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S. Pillay
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1
Predicted meta-omics
A potential solution to multi-omics data scarcity in microbiome studies
Imbalances in the gut microbiome have been linked to conditions such as inflammatory bowel disease, diabetes, and cancer. While metagenomics and amplicon sequencing are commonly used to study the microbiome, they do not capture all layers of microbial functions. Other meta-omics data can provide more insights, but these are more costly and laborious to procure. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data, enabling the prediction of these other forms of meta-omics data from metagenomics. We evaluated several machine learning models for predicting meta-omics features from various meta-omics inputs. Simpler architectures such as elastic net regression and random forests generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles was more challenging. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of inflammatory bowel disease classification, with performance comparable to that of experimental data.
...
Imbalances in the gut microbiome have been linked to conditions such as inflammatory bowel disease, diabetes, and cancer. While metagenomics and amplicon sequencing are commonly used to study the microbiome, they do not capture all layers of microbial functions. Other meta-omics data can provide more insights, but these are more costly and laborious to procure. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data, enabling the prediction of these other forms of meta-omics data from metagenomics. We evaluated several machine learning models for predicting meta-omics features from various meta-omics inputs. Simpler architectures such as elastic net regression and random forests generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles was more challenging. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of inflammatory bowel disease classification, with performance comparable to that of experimental data.
Antimicrobial resistance (AMR), termed a "silent pandemic" has caused 4.95 million deaths in 2019, with numbers expected to rise. AMR spans human, animal, and environmental sectors, requiring a One Health approach to address this multifaceted global challenge. This dissertation focuses on the under-represented non-clinical sectors and employs the use of metagenomic data to advance AMR research.
The primary focus in AMR research has been on clinical settings, overlooking animals and the environment and leaving data gaps in resource-limited regions. The world of AMR and metagenomic data is first introduced followed by an in-depth review of AMR in non-clinical sectors and the information metagenomic data can provide. The emphasis is on bioinformatic tools, databases, and workflows to support researchers utilising metagenomic data for AMR studies in these sectors.
Moving forward, the wastewater treatment process, including the neglected upstream and downstream freshwater systems, is examined, to assess the microbiome, resistome and mobilome at each stage. Specific differences within every wastewater treatment plant process sector and their role in AMR transmission are identified. Inspired by the natural baseline of antibiotic resistance in soil, a comparative study of the composition of the microbiome, resistome and mobilome in different soil types, from natural to rural soils, is then further presented. Given the limited information on resistance patterns and the effects of geographical and anthropogenic factors, the influence of antibiotic resistance in different soil types is then further explored.
The swine industry, as the largest consumer of antibiotics, raises concerns about the effects of antibiotic use on the gut microbiome of animals. Antibiotics can impact animal health and promote the transmission of AMR to other non-clinical sectors and humans. How antibiotic use affects the fecal microbiome of pigs raised with and without antibiotics is examined to understand the dynamics of antibiotic resistance in the swine industry.
The burden of AMR, particularly in low- and middle-income countries, where resources for infectious disease surveillance are limited, was the inspiration to propose a method for generating metagenomic data in-field and in resource-limited settings, offering a cost-effective solution for outbreak monitoring and pathogen detection.
The main goal of this dissertation is to highlight the under-represented sectors, their significant role in AMR and to promote global inclusivity.
...
The primary focus in AMR research has been on clinical settings, overlooking animals and the environment and leaving data gaps in resource-limited regions. The world of AMR and metagenomic data is first introduced followed by an in-depth review of AMR in non-clinical sectors and the information metagenomic data can provide. The emphasis is on bioinformatic tools, databases, and workflows to support researchers utilising metagenomic data for AMR studies in these sectors.
Moving forward, the wastewater treatment process, including the neglected upstream and downstream freshwater systems, is examined, to assess the microbiome, resistome and mobilome at each stage. Specific differences within every wastewater treatment plant process sector and their role in AMR transmission are identified. Inspired by the natural baseline of antibiotic resistance in soil, a comparative study of the composition of the microbiome, resistome and mobilome in different soil types, from natural to rural soils, is then further presented. Given the limited information on resistance patterns and the effects of geographical and anthropogenic factors, the influence of antibiotic resistance in different soil types is then further explored.
The swine industry, as the largest consumer of antibiotics, raises concerns about the effects of antibiotic use on the gut microbiome of animals. Antibiotics can impact animal health and promote the transmission of AMR to other non-clinical sectors and humans. How antibiotic use affects the fecal microbiome of pigs raised with and without antibiotics is examined to understand the dynamics of antibiotic resistance in the swine industry.
The burden of AMR, particularly in low- and middle-income countries, where resources for infectious disease surveillance are limited, was the inspiration to propose a method for generating metagenomic data in-field and in resource-limited settings, offering a cost-effective solution for outbreak monitoring and pathogen detection.
The main goal of this dissertation is to highlight the under-represented sectors, their significant role in AMR and to promote global inclusivity.
...
Antimicrobial resistance (AMR), termed a "silent pandemic" has caused 4.95 million deaths in 2019, with numbers expected to rise. AMR spans human, animal, and environmental sectors, requiring a One Health approach to address this multifaceted global challenge. This dissertation focuses on the under-represented non-clinical sectors and employs the use of metagenomic data to advance AMR research.
The primary focus in AMR research has been on clinical settings, overlooking animals and the environment and leaving data gaps in resource-limited regions. The world of AMR and metagenomic data is first introduced followed by an in-depth review of AMR in non-clinical sectors and the information metagenomic data can provide. The emphasis is on bioinformatic tools, databases, and workflows to support researchers utilising metagenomic data for AMR studies in these sectors.
Moving forward, the wastewater treatment process, including the neglected upstream and downstream freshwater systems, is examined, to assess the microbiome, resistome and mobilome at each stage. Specific differences within every wastewater treatment plant process sector and their role in AMR transmission are identified. Inspired by the natural baseline of antibiotic resistance in soil, a comparative study of the composition of the microbiome, resistome and mobilome in different soil types, from natural to rural soils, is then further presented. Given the limited information on resistance patterns and the effects of geographical and anthropogenic factors, the influence of antibiotic resistance in different soil types is then further explored.
The swine industry, as the largest consumer of antibiotics, raises concerns about the effects of antibiotic use on the gut microbiome of animals. Antibiotics can impact animal health and promote the transmission of AMR to other non-clinical sectors and humans. How antibiotic use affects the fecal microbiome of pigs raised with and without antibiotics is examined to understand the dynamics of antibiotic resistance in the swine industry.
The burden of AMR, particularly in low- and middle-income countries, where resources for infectious disease surveillance are limited, was the inspiration to propose a method for generating metagenomic data in-field and in resource-limited settings, offering a cost-effective solution for outbreak monitoring and pathogen detection.
The main goal of this dissertation is to highlight the under-represented sectors, their significant role in AMR and to promote global inclusivity.
The primary focus in AMR research has been on clinical settings, overlooking animals and the environment and leaving data gaps in resource-limited regions. The world of AMR and metagenomic data is first introduced followed by an in-depth review of AMR in non-clinical sectors and the information metagenomic data can provide. The emphasis is on bioinformatic tools, databases, and workflows to support researchers utilising metagenomic data for AMR studies in these sectors.
Moving forward, the wastewater treatment process, including the neglected upstream and downstream freshwater systems, is examined, to assess the microbiome, resistome and mobilome at each stage. Specific differences within every wastewater treatment plant process sector and their role in AMR transmission are identified. Inspired by the natural baseline of antibiotic resistance in soil, a comparative study of the composition of the microbiome, resistome and mobilome in different soil types, from natural to rural soils, is then further presented. Given the limited information on resistance patterns and the effects of geographical and anthropogenic factors, the influence of antibiotic resistance in different soil types is then further explored.
The swine industry, as the largest consumer of antibiotics, raises concerns about the effects of antibiotic use on the gut microbiome of animals. Antibiotics can impact animal health and promote the transmission of AMR to other non-clinical sectors and humans. How antibiotic use affects the fecal microbiome of pigs raised with and without antibiotics is examined to understand the dynamics of antibiotic resistance in the swine industry.
The burden of AMR, particularly in low- and middle-income countries, where resources for infectious disease surveillance are limited, was the inspiration to propose a method for generating metagenomic data in-field and in resource-limited settings, offering a cost-effective solution for outbreak monitoring and pathogen detection.
The main goal of this dissertation is to highlight the under-represented sectors, their significant role in AMR and to promote global inclusivity.
Journal article
(2025)
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Stephanie Pillay, Ramin Shirali Hossein Zade, Paul van Lent, David Calderón-Franco, Thomas Abeel
Bacterial resistance to antimicrobials is a global health threat. Within the One Health context, water from regions with high antibiotic usage, such as clinical and urban areas, collects at wastewater treatment plants (WWTPs). In the WWTP, the activated sludge becomes a complex environment where various antimicrobials and microorganisms converge. While significant research has focused on the influent, activated sludge, and effluent, upstream and downstream sectors around the WWTP are often neglected. We conducted a systematic analysis using five publicly available metagenomic datasets (n=164) from different WWTP sectors and adjacent freshwater systems: upstream (n=14), influent (n=14), activated sludge (n=109), effluent (n=14), and downstream (n=13) to identify and characterise the microbiome, resistome, and mobilome. Opportunistic pathogenic bacteria, such as Pseudomonas, Aeromonas, and Acidovorax, were found in all WWTP sectors, with abundances exceeding 9% in the influent. ESKAPE pathogens, including Klebsiella pneumoniae and Enterobacter species, were identified in the effluent with abundances over 1%. We detected 230 antibiotic resistance genes (ARGs) throughout the WWTP. FTU and CKO β-lactamase gene families dominated the upstream, effluent, and downstream sectors, while the OXA β-lactamase gene family was highly abundant in the influent and activated sludge. ARGs, such as the OXA β-lactamase gene family, were linked to plasmids. Class-1 integrons, associated with the sul1 gene, a marker for anthropogenic pollution, were prevalent in the effluent and downstream sectors. Integrative elements (ICEclc, Tn4371, and PGI2), linked to ARGs, were identified in all sectors, increasing AMR dissemination. These integrative elements conferred resistance to antibiotics, including sulfonamides, tetracyclines and carbapenems. Our findings highlight the presence of ARGs and mobile genetic elements in WWTPs and nearby freshwater systems, raising concerns about AMR transmission to humans, animals, and the environment. This study emphasises the need for effective AMR monitoring and strategies in wastewater treatment to protect public and environmental health.
...
Bacterial resistance to antimicrobials is a global health threat. Within the One Health context, water from regions with high antibiotic usage, such as clinical and urban areas, collects at wastewater treatment plants (WWTPs). In the WWTP, the activated sludge becomes a complex environment where various antimicrobials and microorganisms converge. While significant research has focused on the influent, activated sludge, and effluent, upstream and downstream sectors around the WWTP are often neglected. We conducted a systematic analysis using five publicly available metagenomic datasets (n=164) from different WWTP sectors and adjacent freshwater systems: upstream (n=14), influent (n=14), activated sludge (n=109), effluent (n=14), and downstream (n=13) to identify and characterise the microbiome, resistome, and mobilome. Opportunistic pathogenic bacteria, such as Pseudomonas, Aeromonas, and Acidovorax, were found in all WWTP sectors, with abundances exceeding 9% in the influent. ESKAPE pathogens, including Klebsiella pneumoniae and Enterobacter species, were identified in the effluent with abundances over 1%. We detected 230 antibiotic resistance genes (ARGs) throughout the WWTP. FTU and CKO β-lactamase gene families dominated the upstream, effluent, and downstream sectors, while the OXA β-lactamase gene family was highly abundant in the influent and activated sludge. ARGs, such as the OXA β-lactamase gene family, were linked to plasmids. Class-1 integrons, associated with the sul1 gene, a marker for anthropogenic pollution, were prevalent in the effluent and downstream sectors. Integrative elements (ICEclc, Tn4371, and PGI2), linked to ARGs, were identified in all sectors, increasing AMR dissemination. These integrative elements conferred resistance to antibiotics, including sulfonamides, tetracyclines and carbapenems. Our findings highlight the presence of ARGs and mobile genetic elements in WWTPs and nearby freshwater systems, raising concerns about AMR transmission to humans, animals, and the environment. This study emphasises the need for effective AMR monitoring and strategies in wastewater treatment to protect public and environmental health.
Journal article
(2024)
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Erin Noel Jordan, Ramin Shirali Hossein Zade, Stephanie Pillay, Paul van Lent, Thomas Abeel, Oliver Kayser
Yeast metabolism can be engineered to produce xenobiotic compounds, such as cannabinoids, the principal isoprenoids of the plant Cannabis sativa, through heterologous metabolic pathways. However, yeast cell factories continue to have low cannabinoid production. This study employed an integrated omics approach to investigate the physiological effects of cannabidiol on S. cerevisiae CENPK2-1C yeast cultures. We treated the experimental group with 0.5 mM CBD and monitored CENPK2-1C cultures. We observed a latent-stationary phase post-diauxic shift in the experimental group and harvested samples in the inflection point of this growth phase for transcriptomic and metabolomic analysis. We compared the transcriptomes of the CBD-treated yeast and the positive control, identifying eight significantly overexpressed genes with a log fold change of at least 1.5 and a significant adjusted p-value. Three notable genes were PDR5 (an ABC-steroid and cation transporter), CIS1, and YGR035C. These genes are all regulated by pleiotropic drug resistance linked promoters. Knockout and rescue of PDR5 showed that it is a causal factor in the post-diauxic shift phenotype. Metabolomic analysis revealed 48 significant spectra associated with CBD-fed cell pellets, 20 of which were identifiable as non-CBD compounds, including fatty acids, glycerophospholipids, and phosphate-salvage indicators. Our results suggest that mitochondrial regulation and lipidomic remodeling play a role in yeast’s response to CBD, which are employed in tandem with pleiotropic drug resistance (PDR). We conclude that bioengineers should account for off-target product C-flux, energy use from ABC-transport, and post-stationary phase cell growth when developing cannabinoid-biosynthetic yeast strains.
...
Yeast metabolism can be engineered to produce xenobiotic compounds, such as cannabinoids, the principal isoprenoids of the plant Cannabis sativa, through heterologous metabolic pathways. However, yeast cell factories continue to have low cannabinoid production. This study employed an integrated omics approach to investigate the physiological effects of cannabidiol on S. cerevisiae CENPK2-1C yeast cultures. We treated the experimental group with 0.5 mM CBD and monitored CENPK2-1C cultures. We observed a latent-stationary phase post-diauxic shift in the experimental group and harvested samples in the inflection point of this growth phase for transcriptomic and metabolomic analysis. We compared the transcriptomes of the CBD-treated yeast and the positive control, identifying eight significantly overexpressed genes with a log fold change of at least 1.5 and a significant adjusted p-value. Three notable genes were PDR5 (an ABC-steroid and cation transporter), CIS1, and YGR035C. These genes are all regulated by pleiotropic drug resistance linked promoters. Knockout and rescue of PDR5 showed that it is a causal factor in the post-diauxic shift phenotype. Metabolomic analysis revealed 48 significant spectra associated with CBD-fed cell pellets, 20 of which were identifiable as non-CBD compounds, including fatty acids, glycerophospholipids, and phosphate-salvage indicators. Our results suggest that mitochondrial regulation and lipidomic remodeling play a role in yeast’s response to CBD, which are employed in tandem with pleiotropic drug resistance (PDR). We conclude that bioengineers should account for off-target product C-flux, energy use from ABC-transport, and post-stationary phase cell growth when developing cannabinoid-biosynthetic yeast strains.
SHIP
Identifying antimicrobial resistance gene transfer between plasmids
Motivation: Plasmids are carriers for antimicrobial resistance (AMR) genes and can exchange genetic material with other structures, contributing to the spread of AMR. There is no reliable approach to identify the transfer of AMR genes across plasmids. This is mainly due to the absence of a method to assess the phylogenetic distance of plasmids, as they show large DNA sequence variability. Identifying and quantifying such transfer can provide novel insight into the role of small mobile elements and resistant plasmid regions in the spread of AMR. Results: We developed SHIP, a novel method to quantify plasmid similarity based on the dynamics of plasmid evolution. This allowed us to find conserved fragments containing AMR genes in structurally different and phylogenetically distant plasmids, which is evidence for lateral transfer. Our results show that regions carrying AMR genes are highly mobilizable between plasmids through transposons, integrons, and recombination events, and contribute to the spread of AMR. Identified transferred fragments include a multi-resistant complex class 1 integron in Escherichia coli and Klebsiella pneumoniae, and a region encoding tetracycline resistance transferred through recombination in Enterococcus faecalis.
...
Motivation: Plasmids are carriers for antimicrobial resistance (AMR) genes and can exchange genetic material with other structures, contributing to the spread of AMR. There is no reliable approach to identify the transfer of AMR genes across plasmids. This is mainly due to the absence of a method to assess the phylogenetic distance of plasmids, as they show large DNA sequence variability. Identifying and quantifying such transfer can provide novel insight into the role of small mobile elements and resistant plasmid regions in the spread of AMR. Results: We developed SHIP, a novel method to quantify plasmid similarity based on the dynamics of plasmid evolution. This allowed us to find conserved fragments containing AMR genes in structurally different and phylogenetically distant plasmids, which is evidence for lateral transfer. Our results show that regions carrying AMR genes are highly mobilizable between plasmids through transposons, integrons, and recombination events, and contribute to the spread of AMR. Identified transferred fragments include a multi-resistant complex class 1 integron in Escherichia coli and Klebsiella pneumoniae, and a region encoding tetracycline resistance transferred through recombination in Enterococcus faecalis.
Evaluating long-read de novo assembly tools for eukaryotic genomes
Insights and considerations
Journal article
(2023)
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Bianca-Maria Cosma , Ramin Shirali Hossein Zade, Erin Noel Jordan, Paul van Lent, Chengyao Peng, Stephanie Pillay, Thomas Abeel
Background: Assembly algorithm choice should be a deliberate, well-justified decision when researchers create genome assemblies for eukaryotic organisms from third-generation sequencing technologies. While third-generation sequencing by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) has overcome the disadvantages of short read lengths specific to next-generation sequencing (NGS), third-generation sequencers are known to produce more error-prone reads, thereby generating a new set of challenges for assembly algorithms and pipelines. However, the introduction of HiFi reads, which offer substantially reduced error rates, has provided a promising solution for more accurate assembly outcomes. Since the introduction of third-generation sequencing technologies, many tools have been developed that aim to take advantage of the longer reads, and researchers need to choose the correct assembler for their projects. Results: We benchmarked state-of-the-art long-read de novo assemblers to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 12 real and 64 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio continuous long-read (CLR), PacBio high-fidelity (HiFi), and ONT sequencing to evaluate the assemblers. We include 5 commonly used long-read assemblers in our benchmark: Canu, Flye, Miniasm, Raven, and wtdbg2 for ONT and PacBio CLR reads. For PacBio HiFi reads, we include 5 state-of-the-art HiFi assemblers: HiCanu, Flye, Hifiasm, LJA, and MBG. Evaluation categories address the following metrics: reference-based metrics, assembly statistics, misassembly count, BUSCO completeness, runtime, and RAM usage. Additionally, we investigated the effect of increased read length on the quality of the assemblies and report that read length can, but does not always, positively impact assembly quality. Conclusions: Our benchmark concludes that there is no assembler that performs the best in all the evaluation categories. However, our results show that overall Flye is the best-performing assembler for PacBio CLR and ONT reads, both on real and simulated data. Meanwhile, best-performing PacBio HiFi assemblers are Hifiasm and LJA. Next, the benchmarking using longer reads shows that the increased read length improves assembly quality, but the extent to which that can be achieved depends on the size and complexity of the reference genome.
...
Background: Assembly algorithm choice should be a deliberate, well-justified decision when researchers create genome assemblies for eukaryotic organisms from third-generation sequencing technologies. While third-generation sequencing by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) has overcome the disadvantages of short read lengths specific to next-generation sequencing (NGS), third-generation sequencers are known to produce more error-prone reads, thereby generating a new set of challenges for assembly algorithms and pipelines. However, the introduction of HiFi reads, which offer substantially reduced error rates, has provided a promising solution for more accurate assembly outcomes. Since the introduction of third-generation sequencing technologies, many tools have been developed that aim to take advantage of the longer reads, and researchers need to choose the correct assembler for their projects. Results: We benchmarked state-of-the-art long-read de novo assemblers to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 12 real and 64 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio continuous long-read (CLR), PacBio high-fidelity (HiFi), and ONT sequencing to evaluate the assemblers. We include 5 commonly used long-read assemblers in our benchmark: Canu, Flye, Miniasm, Raven, and wtdbg2 for ONT and PacBio CLR reads. For PacBio HiFi reads, we include 5 state-of-the-art HiFi assemblers: HiCanu, Flye, Hifiasm, LJA, and MBG. Evaluation categories address the following metrics: reference-based metrics, assembly statistics, misassembly count, BUSCO completeness, runtime, and RAM usage. Additionally, we investigated the effect of increased read length on the quality of the assemblies and report that read length can, but does not always, positively impact assembly quality. Conclusions: Our benchmark concludes that there is no assembler that performs the best in all the evaluation categories. However, our results show that overall Flye is the best-performing assembler for PacBio CLR and ONT reads, both on real and simulated data. Meanwhile, best-performing PacBio HiFi assemblers are Hifiasm and LJA. Next, the benchmarking using longer reads shows that the increased read length improves assembly quality, but the extent to which that can be achieved depends on the size and complexity of the reference genome.
Journal article
(2023)
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Susan C. Yi, Anne Heijbroek, Luis Cutz, Stephanie Pillay, Wiebren de Jong, Thomas Abeel, Julia Gebert
Application of biochar to landfill cover soils can purportedly improve methane (CH4) oxidation rates, but understanding the combined effects of soil texture, compaction, and biochar on the activity and composition of the methanotrophs is limited. The amendment of wood biochar on two differently textured landfill cover soils at three compaction levels of the Proctor density was explored by analyzing changes in soil physical properties relevant to methane oxidation, the effects on CH4 oxidation rates, and the composition of the methanotrophic community. Loose soils with and without biochar were pre-incubated to equally elevate the CH4 oxidation rates. Hereafter, soils were compacted and re-incubated. Methane oxidation rates, gas diffusivity, water retention characteristics, and pore size distribution were analyzed on the compacted soils. The relative abundance of methanotrophic bacteria (MOB) was determined at the end of both the pre-incubation and incubation tests of the packed samples. Biochar significantly increased porosity at all compaction levels, enhancing diffusion coefficients. Also, a re-distribution in pore sizes was observed. Increased gas diffusivity from low compaction and amendment of biochar, though, did not reflect higher methane oxidation rates due to high diffusive oxygen fluxes over the limited height of the compacted soil specimens. All soils, with and without biochar, were strongly dominated by Type II methanotrophs. In the sandy soil, biochar amendment strongly increased MOB abundance, which could be attributed to a corresponding increase in the relative abundance of Methylocystis species, while no such response was observed in the clayey soil. Compaction did not change the community composition in either soil. Fir-wood biochar addition to landfill cover soils may not always enhance methanotrophic activity and hence reduce fugitive methane emissions, with the effect being soil-specific. However, especially in finer and more compacted soils, biochar amendment can maintain soil diffusivity above a critical level, preventing the collapse of methanotrophy.
...
Application of biochar to landfill cover soils can purportedly improve methane (CH4) oxidation rates, but understanding the combined effects of soil texture, compaction, and biochar on the activity and composition of the methanotrophs is limited. The amendment of wood biochar on two differently textured landfill cover soils at three compaction levels of the Proctor density was explored by analyzing changes in soil physical properties relevant to methane oxidation, the effects on CH4 oxidation rates, and the composition of the methanotrophic community. Loose soils with and without biochar were pre-incubated to equally elevate the CH4 oxidation rates. Hereafter, soils were compacted and re-incubated. Methane oxidation rates, gas diffusivity, water retention characteristics, and pore size distribution were analyzed on the compacted soils. The relative abundance of methanotrophic bacteria (MOB) was determined at the end of both the pre-incubation and incubation tests of the packed samples. Biochar significantly increased porosity at all compaction levels, enhancing diffusion coefficients. Also, a re-distribution in pore sizes was observed. Increased gas diffusivity from low compaction and amendment of biochar, though, did not reflect higher methane oxidation rates due to high diffusive oxygen fluxes over the limited height of the compacted soil specimens. All soils, with and without biochar, were strongly dominated by Type II methanotrophs. In the sandy soil, biochar amendment strongly increased MOB abundance, which could be attributed to a corresponding increase in the relative abundance of Methylocystis species, while no such response was observed in the clayey soil. Compaction did not change the community composition in either soil. Fir-wood biochar addition to landfill cover soils may not always enhance methanotrophic activity and hence reduce fugitive methane emissions, with the effect being soil-specific. However, especially in finer and more compacted soils, biochar amendment can maintain soil diffusivity above a critical level, preventing the collapse of methanotrophy.
The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment – including animals, plants, soil, and water – are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.
...
The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment – including animals, plants, soil, and water – are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.