C. Peng | TU Delft Repository

Antibiotic growth promoter and phytogenic feed additive consistently alter microbial community structure in chicken cecum

Journal article (2026) - C. Peng, G. delle Grazie, M. Ghanbari, A. May, T. Abeel

BackgroundEfforts to replace antibiotic growth promoters (AGPs) in livestock are often hindered by a limited mechanistic understanding of how sub-therapeutic antibiotic doses enhance animal growth. Since AGP concentrations are typically too low to directly suppress pathogens, their effects on the gut microbiome, particularly its ecological dynamics, warrant closer investigation. A critical but underexplored dimension is how these additives influence the structure and stability of microbial communities as interconnected ecosystems.MethodsWe conducted a comparative network-based analysis to examine the effects of zinc-bactracin, a commonly used AGP, and Digestarom®, an alternative phytogenic feed additive (PFA) on cecal microbiome dynamics in broiler chickens. Using metagenomic data from a repeated cross-sectional randomized controlled trial of 96 broiler chickens assigned to three dietary groups: Basal (Control), AGP and PFA, we constructed microbial co-occurrence networks using Spearman's correlation for birds raised on basal, AGP-, or PFA-supplemented diets at key developmental stages (Day 3, 14, 21, and 35). We assessed changes in network topology, modular organization and node centrality. We evaluated whether the network-prioritized keystone taxa could discriminate among diets using a Random Forest classifier.ResultsCompared to the Control group, both AGP and PFA treatments induced consistent shifts in network topology, including reduced connectivity, increased modularity, increased percentage of positive interactions, enhanced mucosa connectivity, and improved structural robustness over experiment time. Overall, these treatment-induced changes were more pronounced under AGP than under PFA. Despite these changes, we identified conserved subgraphs with stable interconnections across diets and time points during the experiment. The node centrality analysis revealed condition-specific keystone taxa, but Linear Discriminant Analysis (LDA) and Random Forest (RF) struggled to accurately differentiate between diets using their abundance, particularly between PFA and the two other groups.ConclusionOur findings reveal that feed additives can reshape gut microbial dynamics without producing marked compositional shifts. The consistent network-level changes observed for both AGP and PFA highlight the value of ecological network analysis in uncovering microbial community responses. These insights improve our understanding of cecal microbiome responses in chickens, highlight potential modes of action of AGPs, and offer a comparative framework for assessing the microbial impacts of alternative feed additives. ...

BackgroundEfforts to replace antibiotic growth promoters (AGPs) in livestock are often hindered by a limited mechanistic understanding of how sub-therapeutic antibiotic doses enhance animal growth. Since AGP concentrations are typically too low to directly suppress pathogens, their effects on the gut microbiome, particularly its ecological dynamics, warrant closer investigation. A critical but underexplored dimension is how these additives influence the structure and stability of microbial communities as interconnected ecosystems.MethodsWe conducted a comparative network-based analysis to examine the effects of zinc-bactracin, a commonly used AGP, and Digestarom®, an alternative phytogenic feed additive (PFA) on cecal microbiome dynamics in broiler chickens. Using metagenomic data from a repeated cross-sectional randomized controlled trial of 96 broiler chickens assigned to three dietary groups: Basal (Control), AGP and PFA, we constructed microbial co-occurrence networks using Spearman's correlation for birds raised on basal, AGP-, or PFA-supplemented diets at key developmental stages (Day 3, 14, 21, and 35). We assessed changes in network topology, modular organization and node centrality. We evaluated whether the network-prioritized keystone taxa could discriminate among diets using a Random Forest classifier.ResultsCompared to the Control group, both AGP and PFA treatments induced consistent shifts in network topology, including reduced connectivity, increased modularity, increased percentage of positive interactions, enhanced mucosa connectivity, and improved structural robustness over experiment time. Overall, these treatment-induced changes were more pronounced under AGP than under PFA. Despite these changes, we identified conserved subgraphs with stable interconnections across diets and time points during the experiment. The node centrality analysis revealed condition-specific keystone taxa, but Linear Discriminant Analysis (LDA) and Random Forest (RF) struggled to accurately differentiate between diets using their abundance, particularly between PFA and the two other groups.ConclusionOur findings reveal that feed additives can reshape gut microbial dynamics without producing marked compositional shifts. The consistent network-level changes observed for both AGP and PFA highlight the value of ecological network analysis in uncovering microbial community responses. These insights improve our understanding of cecal microbiome responses in chickens, highlight potential modes of action of AGPs, and offer a comparative framework for assessing the microbial impacts of alternative feed additives.

Effects of antibiotic growth promoter and its natural alternative on poultry cecum ecosystem: an integrated analysis of gut microbiota and host expression

Journal article (2024) - C. Peng, Mahdi Ghanbari, Ali May, T.E.P.M.F. Abeel

Background: In-feed antibiotic growth promoters (AGPs) have been a cornerstone in the livestock industry due to their role in enhancing growth and feed efficiency. However, concerns over antibiotic resistance have driven a shift away from AGPs toward natural alternatives. Despite the widespread use, the exact mechanisms of AGPs and alternatives are not fully understood. This necessitates holistic studies that investigate microbiota dynamics, host responses, and the interactions between these elements in the context of AGPs and alternative feed additives. Methods: In this study, we conducted a multifaceted investigation of how Bacitracin, a common AGP, and a natural alternative impact both cecum microbiota and host expression in chickens. In addition to univariate and static differential abundance and expression analyses, we employed multivariate and time-course analyses to study this problem. To reveal host-microbe interactions, we assessed their overall correspondence and identified treatment-specific pairs of species and host expressed genes that showed significant correlations over time. Results: Our analysis revealed that factors such as developmental age substantially impacted the cecum ecosystem more than feed additives. While feed additives significantly altered microbial compositions in the later stages, they did not significantly affect overall host gene expression. The differential expression indicated that with AGP administration, host transmembrane transporters and metallopeptidase activities were upregulated around day 21. Together with the modulated kininogen binding and phenylpyruvate tautomerase activity over time, this likely contributes to the growth-promoting effects of AGPs. The difference in responses between AGP and PFA supplementation suggests that these additives operate through distinct mechanisms. Conclusion: We investigated the impact of a common AGP and its natural alternative on poultry cecum ecosystem through an integrated analysis of both the microbiota and host responses. We found that AGP appears to enhance host nutrient utilization and modulate immune responses. The insights we gained are critical for identifying and developing effective AGP alternatives to advance sustainable livestock farming practices. ...

Background: In-feed antibiotic growth promoters (AGPs) have been a cornerstone in the livestock industry due to their role in enhancing growth and feed efficiency. However, concerns over antibiotic resistance have driven a shift away from AGPs toward natural alternatives. Despite the widespread use, the exact mechanisms of AGPs and alternatives are not fully understood. This necessitates holistic studies that investigate microbiota dynamics, host responses, and the interactions between these elements in the context of AGPs and alternative feed additives. Methods: In this study, we conducted a multifaceted investigation of how Bacitracin, a common AGP, and a natural alternative impact both cecum microbiota and host expression in chickens. In addition to univariate and static differential abundance and expression analyses, we employed multivariate and time-course analyses to study this problem. To reveal host-microbe interactions, we assessed their overall correspondence and identified treatment-specific pairs of species and host expressed genes that showed significant correlations over time. Results: Our analysis revealed that factors such as developmental age substantially impacted the cecum ecosystem more than feed additives. While feed additives significantly altered microbial compositions in the later stages, they did not significantly affect overall host gene expression. The differential expression indicated that with AGP administration, host transmembrane transporters and metallopeptidase activities were upregulated around day 21. Together with the modulated kininogen binding and phenylpyruvate tautomerase activity over time, this likely contributes to the growth-promoting effects of AGPs. The difference in responses between AGP and PFA supplementation suggests that these additives operate through distinct mechanisms. Conclusion: We investigated the impact of a common AGP and its natural alternative on poultry cecum ecosystem through an integrated analysis of both the microbiota and host responses. We found that AGP appears to enhance host nutrient utilization and modulate immune responses. The insights we gained are critical for identifying and developing effective AGP alternatives to advance sustainable livestock farming practices.

Evaluating long-read de novo assembly tools for eukaryotic genomes

Insights and considerations

Journal article (2023) - 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.

Unveiling microbial biomarkers of ruminant methane emission through machine learning

Journal article (2023) - Chengyao Peng, Ali May, Thomas Abeel

BackgroundEnteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants.MethodsIn this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features.ResultsAmong the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions.ConclusionWe demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions. ...

BackgroundEnteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants.MethodsIn this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features.ResultsAmong the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions.ConclusionWe demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.