PSVI-16 Fecal microbiome of nursery pigs fed phytogenics and antibiotics

Abstract The objective of this study was to evaluate effects of feeding two phytogenic products (PHY1 and PHY2; blends of essential oils and plant extracts) in diets with or without antibiotics (AureoMix S 10-10; AB) on fecal microbiome of nursery pigs. A total of 400 nursery pigs (6.8 kg BW; 20 d of age) were fed one of the six dietary treatments (9 pens/treatment), including: control (0% AB; 0% phytogenics), 0.5% AB, phytogenics (0.02% PHY1 or 0.03% PHY2) or the combination of phytogenic and AB (PHY1 x AB or PHY2 x AB). On d 46 postweaning, 48 fecal samples were collected (1 pig/pen; 7–9 pigs/treatment) and were subjected to the analyses of microbial communities by using 16S rRNA V4 amplicon sequencing with Illumina MiSeq. The sequence data were analyzed by using Qiime and the rarefied OTU table was submitted to Calypso to evaluate the alpha and beta diversity, taxonomic classification, and the differential taxa associated to the dietary treatments. There were differences among treatments on alpha diversity, where the control and PHY2 pigs had lower OTU richness (P = 0.05) and chao1 index (P < 0.10) compared to pigs fed AB alone or AB with phytogenics. There were also differences among treatments on microbial beta diversity of pigs (P < 0.01). The most abundant phyla included Firmicute, Bacteroidetes, Actinobacteria, Tenericutes, Proteobacteria, Spirochaetes, and TM7. At family level, pigs fed AB had greater Ruminococcaceae compared to the control, but lower Coriobacteriaceae and Erysipelotrichaceae compared to PHY1 or PHY2 group (P < 0.05). Feature selection by LEfSe indicated that dominant genus associated to AB treatment was Unclassified RF39, while dominant genera associated to PHY2 treatment were Cantenibacterium, unclassified Coriobacteriaceae, Blautia, Eubacterium, and Collinsella. In conclusion, feeding AB and phytogenic products had different impacts on the fecal bacteria of nursery pigs.

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269 Influence of the maternal rumen microbiome on development of the calf meconium and rumen microbiome

Journal of Animal Science ◽

10.1093/jas/skaa278.359 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 195-195

Author(s):

Kelly Woodruff ◽

Gwendolynn Hummel ◽

Kathleen Austin ◽

Travis Smith ◽

Hannah Cunningham

Keyword(s):

Beta Diversity ◽

Management Practices ◽

Sequence Data ◽

Rumen Fluid ◽

Alpha Diversity ◽

Illumina Miseq ◽

Sample Type ◽

Microbial Composition ◽

Alpha And Beta Diversity ◽

Rumen Microbiome

Abstract Optimization of host performance may be achieved through programming of the rumen microbiome. Thus, understanding maternal influences on the development of the calf rumen microbiome is critical. We hypothesized that the cow maternal rumen microbiome would influence colonization of the calf rumen microbiome. Our objective was to relate the microbiome of the cow rumen fluid prior to parturition (RFC) and at weaning (RFCw) to the calf’s meconium microbiome (M) and calf rumen fluid microbiome at birth (RFd1), d 2 (RFd2), d 28 (RFd28), and weaning (RFNw). Multiparous Angus crossbred cows (n = 10) from the University of Wyoming beef herd were used. Rumen fluid was collected from the cows prior to parturition and at weaning. Immediately following parturition, meconium and rumen fluid were collected from the calf. Rumen fluid was collected again at d 2, 28, and at weaning. Microbial DNA was isolated and 16S rRNA sequencing was completed on the Illumina MiSeq. Sequence data were analyzed with QIIME2 to determine both alpha and beta diversity by sample type and day. Alpha diversity metrics reported similarities in the early gut microbiome (M, RFd1, and RFD2; q ≥ 0.12) and between the cow and calf at weaning (q ≥ 0.06). Microbial composition as determined by beta diversity differed in the early rumen microbiome (RFd1, RFd2, and RFd28; q ≤ 0.04). There were similarities in composition between M, RFCw, and RFd1 (q ≥ 0.09). These data can be used to develop hypotheses for the pathway of colonization in the early gut and can provide insight into management practices affecting the microbiome, improving host performance.

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Effect of Bovine MFGM and LF in Infant Formula on Gut Microbiome and Metabolome at Four Months of Age

Current Developments in Nutrition ◽

10.1093/cdn/nzab027 ◽

2021 ◽

Author(s):

Maciej Chichlowski ◽

Nicholas Bokulich ◽

Cheryl L Harris ◽

Jennifer L Wampler ◽

Fei Li ◽

...

Keyword(s):

Infant Formula ◽

Beta Diversity ◽

Milk Fat ◽

Alpha Diversity ◽

Illumina Miseq ◽

Rrna Gene ◽

Term Infants ◽

Linear Discriminant ◽

Alpha And Beta Diversity ◽

Metabolite Profiles

Abstract Background Milk fat globule membrane (MFGM) and lactoferrin (LF) are human milk bioactive components demonstrated to support gastrointestinal (GI) and immune development. Significantly fewer diarrhea and respiratory-associated adverse events through 18 months of age were previously reported in healthy term infants fed a cow's milk-based infant formula with added source of bovine MFGM and bovine LF through 12 months of age. Objectives To compare microbiota and metabolite profiles in a subset of study participants. Methods Stool samples were collected at Baseline (10–14 days of age) and Day 120 (MFGM + LF: 26, Control: 33). Bacterial community profiling was performed via16S rRNA gene sequencing (Illumina MiSeq) and alpha and beta diversity were analyzed (QIIME 2). Differentially abundant taxa were determined using Linear discriminant analysis effect size (LefSE) and visualized (Metacoder). Untargeted stool metabolites were analyzed (HPLC/mass spectroscopy) and expressed as the fold-change between group means (Control: MFGM + LF ratio). Results Alpha diversity increased significantly in both groups from baseline to 4 months. Subtle group differences in beta diversity were demonstrated at 4 months (Jaccard distance; R2 = 0.01, P = 0.042). Specifically, Bacteroides uniformis and Bacteroides plebeius were more abundant in the MFGM + LF group at 4 months. Metabolite profile differences for MFGM + LF vs Control included: lower fecal medium chain fatty acids, deoxycarnitine, and glycochenodeoxycholate, and some higher fecal carbohydrates and steroids (P < 0.05). After applying multiple test correction, the differences in stool metabolomics were not significant. Conclusions Addition of bovine MFGM and LF in infant formula was associated with subtle differences in stool microbiome and metabolome by four months of age, including increased prevalence of Bacteroides species. Stool metabolite profiles may be consistent with altered microbial metabolism. Trial registration: https://clinicaltrials.gov/ct2/show/NCT02274883).

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Prospective evaluation of the fecal microbiome in dogs with large-cell lymphoma receiving chop chemotherapy

10.32469/10355/88084 ◽

2021 ◽

Author(s):

◽

Jason Couto

Keyword(s):

Microbial Diversity ◽

Beta Diversity ◽

Large Cell ◽

Amplicon Sequencing ◽

Healthy Controls ◽

Chop Chemotherapy ◽

Fecal Samples ◽

Fecal Microbiome ◽

Alpha And Beta Diversity ◽

Healthy Dogs

The fecal microbiome composition has been associated with reduced efficacy of cancer therapy and adverse side effects in humans, and chemotherapy has been shown to alter the gut microbiome. The relationship between microbiota and chemotherapy efficacy and tolerability has not been investigated in dogs. We aimed to evaluate changes in fecal microbial diversity during a cycle of CHOP chemotherapy in dogs with lymphoma and whether these changes correlated with adverse events or treatment response. Eighteen dogs with lymphoma were prospectively enrolled, and stool samples were acquired weekly for 6 weeks during CHOP. Fecal samples was analyzed via 16S rRNA amplicon sequencing as previously described. Treatment-associated differences in richness, alpha and beta diversity were determined through comparison to data from healthy controls (n = 26) using factorial ANOVA and PERMANOVA. Dogs with lymphoma had decreased fecal microbial diversity when compared with healthy controls at baseline and throughout treatment (p= 0.0002, 0.0003, 0.0001). Alpha and beta diversity did not significantly change in dogs throughout a cycle of CHOP chemotherapy (p = 0.520 and 0.995). Samples pre-treated with antibiotics were significantly less diverse (alpha and beta diversity) than untreated samples (p = 0.002, 0.0001 respectively). Dogs with lymphoma and fecal samples under the presence of antibiotics had higher levels of Escherchia species in their feces compared to normal dogs. The fecal microbiome of healthy dogs and dogs with lymphoma receiving CHOP is relatively stable over time, but dogs with lymphoma have reduced microbial diversity compared to healthy dogs before and during treatment. An increase in Proteobacteria abundance during treatment may be related to chemotherapy and/or antibiotic use.

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Gut microbiota composition is associated with narcolepsy type 1

Neurology Neuroimmunology & Neuroinflammation ◽

10.1212/nxi.0000000000000896 ◽

2020 ◽

Vol 7 (6) ◽

pp. e896

Author(s):

Alexandre Lecomte ◽

Lucie Barateau ◽

Pedro Pereira ◽

Lars Paulin ◽

Petri Auvinen ◽

...

Keyword(s):

Community Structure ◽

Gut Microbiota ◽

Bacterial Communities ◽

Beta Diversity ◽

Alpha Diversity ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Differential Abundance ◽

Alpha And Beta Diversity

ObjectiveTo test the hypothesis that narcolepsy type 1 (NT1) is related to the gut microbiota, we compared the microbiota bacterial communities of patients with NT1 and control subjects.MethodsThirty-five patients with NT1 (51.43% women, mean age 38.29 ± 19.98 years) and 41 controls (57.14% women, mean age 36.14 ± 12.68 years) were included. Stool samples were collected, and the fecal microbiota bacterial communities were compared between patients and controls using the well-standardized 16S rRNA gene amplicon sequencing approach. We studied alpha and beta diversity and differential abundance analysis between patients and controls, and between subgroups of patients with NT1.ResultsWe found no between-group differences for alpha diversity, but we discovered in NT1 a link with NT1 disease duration. We highlighted differences in the global bacterial community structure as assessed by beta diversity metrics even after adjustments for potential confounders as body mass index (BMI), often increased in NT1. Our results revealed differential abundance of several operational taxonomic units within Bacteroidetes, Bacteroides, and Flavonifractor between patients and controls, but not after adjusting for BMI.ConclusionWe provide evidence of gut microbial community structure alterations in NT1. However, further larger and longitudinal multiomics studies are required to replicate and elucidate the relationship between the gut microbiota, immunity dysregulation and NT1.

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21 Profiling sow vaginal and fecal and piglet gastrointestinal microbiome and the passage of sow microbiome to their progeny from birth to weaning

Journal of Animal Science ◽

10.1093/jas/skz258.039 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 21-21

Author(s):

Huyen Tran ◽

Brenda de Rodas ◽

Manohar M Lahoti ◽

Timothy J Johnson

Keyword(s):

Alpha Diversity ◽

Illumina Miseq ◽

Amplicon Sequencing ◽

Gastrointestinal Microbiome ◽

Core Microbiome ◽

Linear Discriminant ◽

Fecal Microbiome ◽

The Core ◽

Vaginal Swabs ◽

Sampling Points

Abstract The objectives of this study were 1) to profile the sow vaginal and fecal microbiome and the corresponding piglet gastrointestinal microbiome from birth to weaning, and 2) to identify the core microbiome shared between sows and piglets. A total of 226 samples collected from sows (vaginal swabs pre-farrow; fecal samples at farrow, d 3, 7, 10, 17 post-farrow) and their progenies (stomach, ileum, and colon digesta at birth, d 2, and 14 after birth) were used for the analyses of microbial community structure using 16S rRNA V4 amplicon sequencing with Illumina MiSeq. Our data indicated that the piglet and sow microbiome were quite distinct. Piglets had lower bacterial alpha diversity (chao1, richness, Shannon, Simpson indices; P < 0.01) than sows across all timepoints. Beta diversity of piglets by sample types was significantly different (P < 0.001) than sows by sample types when averaged across all timepoints or separation by timepoints. Feature selection by the Linear discriminant analysis effect size (LEfSe) indicated that the genera associated with piglets included those classified as Lactobacillus, unclassified Micrococcaceae, and Rothia when averaged across sampling points and sample types. Genera associated with sows included those classified as Treponema, YRC22, Unclassified RF39, Unclassified Christensenellaceae, Turicibacter, Unclassified RFP12, Unclassified F16, Collinsella, Coprococcus, Unclassified Coriobacteriaceae, and Unclassified Mogibacteriaceae. The genera shared between sow vaginal samples and piglets included those classified as Bacteroides, Fusobacterium, Haemophilus, Prevotella, Veillonella, and unclassified Clostridiadiaceae. The genera shared between sow fecal and piglet samples included those classified as Bacteroides, Lactobacillus, unclassified Clostridiadiaceae, unclassified Ruminococceae, and Prevotella. Overall, there are evidences that bacterial genera were passed from sows to piglets and influenced the microbial communities of piglets later in life.

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APOE Genetics Influence Murine Gut Microbiome

10.21203/rs.3.rs-746611/v1 ◽

2021 ◽

Author(s):

Diana J. Zajac ◽

Stefan J. Green ◽

Lance A. Johnson ◽

Steven Estus

Keyword(s):

Gut Microbiome ◽

Beta Diversity ◽

Univariate Analysis ◽

Microbial Community Composition ◽

Alpha Diversity ◽

Amplicon Sequencing ◽

Linear Discriminant ◽

Fecal Microbiome ◽

Female Mice ◽

The Impact

Abstract Background: Apolipoprotein E (APOE) alleles impact pathogenesis and risk for multiple human diseases, making them primary targets for disease treatment and prevention. Previously, we and others reported an association between APOE alleles and the gut microbiome. Here, we tested whether these results are confirmed by using mice that were maintained under ideal conditions for microbiome analyses. Methods: To model human APOE alleles, this study used APOE targeted replacement (TR) mice on a C57Bl/6 background. To minimize genetic drift, APOE3 mice were crossed to APOE2 or APOE4 mice prior to the study, and the resulting heterozygous progeny crossed further to generate the study mice. To maximize environmental homogeneity, mice with mixed genotypes were housed together and used bedding from the cages was mixed and added back as a portion of new bedding. Fecal samples were obtained from mice at three-, five- and seven-months of age, and microbiota analyzed by 16S ribosomal RNA gene amplicon sequencing. APOE2/E2 and APOE2/E3 mice were categorized as APOE2, APOE3/E4 and APOE4/E4 mice were categorized as APOE4, and APOE3/E3 mice were categorized as APOE3. Linear discriminant analysis of Effect Size (LefSe) identified taxa associated with APOE status, depicted as cladograms to show phylogenetic relatedness. The influence of APOE status was tested onalpha-diversity (Shannon H index) and beta-diversity (principal coordinate analyses and PERMANOVA). Individual taxa associated with APOE status were identified by classical univariate analysis. Whether findings in the APOE mice were replicated in humans was evaluated by using published microbiome genome wide association data. Results: Cladograms revealed robust differences with APOE in male mice and limited differences in female mice. The richness and evenness (alpha-diversity) and microbial community composition (beta-diversity) of the fecal microbiome was robustly associated with APOE status in male but not female mice. Classical univariate analysis revealed individual taxa that were significantly increased or decreased with APOE, illustrating a stepwise APOE2-APOE3-APOE4 pattern of association. The Clostridia class, Clostridiales order, Ruminococacceae family and related genera increased with APOE2 status. The Erysipelotrichia phylogenetic branch increased with APOE4 status, a finding that extended to humans.Conclusions: In this study wherein mice were maintained in an ideal fashion for microbiome studies, gut microbiome profiles were strongly and significantly associated with APOE status in male APOE-TR mice. Erysipelotrichia in particular appears to increase with APOE4 in both mice and humans. Further evaluation of these findings in humans, as well as studies evaluating the impact of the APOE-associated microbiota on disease-relevant phenotypes, will be necessary to determine if alterations in the gut microbiome represents a novel mechanism whereby APOE alleles impact disease.

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The power of microbiome studies: some considerations on which alpha and beta metrics to use and how to report analysis the results

10.21203/rs.3.rs-698991/v1 ◽

2021 ◽

Author(s):

Jannigje Gerdien Kers ◽

Edoardo Saccenti

Keyword(s):

Sample Size ◽

Beta Diversity ◽

Sequence Data ◽

Animal Experiments ◽

Simulated Data ◽

Alpha Diversity ◽

Data Sets ◽

Power Calculations ◽

Alpha And Beta Diversity ◽

Diversity Metrics

Abstract Since sequencing techniques become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observed dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data was used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota data sets and the effect on the sample size. Our data showed that beta diversity metrics are most sensitive to observe differences compared to alpha diversity metrics. The structure of the data influenced which alpha metrics are most sensitive. Regarding beta diversity, the Bray-Curtis metric is in general most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias. We recommend to perform power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: on the basis of this observation, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e. p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish, and we stress here the word publish, a statistical plan before experiments are initiated: this practice is customary for clinical trials where a statistical plan describing the endpoints and the corresponding statistical analyses must be disclosed before the start of the study.

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Inconsistent Patterns of Microbial Diversity and Composition Between Highly Similar Sequencing Protocols: A Case Study With Reef-Building Corals

Frontiers in Microbiology ◽

10.3389/fmicb.2021.740932 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hannah E. Epstein ◽

Alejandra Hernandez-Agreda ◽

Samuel Starko ◽

Julia K. Baum ◽

Rebecca Vega Thurber

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Beta Diversity ◽

Sequence Data ◽

Alpha Diversity ◽

Gene Profiling ◽

Rrna Gene ◽

Sequencing Platform ◽

Alpha And Beta Diversity ◽

Diversity Metrics

16S rRNA gene profiling (amplicon sequencing) is a popular technique for understanding host-associated and environmental microbial communities. Most protocols for sequencing amplicon libraries follow a standardized pipeline that can differ slightly depending on laboratory facility and user. Given that the same variable region of the 16S gene is targeted, it is generally accepted that sequencing output from differing protocols are comparable and this assumption underlies our ability to identify universal patterns in microbial dynamics through meta-analyses. However, discrepant results from a combined 16S rRNA gene dataset prepared by two labs whose protocols differed only in DNA polymerase and sequencing platform led us to scrutinize the outputs and challenge the idea of confidently combining them for standard microbiome analysis. Using technical replicates of reef-building coral samples from two species, Montipora aequituberculata and Porites lobata, we evaluated the consistency of alpha and beta diversity metrics between data resulting from these highly similar protocols. While we found minimal variation in alpha diversity between platform, significant differences were revealed with most beta diversity metrics, dependent on host species. These inconsistencies persisted following removal of low abundance taxa and when comparing across higher taxonomic levels, suggesting that bacterial community differences associated with sequencing protocol are likely to be context dependent and difficult to correct without extensive validation work. The results of this study encourage caution in the statistical comparison and interpretation of studies that combine rRNA gene sequence data from distinct protocols and point to a need for further work identifying mechanistic causes of these observed differences.

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Microbiological description of self-overgrowing spoil heaps and sand quarries in Nordwest Russia

10.5194/egusphere-egu21-2086 ◽

2021 ◽

Author(s):

Aleksei Zverev ◽

Anastasiia Kimeklis ◽

Grigory Gladkov ◽

Arina Kichko ◽

Evgeny Andronov ◽

...

Keyword(s):

Beta Diversity ◽

Alpha Diversity ◽

Illumina Miseq ◽

Differential Abundance ◽

Alpha And Beta Diversity ◽

Technogenic Landscapes ◽

Different Types ◽

Undisturbed Soils ◽

Disturbed Soils ◽

Diversity Metrics

Self-overgrowing recovery of disturbed soils is one of important processes in reclamation of disturbed soils. Different types of anthropogenic disturbances followed by variety of soil types and their genesis leads to different bacterial communities, envolved in reclamation processes. Here we describe regional self-overgrowing soils in two location (Novgorod region, Northwest Russia). We analyse top level of industrial disturbed soils after coil mining (spoil tips with extremely low pH, and overburden soil) and sand quarry dumps followed by local undisturbed soils.We perform 16s amplicone sequencind (v4-region) by Illumina MiSEQ and chemical routine analysis (pH, C, N and other). We provide alpha- and beta-diversity analysis, followed by CCA and analysis of differential abundance of taxa.Sand quarry dumps and regional soils looks common on phyla level, and represent common soil phyla like Proteobacteria, Actinobacteria and Verrucomicrobia. Alpha-diversity metrics aslo are similar, despite difference in beta-diversity. Overburden soil and soil from spot tips, by contrast, is very different even in phylum level. Main intermediants here are Actinobacteria, Chloroflexi &#1080; Nitrospirae. Also they show extremely low alpha-diversity metrics.This work was supported by RSF 17-16-01030, &#171;Dynamics of soil biota in chronoseries of post-technogenic landscapes: analysis of soil-ecological efficiency of ecosystem restoration processes&#187;

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Study on the diversity of epiphytic bacteria on corn and alfalfa using Illumina MiSeq/NovaSeq high-throughput sequencing system

Annals of Microbiology ◽

10.1186/s13213-021-01649-1 ◽

2021 ◽

Vol 71 (1) ◽

Author(s):

Meixiao Wu ◽

Yuehua Wang ◽

Yijing Wang ◽

Xuefei Wang ◽

Ming Yu ◽

...

Keyword(s):

High Throughput ◽

Beta Diversity ◽

High Throughput Sequencing ◽

Raw Materials ◽

Alpha Diversity ◽

Illumina Miseq ◽

Diversity Indices ◽

Epiphytic Bacteria ◽

Alpha And Beta Diversity ◽

Paired End Sequencing

Abstract Purpose To investigate the diversity of the epiphytic bacteria on corn (Zea mays) and alfalfa (Medicago sativa) collected in Hengshui City and Xingtai City, Hebei Province, China, and explore crops suitable for natural silage. Methods The Illumina MiSeq/NovaSeq high-throughput sequencing system was used to conduct paired-end sequencing of the community DNA fragments from the surface of corn and alfalfa collected in Hengshui and Xingtai. QIIME2 and R software were used to sort and calculate the number of sequences and taxonomic units for each sample. Thereafter, the alpha and beta diversity indices at of species level were calculated, and the abundance and distribution of taxa were analyzed and compared between samples. Result At phylum level, the dominant groups were Proteobacteria (70%), Firmicutes (13%), Actinobacteria (9%), and Bacteroidetes (7%). Meanwhile, the dominant genera were Pseudomonas (8%), Acinetobacter (4%), Chryseobacterium (3%), and Hymenobacter (1%). Enterobacteriaceae (24%) were the most predominant bacteria in both the corn and alfalfa samples. Alpha diversity analysis and beta diversity indices revealed that the diversity of epiphytic microbial communities was significantly affected by plant species but not by region. The diversity and richness of the epiphytic bacterial community of alfalfa were significantly higher than those of corn. Conclusion This study contributes to the expanding knowledge on the diversity of epiphytic bacteria in corn and alfalfa silage and provides a basis for the selection of raw materials.

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