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The effects of a high-flavonoid corn cultivar on the gastrointestinal tract microbiota in chickens undergoing necrotic enteritis
High-flavonoid corn effects on the gut microbiota
https://orcid.org/0000-0002-4817-9391
Buiatte Vinicius Conceptualization Data curation Formal analysis Investigation Methodology Project administration Validation Visualization Writing – original draft Writing – review & editing 1
https://orcid.org/0000-0003-0159-9177
Proszkowiec-Weglarz Monika Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Resources Supervision Visualization Writing – original draft Writing – review & editing 2
Miska Katarzyna Data curation Formal analysis Funding acquisition Investigation Methodology Resources Writing – review & editing 2
https://orcid.org/0000-0003-1716-2738
Dominguez Dorian Methodology Writing – review & editing 3
Mahmoud Mahmoud Methodology Writing – review & editing 1
https://orcid.org/0000-0003-3059-8560
Lesko Tyler Methodology Writing – review & editing 4
https://orcid.org/0009-0004-0906-8260
Panek Bryan P. Methodology Writing – review & editing 4
Chopra Surinder Conceptualization Funding acquisition Methodology Resources Supervision Writing – review & editing 4
Jenkins Mark Conceptualization Methodology Writing – review & editing 5
Lorenzoni Alberto Gino Conceptualization Funding acquisition Investigation Methodology Project administration Resources Supervision Writing – review & editing 1 *
1 Department of Animal Science, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States of America
2 Animal Biosciences & Biotechnology Laboratory, Beltsville Agricultural Research Center, USDA, ARS, Beltsville, MD, United States of America
3 Veterinary Services, Animal and Plant Health Inspection Service, USDA, Richmond, VA, United States of America
4 Department of Plant Science, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States of America
5 Animal Parasitic Diseases Laboratory, Beltsville Agricultural Research Center, USDA, ARS, Beltsville, MD, United States of America
Kogut Michael H. Editor
USDA-Agricultural Research Service, UNITED STATES OF AMERICA
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: agl20@psu.edu
17 9 2024
2024
19 9 e030733315 3 2024
3 7 2024
https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

The search for alternative therapies to antimicrobial growth promoters (AGP) in poultry production has gained momentum in the past years because of consumer preference and government restrictions on the use of AGP in animal production. Flavonoids are plant-derived metabolites that have been studied for their health-promoting properties that could potentially be used as an alternative to AGP in poultry. In a previous study, we showed that the inclusion of a flavonoid-rich corn cultivar (PennHFD1) in the diet improved the health of broilers undergoing necrotic enteritis. However, the mechanisms of action by which the PennHFD1-based diet ameliorated necrotic enteritis are unknown. This study describes the microbial diversity and composition of the jejunum and ileum of chickens co-infected with Eimeria maxima and Clostridium perfringens and treated with a high-flavonoid corn-based diet. Luminal content and mucosal samples from the jejunum and ileum were collected for DNA extraction, 16S rRNA amplicon sequencing and data analyses. The infection model and the dietary treatments significantly changed the alfa diversity indices (Mucosal samples: ASVs, P = 0.04; Luminal content samples: ASVs, P = 0.03), and beta diversities (Mucosal samples: P < 0.01, Luminal content: P < 0.01) of the ileal samples but not those of the jejunal samples. The microbial composition revealed that birds fed the high-flavonoid corn diet had a lower relative abundance of C. perfringens compared to birds fed the commercial corn diet. The treatments also changed the relative abundance of other bacteria that are related to gut health, such as Lactobacillus. We concluded that both the infection model and the dietary high-flavonoid corn changed the broilers’ gut microbial diversity and composition. In addition, the decrease in the relative abundance of C. perfringens corroborates with a decrease in mortality and intestinal lesions due to necrotic enteritis. Collecting different segments and sample types provided a broader understanding of the changes in the gut microbiota among treatments.

http://dx.doi.org/10.13039/100009791 College of Agricultural Sciences, Pennsylvania State University PEN04613 Chopra Surinder http://dx.doi.org/10.13039/100007917 Agricultural Research Service 80423100010800D https://orcid.org/0000-0003-0159-9177
Proszkowiec-Weglarz Monika This research was partially supported by a Hatch project (PEN04613) to SC and a Seed Grant Award from the College of Agricultural Sciences to GL and SC, and the in-house USDA-ARS CRIS project number 8042-31000-108-00D (MPW and KM). Data AvailabilityThe obtained sequences were deposited in the NCBI Sequence Read Archive (SRA) database (Accession number PRJNA955283).
Data Availability

The obtained sequences were deposited in the NCBI Sequence Read Archive (SRA) database (Accession number PRJNA955283).
==== Body
pmcIntroduction

Concerns with antibiotic resistance in animal production and market demands for poultry meat produced without antimicrobial growth promoters (AGP) have led a portion of the poultry industry to switch to antibiotic-free systems [1, 2]. Antibiotic stewardship in broiler production has greatly improved in the past years, with substantial reductions in the use of antibiotics [3]. However, the discontinued use of antibiotics is associated with an increased incidence of bacterial diseases in chickens, such as avian necrotic enteritis (NE) [1]

NE is an enteric disease caused by toxin-producing strains of Clostridium perfringens, a Gram-positive, spore-forming, and anaerobic bacterium ubiquitous in the gastrointestinal tract (GIT) of animals. NE affects chickens from 2 to 5 weeks of age, negatively impacting growth performance and increasing mortality [4]. It is estimated that NE costs $6 billion annually worldwide because of production losses and prevention measures [5]

Multiple factors predispose chickens to NE, such as Eimeria spp. infections, mycotoxins, dietary non-starch polysaccharides, and high dietary animal protein. These factors can result in increased mucus production, inflammatory responses, and an imbalance of the intestinal microbiota, favoring the multiplication and adhesion of pathogenic C. perfringens strains [6]

A healthy intestinal microbiota contributes to the host’s physiology through several symbiotic mechanisms and outcompetes pathogenic bacteria that could disrupt homeostasis [7]. In chickens, this microbiota has fundamental roles that are important for growth performance, such as digestion and utilization of nutrients, modulation of the immune system, and protection against pathogens [8].

Alternative treatments that can support commensal bacteria and control pathogenic C. perfringens are needed to decrease the impact caused by NE. Phytobiotics are primary and secondary plant metabolites that have been studied as alternative candidates to antibiotics in poultry production [9]. Flavonoids are secondary plant metabolites that have been shown to have several health-promoting effects that support GIT health [10]. In humans, flavonoids have shown the potential to modulate the GIT microbiota, increasing the abundance of beneficial bacteria in the GIT [11]. In broiler chickens, flavonoid extracts can modulate the immune function and increase the abundance of beneficial microbes in the ceca [12].

In our previous study, the inclusion of a proprietary high-flavonoid corn cultivar (PennHFD1, Penn State University, USA) in the diets of broiler chickens coinfected with E. maxima and C. perfringens reduced the severity of necrotic enteritis. Chickens that received a diet with 31.5% of PennHFD1 had nearly 43% less mortality, 52% lower incidence of intestinal lesions, higher body weight gain, and lower feed conversion ratio (FCR) compared to chickens fed a diet formulated with a commercial corn variety [13]. However, the mechanisms of action by which the specialty corn ameliorated NE are unknown.

In this study, we hypothesized that the inclusion of PennHFD1 in the diets of the broiler chickens modulated the intestinal microbiota of chickens undergoing NE. The objectives of this study were to analyze the intestinal microbiota composition and diversity of chickens fed a PennHFD1-based diet and compare them to those of chickens fed a commercial corn-based diet.

Materials and methods

Experimental design

The experiment was conducted at the Poultry Education and Research Center, Penn State (University Park, PA), and has been previously published in Buiatte et al. (2022). All animal procedures were previously approved by the Institutional Animal Care and Use Committee (IACUC) at The Pennsylvania State University (n. PROTO202001566). A total of 400 day-old straight-run broiler chickens (Ross 308, Aviagen) were randomly divided into 20 floor pens located in two identical temperature-controlled rooms to receive one of the following treatments: Uninfected birds fed a commercial corn-based diet (CTL A); Uninfected birds fed the PennHFD1-based diet (CTL B); Birds co-infected with E. maxima and C. perfringens, fed a commercial corn-based diet (INF A); Birds co-infected with E. maxima and C. perfringens, fed the PennHFD1-based diet (INF B). Treatments were assigned to floor pens using a completely randomized design with five replicates per treatment. Birds were reared for 21 days with feed and water provided ad libitum for the entire experiment. The treatment diets were formulated as previously described [13], to meet or exceed the National Research Council (1994). Corn represented 31.5% of the diet, and corn type (commercial corn vs. PennHFD1 corn) was the only difference between the diets. Birds were monitored twice a day throughout the experiment. The flavylium ion concentration was 1.98 absorbance/g for the PennHFD1 corn and 0.17 absorbance/g for the commercial corn cultivar. Proximate analyses of both corn lines were performed and nutrient composition was similar among the corn lines [13].

Experimental model of necrotic enteritis

NE was induced by using a modified model previously described [14]. Briefly, birds were fed diets containing ingredients known to be predisposing factors for NE, such as wheat and fishmeal [15, 16]. At 13 days of age, chickens from the treatments INF A and INF B were infected with 5,000 oocysts of E. maxima via oral gavage. On days 18 and 19, the feed from the treatments INF A and INF B was inoculated with 1 mL of 1 x 109 CFU of C. perfringens (2 NetB-positive strains, 1 NetB-negative strain) per bird. Feeders from all treatments were removed for 12 hours prior to the first inoculation of C. perfringens. On d 21, birds (5 birds/pen) were euthanized by cervical dislocation and necropsied for lesion identification and scoring (Data previously reported in Buiatte et al., 2022). All animals were euthanized at the end of the experiment by cervical dislocation.

Sample collection

A subset of chickens was randomly selected for tissue sampling (n = ~5 birds/treatment). From each bird, four samples were collected for microbial composition and diversity analyses: jejunal luminal content (JLC); jejunal mucosa (JM), ileal luminal content (ILC), and ileal mucosa (IM). Jejunal samples were collected 10 cm proximal to the Meckel’s diverticulum and ileal samples were collected 20 cm distal to the Meckel’s diverticulum. For luminal content samples, the segment of the intestine was excised with a sterile scalpel blade, and content was collected directly into the cryogenic vials by applying manual pressure to the intestinal serosa. After the removal of intestinal content, the same intestinal segment was longitudinally opened to expose the mucosa. The remaining intestinal content was rinsed with sterile PBS, and the mucosa was scraped with a microscope slide (previously cleaned with 70% ethanol and DNA AWAY™, MBP, Inc., San Diego, CA) and stored in cryogenic tubes. All samples were immediately frozen in liquid nitrogen and transferred to a -80°C freezer.

16S ribosomal RNA gene amplicon sequencing

DNA was extracted from the samples using the DNeasy PowerSoil kit (Qiagen, Valencia, CA, USA) according to manufacturer’s instructions. DNA quantity was assessed with NanoDrop (ThermoFisher Scientific, Inc. Waltham, MA, USA), and DNA quality was evaluated with TapeStation System (Agilent Technologies, Santa Clara, CA, USA). High-throughput sequencing of the hypervariable region V3-V4 of the 16S ribosomal RNA gene was conducted with Illumina workflow and consumables (Illumina, Inc., San Diego, CA, USA). The primers used for amplification of the target region were: Forward: 5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3’ and Reverse: 5’ GTCTCGTGGGCTCGGAGA TGTGTATAAGAGACAGGACTACHV GGGTATCTA ATCC-3’.

Amplification by PCR was followed by amplicon cleaning and indexing as previously described [17]. The concentration and quality of the amplicons were determined using QIAxcel DNA Hi-Resolution cartridge, proprietary QIAxcel ScreenGel software (version 1.6.0, www.qiagen.com), and QIAxcel Advanced System (Qiagen) following the manufacturer’s instructions. The pooled DNA library (4nM) and PhiX control v3 (Illumina, Inc., 4nM) were denatured with 0.2 N NaOH (Sigma-Aldrich, Corp., St. Louis, MO, USA) and diluted to a final concentration of 4 pM. The library was mixed with PhiX control (20% v/v) and pair-ended 300 x 300 bp with Illumina MiSeq platform and MiSeq Reagent Kit v3 (Illumina, Inc). The obtained sequences were deposited in the NCBI Sequence Read Archive (SRA) database (Accession number PRJNA955283).

Data analyses

Quality control and analysis of sequence reads were performed with Quantitative Insight Into Microbial Ecology (QIIME) software package 2 (version 2021.4.0, http://qiime2.org) [18]. Demultiplexing, filtering and dereplication of raw fastq files were performed with q2-dada2 [19]. Sequences with an average Phred score lower than 25 were removed from the dataset. MAFFT was used for multiple sequence alignment [20] and phylogenetic trees were generated with Fastree [21]. Taxonomy was assigned to amplicon sequence variants (ASVs) with DADA2 via the q2-feature-classifier classify-sklearn naïve Bayes taxonomy classifier [22] using the Greengenes database (v. 13_8; http://greengenes.secongenome.com).

Alpha diversity indices (ASVs, Shannon’s diversity index, Pielou’s Evenness, Faith’s Phylogenetic Diversity) were obtained through QIIME2 package. Alpha diversity metrics were used to measure species richness and/or evenness within one sample, and the non-parametric Kruskal-Wallis test was used to analyze differences in alpha diversity between treatment groups. Analysis of beta diversity was performed in QIIME2 employing unweighted UniFrac. To test for significance in unweighted UniFrac distances, the non-parametric permutational analysis of variance (PERMANOVA) test was used. Principal coordinate analysis (PCoA) was used to visualize distances between treatment groups as well as to visualize clustering of samples (QIIME2) [23]. Linear Discriminant Analysis (LDA) Effect Size (LEfSe) algorithm [24] was used to identify taxa with significant differential abundance between treatment groups. Taxonomic composition and LEfSe graphs were generated using ggplot2 in R Studio (R Core Team, 2020).

Results

Sequencing data

A total of 11,655,786 raw sequences were generated from all samples (n = 88). After quality trimming, 3,138,150 sequence reads were obtained. The total number of raw and filtered reads and the mean reads per sample for each bacterial population are shown in Table 1.

10.1371/journal.pone.0307333.t001 Table 1 Sequencing data from bacterial populations from luminal content (LC) and mucosa (M) of the jejunum (J) and ileum (I) in broiler chickens.

	Sequence reads / sample type	
	JLC1	JM1	ILC2	ILM2	
Raw reads					
Total	1,344,406	1,936,943	4,036,054	4,338,383	
Mean	64,019	92,235	175,480	188,625	
Reads after quality trimming					
Total	221,097	228,090	1,489,820	1,199,143	
Mean	10,528	10,861	64,774	52,136	
Sequencing depth3 for analysis	2,600	1,699	12,133	2,701	
1 n = 21

2 n = 23.

JLC (Jejunum luminal content); JM (Jejunal mucosa); ILC (Ileum luminal content); IM (Ileal mucosa).

Rarefaction curves were used to determine the sequencing depth for alpha and beta diversity analysis.

Alpha diversity

The effects of treatments on alpha diversity indices are shown in S1 Table. Diet (Commercial corn or PennHFD1 diet) and infection (uninfected or coinfection with E. maxima and C. perfringens) did not affect the alpha diversity in jejunal samples (JLC and JM, P > 0.05; S1 Table). In contrast, alpha diversity was significantly affected in ileal samples (ILC and ILM). The interaction of diet (commercial corn and PennHFD1 corn) and infection (uninfected and infected) significantly affected the ASVs of ILC samples (P = 0.039, S1 Table). Samples from infected birds fed a commercial corn diet (INF A) had fewer ASVs compared to CTL A and CTL B, but not different from INF B (Fig 1). In the IM samples, infected chickens fed the commercial corn diet (INF A) had lower values of ASVs (P = 0.045, Fig 2), less Richness (P = 0.005, Fig 2), and lower Shannon index (P = 0.038, Fig 2) in comparison to CTL A birds (Fig 2).

10.1371/journal.pone.0307333.g001 Fig 1 The effect of infection (control and infected) and diet (commercial corn and PennHFD1 corn) on amplicon sequencing variants (ASVs) of bacterial populations from ileal luminal content (ILC) in broiler chickens.

CTL A (Control birds fed the commercial corn diet); CTL B (Control birds fed the PennHFD1 diet); INF A (Birds co-infected with E. maxima and C. perfringens fed the commercial corn diet); INF B (Birds co-infected with E. maxima and C. perfringens fed the PennHFD1 diet).

10.1371/journal.pone.0307333.g002 Fig 2 The effect of infection (control and infected) and diet (commercial corn and PennHFD1 corn) on amplicon sequencing variants (ASVs), Richness and Shannon indices of bacterial populations from ileal mucosa (IM) in broiler chickens.

CTL A (Control birds fed the commercial corn diet); CTL B (Control birds fed the PennHFD1 diet); INF A (Birds co-infected with E. maxima and C. perfringens fed the commercial corn diet); INF B (Birds co-infected with E. maxima and C. perfringens fed the PennHFD1 diet).

Beta diversity

PERMANOVA analysis based on the unweighted UniFrac distances showed that treatments did not affect the beta diversity of microbial communities in jejunal samples (JLC and JM) (P > 0.05, data not shown), but a trending separation between diets (commercial corn diet or PennHFD1 diet) was observed in JLC samples (PERMANOVA, P = 0.055). The interaction between diet (commercial corn diet and PennHFD1 diet) and infection (control and infected) significantly affected the beta diversity of ileal luminal content (PERMANOVA, P = 0.002) and ileal mucosa (PERMANOVA, P = 0.003) samples (data not shown).

The principal coordinates analysis (PCoA) of microbial communities from JLC samples showed a trending clustering of treatments that received the PennHFD1 diet (CTL B and INF B) (Fig 3A). In JM samples, there were no apparent clusters among treatments (Fig 3B). The PCoA of bacterial communities in ILC samples did not show apparent clusters (Fig 3C). In IM samples, the CTL A samples clustered separately from the treatments CTL B, INF A, and INF B (Fig 3D).

10.1371/journal.pone.0307333.g003 Fig 3 Principal Coordinates Analysis (PCoA) based on unweighted UniFrac distances of the microbial communities found in ileal samples (ILC and IM) and jejunal samples (JLC and JM) collected from 21-day-old chickens coinfected with E. maxima and C. perfringens.

CTL (Control); INF (Infected); A (Commercial corn diet); B (PennHFD1 diet). Control: Non-infected chickens; Infected: Chickens coinfected with E. maxima and C. perfringens.

Microbial composition and differential relative abundance

The relative abundances of bacteria at the genus and species levels identified from jejunal and ileal samples are shown in Figs 4 and 5, and S2 Table.

10.1371/journal.pone.0307333.g004 Fig 4 Relatively abundant bacteria (%) identified in jejunal samples (JLC and JM) collected from chickens fed a commercial corn line-based diet or a high-flavonoid corn-based diet (PennHFD1), infected or uninfected (control) with E. maxima and C. perfringens.

JLC = jejunal luminal content; JM = Jejunal mucosa; Feed A = commercial corn line-based diet; Feed B = PennHFD1 (high-flavonoid)-based diet; Control (uninfected chickens); Infected (chickens co-infected with E. maxima and C. perfringens). UNCL = Unclassified bacteria reads; LAR = Low abundance reads.

10.1371/journal.pone.0307333.g005 Fig 5 Relatively abundant bacteria (%) identified in ileal samples (ILC and IM) collected from chickens fed a commercial corn line-based diet or a high-flavonoid corn-based diet (PennHFD1), infected or uninfected (control) with E. maxima and C. perfringens.

ILC = Ileal luminal content; IM = Ileal mucosa; Feed A = commercial corn line-based diet; Feed B = PennHFD1 (high-flavonoid)-based diet; Control (uninfected chickens); Infected (chickens co-infected with E. maxima and C. perfringens). UNCL = Unclassified bacteria reads; LAR = Low abundance reads.

Jejunal samples

Lactobacillus and Clostridium were the most relatively abundant genera in JLC samples (Fig 4A, S2 Table), from which Clostridium represented 18.76% of the genera identified in the treatment INF A and 3.32% of the genera identified in the treatment INF B. Regardless of the infection status, birds fed PennHFD1 showed a lower relative abundance of C. perfringens (CTL B, 0.79%; INF B, 3.26%) than chickens fed the commercial corn (CTL A, 2.40%; INF A, 18.76%; Fig 4B). From the Lactobacillus genera, Lactobacillus salivarius, and Lactobacillus reuteri were the most abundant species in JLC samples (Fig 4B). Lower abundance reads (LAR) and unclassified bacterial reads (UNCL) were less relatively abundant in infected treatments compared to uninfected treatments at the genus and species levels (Fig 4A and 4B).

More genera were identified in JM samples than in JLC samples, which included Lactobacillus, Clostridium, Bacteroides, and Escherichia (Fig 4C). Infected chickens obtained a higher relative abundance of Escherichia than uninfected chickens. LAR and UNCL were less relatively abundant in infected treatments than in uninfected treatments in JM samples (Fig 4C), but more relatively abundant in JM samples than JLC samples. At the species level, chickens fed PennHFD1 showed a lower relatve abundance of C. perfringens (CTL B = 0%, INF B = 0.40%; Fig 4D) than birds fed the commercial corn diet (CTL A = 1.95%; INF A = 12.27%; Fig 4D). E. coli and L. reuteri showed higher relative abundance in the infected birds than in the uninfected birds (Fig 4D).

Ileal samples

Lactobacillus was the most relatively abundant genera in ILC samples among all treatments (Fig 5A), and higher relative abundances were identified in chickens fed the commercial corn than in chickens fed PennHFD1. Clostridium and Escherichia were more relatively abundant in infected chickens compared to uninfected chickens, and at the species level, C. perfringens and E. coli were identified as the representative relatively abundant species (Fig 5B). Birds fed PennHFD1 showed a slightly lower relative abundance of C. perfringens than birds fed commercial corn.

In IM samples, Lactobacillus was more relatively abundant in infected treatments than in uninfected treatments. At the genus level, LAR accounted for more than 40% of the reads obtained in the uninfected treatments (Fig 5C) compared to less than 10% in the infected treatments. Clostridium was less relatively abundant in chickens fed PennHFD1 (CTL B = 1.04%; INF B = 20.79%) than in chickens fed the commercial corn (CTL A = 2.55%; INF A = 26.72%) (Fig 5C). At the species level, C. perfringens was more relatively abundant in chickens fed the commercial corn (CTL A = 2.53%; INF A = 26.72%) than in chickens fed PennHFD1 (CTL B = 0.96%; INF B = 20.79%; Fig 5D). Lactobacillus salivarius and Bacteroides fragilis were more relatively abundant in the uninfected treatments than in the infected treatments (Fig 5D and S2 Table), whereas Lactobacillus reuteri was more relatively abundant in the infected than in the uninfected treatments.

Differential relative abundance

LDA effect size (LEfSe) identified the differentially abundant taxa when comparing the two diets (commercial corn and PennHFD1), regardless of the infection status. In JLC samples, differentially abundant taxa were identified in chickens fed PennHFD1, from which Clostridium was the most differentially relatively abundant genus (Fig 6). Clostridium was differentially abundant in JM samples from chickens fed the commercial corn diet compared to the PennHFD1 diet. At the family level, Clostridiaceae, Enterobacteriaceae, and Oxalobacteraceae were identified as differentially abundant. At the genus level, besides Clostridium, Ralstonia was also differentially abundant.

10.1371/journal.pone.0307333.g006 Fig 6 Effect of diet (A and B) on differentially abundant taxa in jejunal samples (JLC and JM) obtained by Linear Discriminant Analysis (LDA) effect size (LEfSe). Commercial corn = Commercial corn-based diet; PennHFD1 = PennHFD1-based diet; ILC = Ileal luminal content samples; IM = Ileal mucosa samples.

In ileal samples, differentially abundant taxa were identified in chickens fed PennHFD1 (Fig 7). In ILC samples, the family Turicibacteraceae was the most differentially abundant taxa, whereas, in IM samples, the genus Streptococcus was identified as the most differentially abundant bacteria.

10.1371/journal.pone.0307333.g007 Fig 7 Effect of diet (A and B) on the differentially abundant taxa in ileum samples (ILC and IM) obtained by Linear Discriminant Analysis (LDA) effect size (LEfSe). Commercial corn = Commercial corn-based diet; PennHFD1 = PennHFD1-based diet; ILC = Ileal luminal content samples; IM = Ileal mucosa samples.

Discussion

Necrotic enteritis and dysbacteriosis are commonly associated because of a multitude of factors that can affect microbial balance in the intestines and the multiplication of C. perfringens [8]. Dysbacteriosis is commonly reported in chickens undergoing NE, and it has been shown to be an important consequence of the disease, and not necessarily a pre-existing condition [25]. Displacement of some bacteria, such as lactic acid-producing bacteria and butyrate producers is commonly reported in NE studies that use predisposing factors alongside inoculation of C. perfringens [26].

Many studies have shown that NE challenges can differentially impact the microbiota. For instance, some NE models reduce the abundance of Lactobacillus in the small intestines, which can be interpreted as an important finding of the disease because of the role of Lactobacillus in GIT health and function [27, 28]. In our experiment, birds co-infected with E. maxima and C. perfringens showed a slightly higher relative abundance of Lactobacillus compared to uninfected chickens. Bortoluzzi et al. (2019) [29] reported an increase in Lactobacillus abundance in the ileum of birds challenged with a model of NE that used a coccidia vaccine and reused litter. A study that characterized the cecal microbiota of chickens under four NE models, obtained an increased relative abundance of some Lactobacilli species in birds that received Eimeria as part of the NE model, which was correlated with an increased concentration of short-chain fatty acids (SCFAs) in the GIT [30]. These differential results highlight the variability of the GIT microbiota in response to different NE challenges.

Polyphenolic compounds, such as flavonoids, can be metabolized by intestinal bacteria. Studies have shown that some polyphenols can modulate the GIT microbiome by promoting the multiplication of beneficial bacteria and suppressing pathogens [31]. In our previous experiment, a flavonoid-rich corn (PennHFD1) diet ameliorated the impacts caused by NE in broiler chickens [13]. In this study, the effects of the PennHFD1-based diet on the intestinal microbiota of those chickens were evaluated.

The treatments (infection and diet) did not change the alfa diversity of jejunal samples but significantly changed those of ileal samples, possibly because ileal samples had a richer microbial composition. For instance, in JLC samples, the most abundant genera were Lactobacillus and Clostridium, whereas in ILC samples, the most relatively abundant genera were Lactobacillus, Clostridium, Enterococcus, Escherichia, Candidatus Arthomitus and SMB53. Therefore, a greater number of genera could have been susceptible to effects caused by the infection model that involves E. maxima and C. perfringens, and the dietary components, leading to changes in the alfa diversity indices.

The corn type and infection status had a confounding effect on the beta diversity of ileal samples. Dietary components and pathogens are among the most important factors that can drive differences in the GIT microbiota [31]. A trending difference in the unweighted UniFrac distances between the diets (PennHFD1 and commercial corn) was observed in JLC samples using PERMANOVA and PCoA, which could have been significant with a larger sample size.

Based on the taxonomic classification, Lactobacillus was the most relatively abundant genus in both jejunal and ileal samples. Luminal samples (JLC and ILC) had a higher relative abundance of Lactobacillus than mucosal samples (JM and IM). In fact, Lactobacillus is among the most abundant bacteria found in the small intestines of chickens [32, 33]. Lactobacillus is commonly associated with healthy microbiota, and it is included in several probiotic products designed to improve growth performance in chickens [34]. In their symbiotic relationship with the host, Lactobacilli produce SCFA and lactic acid from nutrients released during digestion. The production of these metabolites leads to a decrease in the luminal pH, which boosts the multiplication of Lactobacilli [34–36] and may inhibit pathogenic bacteria, such as C. perfringens. Previous studies show the antagonistic effect of Lactobacillus against C. perfringens through the production of bacteriocins and competitive colonization of the intestines [37]. As previously mentioned, the relative abundance of Lactobacillus was higher in infected chickens than in uninfected chickens, and no consistent effect of the diets on Lactobacillus was observed.

In all sample types, C. perfringens was less relatively abundant in chickens fed the high-flavonoid corn diet than in chickens fed the commercial corn diet, regardless of infection status. In a murine study, animals fed a diet with a flavonoid-rich ingredient (Rubus occidentalis) had less Clostridium spp. in the colon luminal content compared to those fed the control diet [38]. Some classes of flavonoids, such as flavan-3-ols, have shown antimicrobial activity against C. perfringens in vitro [39]. Interestingly, the genus Clostridium was identified as more differentially relatively abundant (LEfSe; Fig 6) in the luminal samples of the jejunum (JLC) from chickens fed the PennHFD1 diet compared to chickens fed the commercial corn, regardless of the infection status. Conversely, in mucosal samples (JM), Clostridium was identified as more differentially abundant in chickens fed the commercial corn diet (LEfSe, Fig 6). The formation of NE lesions occurs when C. perfringens attaches to the enterocytes and secretes proteolytic toxins [40]. It has been shown that after the formation of lesions, C. perfringens lines up at the submucosa forming a biofilm-like structure [4]. Therefore, mucosal samples may be more appropriate to evaluate C. perfringens infection than luminal content samples with high-throughput sequencing techniques.

Although Clostridium comprises many species that are not related to necrotic enteritis, reports show that inoculation of chickens with pathogenic strains of C. perfringens leads to a dramatic displacement of the native Clostridia in the intestine [25]. In our experiment, chickens were infected with a mixture of toxin-producing strains, isolated from field cases of NE. Although C. perfringens was also identified as relatively abundant in uninfected treatments, no intestinal lesions were detected in the sampled birds [13]. Therefore, in the infected treatments, C. perfringens may have more effectively colonized the intestinal mucosa of chickens fed commercial corn than of chickens fed PennHFD1. This is associated with the incidence of intestinal lesions; chickens fed the PennHFD1 diet had a 52% lower incidence of intestinal lesions compared to chickens fed the commercial corn diet [13].

It should be noted that PennHFD1 may have interfered with several factors intrinsic to our experimental model, that could have influenced the gut microbiota, such as E. maxima infection and replication, the intestinal immune response, as well as other physiological processes. It has been shown that supplementation of flavonoids in broiler diets can modulate cellular and humoral immune responses, and lipid metabolism, and promote anti-oxidant effects [41]. Moritz et al. (2022) [42] showed that birds fed a sorghum-based diet containing several flavonoid compounds were less impacted by NE, which was associated with the upregulation of genes involved in the immune response to bacterial infections; however, the authors did not evaluate possible effects against E. maxima. In a previous study of our research group involving a different experimental design, we observed that the inclusion of PennHFD1 in a corn-soy based diet did not interfere with E. maxima oocyst shedding [43]. The complexity of the host-microbiota crosstalk and the onset of NE should be considered when interpreting the beneficial effects of feed additives and functional feedstuffs in experimental infections.

Among other taxa reported in this study, Candidatus Arthromitus was displaced in the ileal samples of birds co-infected with E. maxima and C. perfringens. These are segmented filamentous (SFB) commensal to the intestinal mucosa. They play a key role in the innate immune system, inducing Th17 lymphocytes to produce IL-17, a proinflammatory cytokine that increases during E. maxima infections [44]. In our study, C. Arthromitus was only identified in uninfected chickens. A similar finding was observed in a previous study that used three broiler breeds infected with E. maxima and C. perfringens, in which C. Arthromitus was only identified in uninfected Ross chickens. The authors suggested that this could be related to a possible difference in the resistance to NE among breeds [45]. However, data from other studies suggest that factors that interfere with the GIT lining integrity, such as mycotoxins and Eimeria infections, may negatively affect C. Arthromitus which could interfere with immunity development [26].

Escherichia is among the most abundant genera found in the intestines of chickens. Studies using polyphenolic compounds have found various effects on Escherichia [31]. In our study, Escherichia was more abundant in infected chickens than in uninfected chickens. In ileal samples, chickens fed the high-flavonoid corn showed a higher abundance of Escherichia than chickens fed the commercial corn. High Escherichia counts have been correlated with a low FCR in chickens [46]. In the current study, infected chickens fed high-flavonoid corn had lower FCR compared to infected birds fed a commercial corn diet [13].

The diversity and composition of the GIT microbiota change substantially throughout different segments of the chicken GIT [47]. Moreover, studies have shown remarkable differences between the microbiota of luminal content samples and mucosal samples [48, 49]. In jejunal samples, Escherichia and Bacteroides were only identified as relatively abundant in mucosal samples and not in luminal content samples, and several taxa were differentially abundant in JM samples compared to JLC samples (Fig 6). In the ileum, Candidatus Arthromitus was only identified in the luminal content samples and not in the mucosal samples. In ILC samples, Turicibacter was significantly influenced by the diet, whereas in IM samples, the dietary treatment significantly affected Streptococcus. The collection of different intestinal segments and sample types provided a broader picture of the effect of the treatments on the GIT microbiota, which can help researchers draw more educated comparisons between studies.

Based on the microbial composition data obtained in this study and the effects reported in our previous publication [13], we speculate that the high-flavonoid corn diet modulated the microbiota in the small intestines by decreasing the population of pathogenic C. perfringens and increasing beneficial bacteria, which may have prevented epithelial adhesion and multiplication of toxin-producing C. perfringens. However, further investigation is needed to elucidate the effects of PennHFD1 on E. maxima infection, the immune response, as well as on the dietary conditions that alter the host’s physiology to better understand how functional feedstuffs improve health in experimental infection models.

In conclusion, the co-infection of E. maxima and C. perfringens and the dietary high-flavonoid corn (PennHFD1) were associated with alterations in the GIT microbial diversity and composition of broiler chickens. C. perfringens was less differentially abundant in jejunal mucosal samples, and this was associated with a lower incidence of NE lesions. This study also supported previous knowledge that collecting different gastrointestinal tract segments and different sample types provides a more comprehensive understanding of the microbiota changes among treatments.

Supporting information

S1 Table Effect of the main effects (feed and infection) and interactions on alpha diversity indices in the jejunal (JLC and JM) and ileal samples (ILC and IM) collected from 21 day-old chickens coinfected with E. maxima and C. perfringens.

ASV = amplicon sequence variant; Feed = Feed A (commercial corn line-based diet) or Feed B (PennHFD1-based diet); Infection = Co-infection with E. maxima and C. perfringens or control (not infected).

(DOCX)

S2 Table Mean relative abundance (%) of taxonomic groups identified as relatively abundant at the genus and species level in jejunal (JLC and JM) and ileal (ILC and IM) samples collected from infected (co-infection with E. maxima and C. perfringens) and control (non-infected) chickens fed a commercial corn-based diet (A) or a high-flavonoid corn-based diet (B). CTL A (Non-infected chickens fed a commercial corn-based diet); CTL B (Non-infected chickens fed a PennHFD1-based diet); INF A (Chickens co-infected with E. maxima and C. perfringens fed a commercial corn-based diet); INF B (Chickens co-infected with E. maxima and C. perfringens fed a PennHFD1-based diet). * Statistical difference (ANOVA, P ≤ 0.05).

(DOCX)

The authors wish to thank Ms. Lori Schreier and Ms. Beverly Russell for laboratory support. Mention of trade name, proprietary product, or specific equipment does not constitute guarantee or warranty by USDA and does not imply its approval to the exclusion of other suitable products.

10.1371/journal.pone.0307333.r001
Decision Letter 0
Kogut Michael H. Academic Editor
© 2024 Michael H. Kogut
2024
Michael H. Kogut
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
18 Apr 2024

PONE-D-24-09821The effects of a high-flavonoid corn cultivar on the gastrointestinal tract microbiota in chickens undergoing necrotic enteritisPLOS ONE

Dear Dr. Lorenzoni,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Both reviewers remarked on the small sample sizes used here which led to both questioning the relevance and depth of the Discussion. I would also expect that the authors direct their revisions specifically to the following comments by the reviewers: Please address the potential direct effects of the fed additive on the coccidial infection and the outcome of the experiments.  Further, both reviewers suggest a more detailed Discussion and an explanation concerning correlation of the microbiota data found here versus causation!

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Additional Editor Comments (if provided):

Reviewer #1: The results are interesting but there is a need to expand the discussion besides what they were expecting regarding the microbiota modulation. I see an important limitation in the NE challenge model regarding the interpretation of "mechanism of action". Coccidia is also being affected by the flavonoids, and it is at least the 50% of the changes that they are introducing there!

The study is a based in a challenge model that uses infection with coccidia as a predisposing factor for the development of C. perfringens necrotic enteritis. Although this is a very common NE challenge model used in several studies, and also imposed to the industry as almost a gold standar, the inclusion of feed additives or diet to compare treatments against a non-challenged control must be carefully interpreted. Many of these additives (or diet components) can alter the infection of coccidia (either in the level as well as the cycle (i.e. shortening or extending)) by direct interaction with the parasite or the physiology of the host. Particularly flavonoids are able to inhibit the invasion and replication of different species of coccidian and also alter epithelial physiology and immune responses of the gut. As C. perfringens challenge is synchronized at a fix time after coccidian delivery, if cycling is lightly altered, it can influence how C. perfringens induce lesions. I would suggest to include this discussion in the paper, limiting the expected outcomes of the results obtained.

In lines 481-483 the authors state that the results "indicate" that microbiota modulation is an important mechanism of action of high-flavonoid corn in the observed reduction of necrotic enteritis. However, although the results show a modulation of the microbiota, they are not strong enough, due to the experimental design and the co-infection challenge model used, to "indicate" that it is a mechanism of action. The results do show that there is a change in the microbiota and that these changes are associated with a decrease in the number (?) of animals with lesions compatible with necrotic enteritis (I understand that there would be no differences in the degree of lesion, the authors refer to a previous work that is supposed to continue in this one) but there is no direct relationship. These results could suggest a potential role of the microbiota in the observed effects. However, if flavonoids are altering for example intestinal transit, water homeostasis, mucus secretion or antimicrobial peptides secretion, then a microbiota modulation would be also observed. Please consider the potential changes in animal physiology as part of the mechanisms of action of the high-flavonoid corn on the observed microbiota changes.

Reviewer #2:

1. The number of replicates is very small to make any logical conclusions.

2. provide the flavainoid content of the corn.

3. provide the basic nutritive value of the corn under study.

[Note: HTML markup is below. Please do not edit.]

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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**********

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Reviewer #1: The manuscript “The effects of a high-flavonoid corn cultivar on the gastrointestinal tract microbiota in chickens undergoing necrotic enteritis” show results that are interesting and useful to understand the evolution of necrotic enteritis and how this type of corn can induce changes in the animal. The study is a based in a challenge model that uses infection with coccidia as a predisposing factor for the development of C. perfringens necrotic enteritis. Although this is a very common NE challenge model used in several studies, and also imposed to the industry as almost a gold standar, the inclusion of feed additives or diet to compare treatments against a non-challenged control must be carefully interpreted. Many of these additives (or diet components) can alter the infection of coccidia (either in the level as well as the cycle (i.e. shortening or extending)) by direct interaction with the parasite or the physiology of the host. Particularly flavonoids are able to inhibit the invasion and replication of different species of coccidian and also alter epithelial physiology and immune responses of the gut. As C. perfringens challenge is synchronized at a fix time after coccidian delivery, if cycling is lightly altered, it can influence how C. perfringens induce lesions. I would suggest to include this discussion in the paper, limiting the expected outcomes of the results obtained.

In lines 481-483 the authors state that the results "indicate" that microbiota modulation is an important mechanism of action of high-flavonoid corn in the observed reduction of necrotic enteritis. However, although the results show a modulation of the microbiota, they are not strong enough, due to the experimental design and the co-infection challenge model used, to "indicate" that it is a mechanism of action. The results do show that there is a change in the microbiota and that these changes are associated with a decrease in the number (?) of animals with lesions compatible with necrotic enteritis (I understand that there would be no differences in the degree of lesion, the authors refer to a previous work that is supposed to continue in this one) but there is no direct relationship. These results could suggest a potential role of the microbiota in the observed effects. However, if flavonoids are altering for example intestinal transit, water homeostasis, mucus secretion or antimicrobial peptides secretion, then a microbiota modulation would be also observed. Please consider the potential changes in animal physiology as part of the mechanisms of action of the high-flavonoid corn on the observed microbiota changes.

Reviewer #2: !. The number of replicates is very small to make any logical conclusions. But it is what it is.

2. provide the flainoid content of the corn.

3. provide the basic nutritive value of the corn under study.

**********

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Reviewer #1: Yes: mariano fernandez miyakawa

Reviewer #2: No

**********

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10.1371/journal.pone.0307333.r002
Author response to Decision Letter 0
Submission Version1
1 Jul 2024

A formatted version of this letter is included with all the other files. We could not copy paste tables in this field.

Manuscript: PONE-D-24-09821

Dear Dr. Kogut, PLOS ONE Academic Editor,

We are pleased to submit the revised draft of our manuscript “The effects of a high-flavonoid corn cultivar on the gastrointestinal tract microbiota in chickens undergoing necrotic enteritis” for consideration of publication. We are thankful for the opportunity to receive constructive feedback that helped improve our manuscript. We believe that we were able to address most of your concerns.

Please see below a point-by-point response to the reviewers’ comments. Our answers in this letter are highlighted in blue color, and the changes made to the manuscript are highlighted in yellow.

Sincerely,

Alberto Gino Lorenzoni, DVM, MS, PhD.

Associate Professor of Poultry Science and Avian Health

Department of Animal Science

The Pennsylvania State University

Corresponding Author

Reviewer’s comments to the authors:

Reviewer #1

The results are interesting but there is a need to expand the discussion besides what they were expecting regarding the microbiota modulation. I see an important limitation in the NE challenge model regarding the interpretation of "mechanism of action". Coccidia is also being affected by the flavonoids, and it is at least the 50% of the changes that they are introducing there!

The study is a based in a challenge model that uses infection with coccidia as a predisposing factor for the development of C. perfringens necrotic enteritis. Although this is a very common NE challenge model used in several studies, and also imposed to the industry as almost a gold standar, the inclusion of feed additives or diet to compare treatments against a non-challenged control must be carefully interpreted. Many of these additives (or diet components) can alter the infection of coccidia (either in the level as well as the cycle (i.e. shortening or extending) by direct interaction with the parasite or the physiology of the host. Particularly flavonoids are able to inhibit the invasion and replication of different species of coccidian and also alter epithelial physiology and immune responses of the gut. As C. perfringens challenge is synchronized at a fix time after coccidian delivery, if cycling is lightly altered, it can influence how C. perfringens induce lesions. I would suggest to include this discussion in the paper, limiting the expected outcomes of the results obtained.

Thanks for taking the time to thoroughly review our manuscript and give us feedback.

We agree with your comments. We expanded the discussion with supporting literature that considers the other factors that interplay in the effects observed in our study. (Page 21, lines 445-457).

In lines 481-483 the authors state that the results "indicate" that microbiota modulation is an important mechanism of action of high-flavonoid corn in the observed reduction of necrotic enteritis. However, although the results show a modulation of the microbiota, they are not strong enough, due to the experimental design and the co-infection challenge model used, to "indicate" that it is a mechanism of action. The results do show that there is a change in the microbiota and that these changes are associated with a decrease in the number (?) of animals with lesions compatible with necrotic enteritis (I understand that there would be no differences in the degree of lesion, the authors refer to a previous work that is supposed to continue in this one) but there is no direct relationship. These results could suggest a potential role of the microbiota in the observed effects. However, if flavonoids are altering for example intestinal transit, water homeostasis, mucus secretion or antimicrobial peptides secretion, then a microbiota modulation would be also observed. Please consider the potential changes in animal physiology as part of the mechanisms of action of the high-flavonoid corn on the observed microbiota changes.

We agree with your comment. We made changes to the text (Page 23, Lines 492-495; and Lines 497-502) and added supporting information (Page 21, lines 444-457) to explore the other factors that were not evaluated in this study.

Reviewer #2:

1. The number of replicates is very small to make any logical conclusions.

We refined our conclusion to account for pitfalls related to experimental design (Page 23, Lines 496-502).

2. provide the flavainoid content of the corn.

We added a sentence reporting the flavonoid content of both corn lines used in the study, and referenced previously reported data (Page 6, Lines 147-148).

Reference:

Buiatte V, Dominguez D, Lesko T, Jenkins M, Chopra S, Lorenzoni AG. Inclusion of high-flavonoid corn in the diet of broiler chickens as a potential approach for the control of necrotic enteritis. Poult Sci. 2022;101. doi:10.1016/j.psj.2022.101796

3. provide the basic nutritive value of the corn under study.

The complete wet chemistry analyses of the corn lines have been previously reported (Figure below). We added a sentence in the materials and methods to address your concern. (Page 6, Lines 148-149).

Reference:

Buiatte V, Dominguez D, Lesko T, Jenkins M, Chopra S, Lorenzoni AG. Inclusion of high-flavonoid corn in the diet of broiler chickens as a potential approach for the control of necrotic enteritis. Poult Sci. 2022;101. doi:10.1016/j.psj.2022.101796

Source: Buiatte et al. (2022), Poultry Science.

Attachment Submitted filename: Rebuttal_Plos_One_Buiatte_2.docx

10.1371/journal.pone.0307333.r003
Decision Letter 1
Kogut Michael H. Academic Editor
© 2024 Michael H. Kogut
2024
Michael H. Kogut
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
3 Jul 2024

The effects of a high-flavonoid corn cultivar on the gastrointestinal tract microbiota in chickens undergoing necrotic enteritis

PONE-D-24-09821R1

Dear Dr. Lorenzoni,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Michael H. Kogut, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

10.1371/journal.pone.0307333.r004
Acceptance letter
Kogut Michael H. Academic Editor
© 2024 Michael H. Kogut
2024
Michael H. Kogut
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
9 Jul 2024

PONE-D-24-09821R1

PLOS ONE

Dear Dr. Lorenzoni,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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PLOS ONE
==== Refs
References

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