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Zheng L, Shen J, Chen R, Hu Y, Zhao W, Leung ELH, Dai L. Genome engineering of the human gut microbiome. J Genet Genomics 2024; 51:479-491. [PMID: 38218395 DOI: 10.1016/j.jgg.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.
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Affiliation(s)
- Linggang Zheng
- Dr Neher's Biophysics Laboratory for Innovative Drug Discovery/State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Juntao Shen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruiyue Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yucan Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Elaine Lai-Han Leung
- Cancer Center, Faculty of Health Science, University of Macau, Macau 999078, China; MOE Frontiers Science Center for Precision Oncology, University of Macau, Macau 999078, China.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Yang S, Ma H, Wang L, Wang F, Xia J, Liu D, Mu L, Yang X, Liu D. The Role of β3-Adrenergic Receptors in Cold-Induced Beige Adipocyte Production in Pigs. Cells 2024; 13:709. [PMID: 38667324 PMCID: PMC11049327 DOI: 10.3390/cells13080709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/13/2024] [Accepted: 04/14/2024] [Indexed: 04/28/2024] Open
Abstract
After exposure to cold stress, animals enhance the production of beige adipocytes and expedite thermogenesis, leading to improved metabolic health. Although brown adipose tissue in rodents is primarily induced by β3-adrenergic receptor (ADRB3) stimulation, the activation of major β-adrenergic receptors (ADRBs) in pigs has been a topic of debate. To address this, we developed overexpression vectors for ADRB1, ADRB2, and ADRB3 and silenced the expression of these receptors to observe their effects on the adipogenic differentiation stages of porcine preadipocytes. Our investigation revealed that cold stress triggers the transformation of subcutaneous white adipose tissue to beige adipose tissue in pigs by modulating adrenergic receptor levels. Meanwhile, we found that ADRB3 promotes the transformation of white adipocytes into beige adipocytes. Notably, ADRB3 enhances the expression of beige adipose tissue marker genes, consequently influencing cellular respiration and metabolism by regulating lipolysis and mitochondrial expression. Therefore, ADRB3 may serve as a pivotal gene in animal husbandry and contribute to the improvement of cold intolerance in piglets.
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Affiliation(s)
- Shuo Yang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Hong Ma
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
| | - Liang Wang
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
| | - Fang Wang
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
| | - Jiqiao Xia
- College of Animal Science and Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
| | - Dongyu Liu
- College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Linlin Mu
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
| | - Xiuqin Yang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Di Liu
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
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Ayoola MB, Das AR, Krishnan BS, Smith DR, Nanduri B, Ramkumar M. Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility. Microorganisms 2024; 12:134. [PMID: 38257961 PMCID: PMC10819212 DOI: 10.3390/microorganisms12010134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the "Genome Feature Extractor Pipeline" that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.
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Affiliation(s)
- Moses B. Ayoola
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA; (M.B.A.); (A.R.D.); (B.S.K.); (B.N.)
| | - Athish Ram Das
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA; (M.B.A.); (A.R.D.); (B.S.K.); (B.N.)
| | - B. Santhana Krishnan
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA; (M.B.A.); (A.R.D.); (B.S.K.); (B.N.)
| | - David R. Smith
- Department of Population Medicine, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA;
| | - Bindu Nanduri
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA; (M.B.A.); (A.R.D.); (B.S.K.); (B.N.)
| | - Mahalingam Ramkumar
- Department of Computer Science and Engineering, Mississippi State University, Starkville, MS 39762, USA
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Benefo EO, Karanth S, Pradhan AK. A machine learning approach to identifying Salmonella stress response genes in isolates from poultry processing. Food Res Int 2024; 175:113635. [PMID: 38128977 DOI: 10.1016/j.foodres.2023.113635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/21/2023] [Accepted: 10/24/2023] [Indexed: 12/23/2023]
Abstract
We explored the potential of machine learning to identify significant genes associated with Salmonella stress response during poultry processing using whole genome sequencing (WGS) data. The Salmonella isolates (n = 177) used in this study were obtained from various chicken sources (skin before chiller, chicken carcass before chiller, frozen chicken, and post-chill chicken carcass). Six machine learning algorithms (random forest, neural network, cost-sensitive learning, logit boost, and support vector machine linear and radial kernels) were trained on Salmonella WGS data, and model fit was assessed using standard evaluation metrics such as the area under the receiver operating characteristic (AUROC) curve and confusion matrix statistics. All models achieved high performances based on the AUROC metric, with logit boost showing the best performance with an AUROC score of 0.904, sensitivity of 0.889, and specificity of 0.920. The significant genes identified included ybtX, which encodes a Yersiniabactin-associated zinc transporter, and the transferase-encoding genes yccK and thiS. Additionally, genes coding for cold (cspA, cspD, and cspE) and heat shock (rpoH and rpoE) responses were identified. Other significant genes included those involved in lipopolysaccharide biosynthesis (irp1, waaD, rfc, and rfbX), DNA repair and replication (traI), biofilm formation (ccdA and fyuA), and cellular metabolism (irtA).
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Affiliation(s)
- Edmund O Benefo
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA
| | - Shraddha Karanth
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA
| | - Abani K Pradhan
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA.
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Liu Y, Yan Y, Yang K, Yang X, Dong P, Wu H, Luo X, Zhang Y, Zhu L. Inhibitory mechanism of Salmonella Derby biofilm formation by sub-inhibitory concentrations of clove and oregano essential oil: A global transcriptomic study. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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