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. 2016 Jul 20;11(7):e0159770.
doi: 10.1371/journal.pone.0159770. eCollection 2016.

Impact of Helminth Infections and Nutritional Constraints on the Small Intestine Microbiota

Affiliations

Impact of Helminth Infections and Nutritional Constraints on the Small Intestine Microbiota

Isabella M Cattadori et al. PLoS One. .

Abstract

Helminth infections and nutrition can independently alter the composition and abundance of the gastrointestinal microbiota, however, their combined effect is poorly understood. Here, we used the T. retortaeformis-rabbit system to examine how the helminth infection and host restriction from coprophagy/ready-to-absorb nutrients affected the duodenal microbiota, and how these changes related to the acquired immune response at the site of infection. A factorial experiment was performed where the bacterial community, its functionality and the immune response were examined in four treatments (Infect, Infect+Collar, Control+Collar and Control). Helminths reduced the diversity and abundance of the microbiota while the combination of parasites and coprophagic restriction led to a more diversified and abundant microbiota than infected cases, without significantly affecting the intensity of infection. Animals restricted from coprophagy and free from parasites exhibited the richest and most abundant bacterial community. By forcing the individuals to absorb nutrients from less digested food, the coprophagic restriction appears to have facilitated the diversity and proliferation of bacteria in the duodenum. Changes in the microbiota were more clearly associated with changes in the immune response for the infected than the nutrient restricted animals. The functional and metabolic characteristics of the duodenal microbiota were not significantly different between treatments. Overall, infection and diet affect the gut microbiota but their interactions and outcome can be complex. These findings can have important implications for the development of control measures to helminth infections where poor nutrition/malnutrition can also be a concern.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Abundance and alpha diversity of the duodenum microbiota.
Abundance and diversity by treatment (Infect: I, Infect+Collar: IC, Control+Collar: CC and Control: C) at the 3 taxonomic levels (time points pooled together). The mean, 25–75% percentiles, maximum and minimum and outliers from the Shannon diversity index are reported. Diversity is significantly lower in I than in CC or C groups. The number of taxa is similar among treatments.
Fig 2
Fig 2. Relative microbiota abundance by animal.
Abundance at the 3 taxonomic levels in the four treatments (Infect: I, Infect+Collar: IC, Control+Collar: CC and Control: C). Only the taxa with a relative abundance greater than 1% at each taxonomic level are presented. The white space that adds up to 100% abundance should be interpreted as 'other' taxa.
Fig 3
Fig 3. Expression of cytokine, transcription factor and function genes in the duodenal mucosa.
Mean values (2-ΔΔct±s.e.) by treatment (Infect: I, Infect+Collar: IC, Control+Collar: CC and Control: C) and sampling time (day post initial infection). Data have been standardized by the housekeeping gene HPRT and the mean values from the baseline animals sampled at day 0. Most of the variables show significant differences among treatments but not sampling time, higher values are observed in the I and IC groups.
Fig 4
Fig 4. Antibody IgA from the duodenal mucus.
Mean (±s.e.) Optical Density (O.D.) for the species-specific (left y-axis, full cycle) and the total (right y-axis, full square) IgA by treatment (Infect: I, Infect+Collar: IC, Control+Collar: CC and Control: C) and sampling time (days post initial infection). Both specific and total IgA are higher in I and IC than in CC or C.
Fig 5
Fig 5. Relationships between microbiota and immune response.
Spearman rank correlations grouping animals by treatment (Infect: I, Infect+Collar: IC, Control+Collar: CC, and Control: C) and using only the bacteria that showed a between-individual coefficient of variation (CV) greater than 150. Negative relationships are in grades of blue while positive relationships are in grades of red; a correlation coefficient quantifies each grade, null correlations (0) are depicted in white; significant correlations (p<0.05) are highlighted with a star. Significant correlations are more common in the Infect group than the remaining treatments.
Fig 6
Fig 6. Relationships between microbiota and T. retortaeformis abundance.
Spearman rank correlations grouping animals by treatment (Infect -I, Infect+Collar -IC), or treatment and time point, and: Panel A- the bacteria families with CV>150 or Panel B- the functions at the KEGG pathway module 2 that exhibited a coefficient of variation CV among individuals greater than 70,000. Additional details on the color-coding of the correlations are reported in Fig 5, while the activities related to the KEGG function IDs are listed in Fig 7. No significant correlations are detected, except in two cases.
Fig 7
Fig 7. Relative abundance of gene functionality in the duodenum by animal.
Data are presented by treatment (Infect: I, Infect+Collar: IC, Control+Collar: CC, and Control: C) at the KEGG pathway level 2. Only the functions with a relative abundance greater than 1% were displayed. The white space that adds up to 100% abundance should be considered as 'other' functions. The following function IDs and related activities were selected: CellProc1 = Cellular Processes: Cell Motility; CellProc2 = Cellular Processes: Transport and Catabolism; EnvInfo1 = Environmental Information: Processing Membrane Transport; EnvInfo2 = Environmental Information: Processing Signal Transduction; GenInfo1 = Genetic Information: Processing Folding Sorting and Degradation; GenInfo2 = Genetic Information: Processing Replication and Repair; GenInfo3 = Genetic Information: Processing Transcription; GenInfo4 = Genetic Information: Processing Translation; Metab1 = Metabolism: Amino Acid Metabolism; Metab2 = Metabolism: Biosynthesis of Other Secondary Metabolites; Metab3 = Metabolism: Carbohydrate Metabolism; Metab4 = Metabolism: Energy Metabolism; Metab5 = Metabolism: Enzyme Families; Metab6 = Metabolism: Glycan Biosynthesis and Metabolism; Metab7 = Metabolism: Lipid Metabolism; Metab8 = Metabolism: Metabolism of Cofactors and Vitamins; Metab9 = Metabolism: Metabolism of Other Amino Acids; Metab10 = Metabolism: Metabolism of Terpenoids and Polyketides; Metab11 = Metabolism: Nucleotide Metabolism; Metab12 = Metabolism: Xenobiotics Biodegradation and Metabolism.
Fig 8
Fig 8. Relationships between gene functionality and immune response.
Spearman rank correlations grouping animals by treatment (Infect: I, Infect+Collar: IC, Control+Collar: CC, and Control: C) and using the KEGG pathway level 2 functions with a CV>70,000. Additional details are reported in Figs 5 and 7. Significant correlations are mainly observed with a type 1 response.

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