scholarly journals Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome

2017 ◽  
Author(s):  
Sumayah F. Rahman ◽  
Matthew R. Olm ◽  
Michael J. Morowitz ◽  
Jillian F. Banfield
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Relative Abundance ◽  
Resistance Genes ◽  
Gut Microbiome ◽  
Statistical Tests ◽  
Antibiotic Resistance Genes ◽  
Major Facilitator Superfamily ◽  
Antibiotic Administration ◽  
Beta Lactamase

AbstractAntibiotic resistance in pathogens is extensively studied, yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leverage genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We find that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly,Clostridium difficilestrains harboring this gene are at higher abundance in formula-fed infants compared toC. difficilelacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have faster replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism’s direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data into five principal components classified by boosted decision trees. Among the genes involved in predicting if an organism increased in relative abundance after treatment are those that encode for subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics and predict how organisms in the gut microbiome will respond to antibiotic administration.ImportanceThe process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows for unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical and industrial applications.

Joining the resistance

2018 ◽  
Vol 10 (425) ◽  
pp. eaar7519
Author(s):  
Stephanie A. Christenson
Keyword(s):  
Antibiotic Resistance ◽  
Preterm Infants ◽  
Resistance Genes ◽  
Gut Microbiome ◽  
Antibiotic Resistance Genes ◽  
Antibiotic Administration ◽  
Before And After

The effect of antibiotic resistance genes on the gut microbiome is examined in preterm infants before and after antibiotic administration.

An Orally Delivered Beta-Lactamase Protects the Gut Microbiome from Antibiotic-Mediated Damage and Mitigates the Propagation of Antibiotic-Resistance Genes in a Porcine Dysbiosis Model

2017 ◽  
Vol 152 (5) ◽  
pp. S1305-S1306
Author(s):  
Sheila Connelly ◽  
Christian Furlan Freguia ◽  
Poorani Subramanian ◽  
Nur A. Hasan ◽  
Rita R. Colwell ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Gut Microbiome ◽  
Antibiotic Resistance Genes ◽  
Beta Lactamase

scholarly journals Oral Beta-Lactamase Protects the Canine Gut Microbiome from Oral Amoxicillin-Mediated Damage

2019 ◽  
Vol 7 (5) ◽  
pp. 150 ◽  
Author(s):  
Sheila Connelly ◽  
Brian Fanelli ◽  
Nur A. Hasan ◽  
Rita R. Colwell ◽  
Michael Kaleko
Keyword(s):  
Antibiotic Resistance ◽  
Small Intestine ◽  
Resistance Genes ◽  
Gut Microbiome ◽  
Antibiotic Resistance Genes ◽  
Oral Antibiotic ◽  
Beta Lactamase ◽  
Gi Tract ◽  
Beta Lactam ◽  
Beta Lactam Antibiotics

Antibiotics damage the gut microbiome, which can result in overgrowth of pathogenic microorganisms and emergence of antibiotic resistance. Inactivation of antibiotics in the small intestine represents a novel strategy to protect the colonic microbiota. SYN-004 (ribaxamase) is a beta-lactamase formulated for oral delivery intended to degrade intravenously administered beta-lactam antibiotics in the gastrointestinal (GI) tract. The enteric coating of ribaxamase protects the enzyme from stomach acid and mediates pH-dependent release in the upper small intestine, the site of antibiotic biliary excretion. Clinical benefit was established in animal and human studies in which ribaxamase was shown to degrade ceftriaxone in the GI tract, thereby preserving the gut microbiome, significantly reducing Clostridioides difficile disease, and attenuating antibiotic resistance. To expand ribaxamase utility to oral beta-lactams, delayed release formulations of ribaxamase, SYN-007, were engineered to allow enzyme release in the lower small intestine, distal to the site of oral antibiotic absorption. Based on in vitro dissolution profiles, three SYN-007 formulations were selected for evaluation in a canine model of antibiotic-mediated gut dysbiosis. Dogs received amoxicillin (40 mg/kg, PO, TID) +/- SYN-007 (10 mg, PO, TID) for five days. Serum amoxicillin levels were measured after the first and last antibiotic doses and gut microbiomes were evaluated using whole genome shotgun sequence metagenomics analyses of fecal DNA prior to and after antibiotic treatment. Serum amoxicillin levels did not significantly differ +/- SYN-007 after the first dose for all SYN-007 formulations, while only one SYN-007 formulation did not significantly reduce systemic antibiotic concentrations after the last dose. Gut microbiomes of animals receiving amoxicillin alone displayed significant loss of diversity and emergence of antibiotic resistance genes. In contrast, for animals receiving amoxicillin + SYN-007, microbiome diversities were not altered significantly and the presence of antibiotic resistance genes was reduced. These data demonstrate that SYN-007 diminishes amoxicillin-mediated microbiome disruption and mitigates emergence and propagation of antibiotic resistance genes without interfering with antibiotic systemic absorption. Thus, SYN-007 has the potential to protect the gut microbiome by inactivation of beta-lactam antibiotics when administered by both oral and parenteral routes and to reduce emergence of antibiotic-resistant pathogens.

scholarly journals Combined effects of composting and antibiotic administration on cattle manure–borne antibiotic resistance genes

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ishi Keenum ◽  
Robert K. Williams ◽  
Partha Ray ◽  
Emily D. Garner ◽  
Katharine F. Knowlton ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Land Application ◽  
Multidimensional Analysis ◽  
Small Scale ◽  
Antibiotic Administration ◽  
Metagenomic Sequencing ◽  
Combined Effects ◽  
Bacterial Microbiota

Abstract Background Research is needed to delineate the relative and combined effects of different antibiotic administration and manure management practices in either amplifying or attenuating the potential for antibiotic resistance to spread. Here, we carried out a comprehensive parallel examination of the effects of small-scale (> 55 °C × 3 days) static and turned composting of manures from dairy and beef cattle collected during standard antibiotic administration (cephapirin/pirlimycin or sulfamethazine/chlortetracycline/tylosin, respectively), versus from untreated cattle, on “resistomes” (total antibiotic resistance genes (ARGs) determined via shotgun metagenomic sequencing), bacterial microbiota, and indicator ARGs enumerated via quantitative polymerase chain reaction. To gain insight into the role of the thermophilic phase, compost was also externally heated to > 55 °C × 15 days. Results Progression of composting with time and succession of the corresponding bacterial microbiota was the overarching driver of the resistome composition (ANOSIM; R = 0.424, p = 0.001, respectively) in all composts at the small-scale. Reduction in relative abundance (16S rRNA gene normalized) of total ARGs in finished compost (day 42) versus day 0 was noted across all conditions (ANOSIM; R = 0.728, p = 0.001), except when externally heated. Sul1, intI1, beta-lactam ARGs, and plasmid-associated genes increased in all finished composts as compared with the initial condition. External heating more effectively reduced certain clinically relevant ARGs (blaOXA, blaCARB), fecal coliforms, and resistome risk scores, which take into account putative pathogen annotations. When manure was collected during antibiotic administration, taxonomic composition of the compost was distinct according to nonmetric multidimensional analysis and tet(W) decayed faster in the dairy manure with antibiotic condition and slower in the beef manure with antibiotic condition. Conclusions This comprehensive, integrated study revealed that composting had a dominant effect on corresponding resistome composition, while little difference was noted as a function of collecting manure during antibiotic administration. Reduction in total ARGs, tet(W), and resistome risk suggested that composting reduced some potential for antibiotic resistance to spread, but the increase and persistence of other indicators of antibiotic resistance were concerning. Results indicate that composting guidelines intended for pathogen reduction do not necessarily provide a comprehensive barrier to ARGs or their mobility prior to land application and additional mitigation measures should be considered.

scholarly journals HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yu Li ◽  
Zeling Xu ◽  
Wenkai Han ◽  
Huiluo Cao ◽  
Ramzan Umarov ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Third Party ◽  
Superior Performance ◽  
Beta Lactamase ◽  
Learning Framework ◽  
Sequence Encoding ◽  
Global Threat

Abstract Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.

scholarly journals Metagenomic Community Composition and Resistome Analysis in a Full-scale Cold Climate Wastewater Treatment Plant

2021 ◽  
Author(s):  
Miguel Uyaguari
Keyword(s):  
Antibiotic Resistance ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Relative Abundance ◽  
Resistance Genes ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Antibiotic Resistance Genes ◽  
Gene Copies ◽  
Final Effluent

Abstract Background: Wastewater treatment plants are an essential part of maintaining the health and safety of the general public. However, they are also an anthropogenic source of antibiotic resistance genes. In this study, we characterized the resistome, the distribution of classes 1-3 integron-integrase genes (intI1, intI2, and intI3) as mobile genetic element biomarkers, and the bacterial and phage community compositions in the North End Sewage Treatment Plant in Winnipeg, Manitoba. Samples were collected from raw sewage, returned activated sludge, final effluent, and dewatered sludge. A total of 28 bacterial and viral metagenomes were sequenced over two seasons, fall and winter. Integron-integrase genes, the 16S rRNA gene, and the coliform beta-glucuronidase gene were also quantified during this time period. Results: Bacterial classes observed above 1% relative abundance in all treatments were Actinobacteria (39.24% ± 0.25%), Beta-proteobacteria (23.99% ± 0.16%), Gamma-proteobacteria (11.06% ± 0.09%), and Alpha-proteobacteria (9.18 ± 0.04%). Families within the Caudovirales order: Siphoviridae (48.69% ± 0.10%), Podoviridae (23.99% ± 0.07%), and Myoviridae (19.94% ± 0.09%) were the dominant phage observed throughout the NESTP. The most abundant bacterial genera (in terms of average percent relative abundance) in influent, returned activated sludge, final effluent, and sludge, respectively, includes Mycobacterium (37.4%, 18.3%, 46.1%, and 7.7%), Acidovorax (8.9%, 10.8%, 5.4%, and 1.3%), and Polaromonas (2.5%, 3.3%, 1.4%, and 0.4%).The most abundant class of antibiotic resistance in bacterial samples was tetracycline resistance (17.86% ± 0.03%) followed by peptide antibiotics (14.24% ± 0.03%), and macrolides (10.63% ± 0.02%). Similarly, the phage samples contained a higher prevalence of macrolide (30.12% ± 0.30%), peptide antibiotic (10.78% ± 0.13%), and tetracycline (8.69% ± 0.11%) resistance. In addition, intI1 was the most abundant integron-integrase gene throughout treatment (1.14x104 gene copies/mL) followed by intI3 (4.97x103 gene copies/mL) while intI2 abundance remained low (6.4x101 gene copies/mL).Conclusions: The wastewater treatment plant successfully reduced the abundance of bacteria, DNA bacteriophages, and antibiotic resistance genes although many of them still remained in effluent and biosolids. The presence of integron-integrase genes throughout treatment and in effluent suggests that antibiotic resistance genes could be actively disseminating resistance between both environmental and pathogenic bacteria.

scholarly journals Horizontal transfer of antibiotic resistance genes in the human gut microbiome

2020 ◽  
Vol 53 ◽  
pp. 35-43 ◽  
Author(s):  
Ross S McInnes ◽  
Gregory E McCallum ◽  
Lisa E Lamberte ◽  
Willem van Schaik
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Gut Microbiome ◽  
Horizontal Transfer ◽  
Antibiotic Resistance Genes ◽  
Human Gut ◽  
Human Gut Microbiome

Exploring divergent antibiotic resistance genes in ancient metagenomes and discovery of a novel beta-lactamase family

2016 ◽  
Vol 8 (5) ◽  
pp. 886-895 ◽  
Author(s):  
Nicolás Rascovan ◽  
Amar Telke ◽  
Didier Raoult ◽  
Jean Marc Rolain ◽  
Christelle Desnues
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Beta Lactamase

Investigation of the antibiotic resistance and biofilm formation of Staphylococcus aureus strains isolated from gangrenous mastitis of ewes

2012 ◽  
Vol 60 (2) ◽  
pp. 189-197 ◽  
Author(s):  
Osman Tel ◽  
Özkan Aslantaş ◽  
Oktay Keskin ◽  
Ebru Yilmaz ◽  
Cemil Demir
Keyword(s):  
Staphylococcus Aureus ◽  
Antibiotic Resistance ◽  
Virulence Factor ◽  
Resistance Genes ◽  
Biofilm Formation ◽  
Antibiotic Resistance Genes ◽  
Penicillin G ◽  
Beta Lactamase ◽  
Potential Virulence Factor ◽  
Amoxicillin Clavulanic Acid

In this study,Staphylococcus aureusstrains (n = 110) isolated from seven ewe flocks in Sanliurfa, Turkey were screened for antibiotic resistance and biofilmforming ability as well as for genes associated with antibiotic resistance and biofilm-forming ability. All isolates were found to be susceptible to oxacillin, gentamicin, clindamycin, cefoxitin, tetracycline, vancomycin, amoxicillin-clavulanic acid, ciprofloxacin and sulphamethoxazole-trimethoprim. The percent proportions of strains resistant to penicillin G, ampicillin and erythromycin were 27.2% (n = 30), 25.4% (n = 28) and 6.3% (n = 7), respectively. Regarding the antibiotic resistance genes, 32 (29%) isolates carried theblaZ and 8 (7.2%) theermC gene. Other resistance genes were not detected in the isolates. All isolates showed biofilm-forming ability on Congo red agar (CRA), while 108 (98.18%) and 101 (91.81%) of them were identified as biofilm producers by the use of standard tube (ST) and microplate (MP) methods, respectively. All isolates carried theicaA andicaD genes but none of them harboured thebapgene. The results demonstrated thatS. aureusisolates from gangrenous mastitis were mainly resistant to penicillins (which are susceptible to the staphylococcal beta-lactamase enzyme), and less frequently to erythromycin. Furthermore, all of theS. aureusisolates produced biofilm which was considered a potential virulence factor in the pathogenesis of staphylococcal mastitis.

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