coli in coinfections in young mice at 22 days postinfection. When age-matched nondiabetic mice (C57BLKS/J) were used as controls, the diabetic mice exhibited 5 to 35 times the number of CFU as did the nondiabetic mice, indicating that diabetes was a significant factor in the severity of the polymicrobial infections.

scholarly journals Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244876
Author(s):  
Moamen M. Elmassry ◽  
Sunghwan Kim ◽  
Ben Busby
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Metagenomic Data ◽  
Efficacy And Safety ◽  
Human Gut ◽  
Microbial Enzymes ◽  
Sequence Read Archive ◽  
Structural Analogues ◽  
Gut Metagenome

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

scholarly journals Whole-genome re-sequencing for the identification of high contribution susceptibility gene variants in patients with type 2 diabetes

2016 ◽  
Vol 13 (5) ◽  
pp. 3735-3746 ◽  
Author(s):  
XIAOJUAN SUN ◽  
WEIGUO SUI ◽  
XIAOBING WANG ◽  
XIANLIANG HOU ◽  
MINGLIN OU ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Susceptibility Gene ◽  
Gene Variants ◽  
Whole Genome ◽  
High Contribution
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