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pmid:27100182

Intestinal Microbiota and Microbial Metabolites Are Changed in a Pig Model Fed a High-Fat/Low-Fiber or a Low-Fat/High-Fiber Diet.

Sonja N Heinritz · Eva Weiss · Meike Eklund · Tobias Aumiller · Sandrine Louis · Andreas Rings · Sabine Messner · Amélia Camarinha-Silva · Jana Seifert · Stephan C Bischoff · Rainer Mosenthin

PloS one · 2016 doi:10.1371/journal.pone.0154329

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:27100182
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2026-11-01T15:40:17Z
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2026-11-01T21:59:01Z
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paper object · 10 fields
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title
Intestinal Microbiota and Microbial Metabolites Are Changed in a Pig Model Fed a High-Fat/Low-Fiber or a Low-Fat/High-Fiber Diet.
doi
10.1371/journal.pone.0154329
pmid
27100182
authors array · 11 items
[0]
Sonja N Heinritz
[1]
Eva Weiss
[2]
Meike Eklund
[3]
Tobias Aumiller
[4]
Sandrine Louis
[5]
Andreas Rings
[6]
Sabine Messner
[7]
Amélia Camarinha-Silva
[8]
Jana Seifert
[9]
Stephan C Bischoff
[10]
Rainer Mosenthin
journal
PloS one
year
2016
abstract
The intestinal microbiota and its metabolites appear to be an important factor for gastrointestinal function and health. However, research is still needed to further elaborate potential relationships between nutrition, gut microbiota and host's health by means of a suitable animal model. The present study examined the effect of two different diets on microbial composition and activity by using the pig as a model for humans. Eight pigs were equally allotted to two treatments, either fed a low-fat/high-fiber (LF), or a high-fat/low-fiber (HF) diet for 7 weeks. Feces were sampled at day 7 of every experimental week. Diet effects on fecal microbiota were assessed using quantitative real-time PCR, DNA fingerprinting and metaproteomics. Furthermore, fecal short-chain fatty acid (SCFA) profiles and ammonia concentrations were determined. Gene copy numbers of lactobacilli, bifidobacteria (P<0.001) and Faecalibacterium prausnitzii (P<0.05) were higher in the LF pigs, while Enterobacteriaceae were more abundant in the HF pigs (P<0.001). Higher numbers of proteins affiliated to Enterobacteriaceae were also present in the HF samples. Proteins for polysaccharide breakdown did almost exclusively originate from Prevotellaceae. Total and individual fecal SCFA concentrations were higher for pigs of the LF treatment (P<0.05), whereas fecal ammonia concentrations did not differ between treatments (P>0.05). Results provide evidence that beginning from the start of the experiment, the LF diet stimulated beneficial bacteria and SCFA production, especially butyrate (P<0.05), while the HF diet fostered those bacterial groups which have been associated with a negative impact on health conditions. These findings correspond to results in humans and might strengthen the hypothesis that the response of the porcine gut microbiota to a specific dietary modulation is in support of using the pig as suitable animal model for humans to assess diet-gut-microbiota interactions. Data are available via ProteomeXchange with identifier PXD003447.
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Using the selected correlation structure, the data were subjected to a mixed model analysis using the MIXED procedure of SAS.
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sections[12] "Statistical analyses of quantitative real-time PCR and microbial metabolite data" paragraph 2
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Using the selected correlation structure, the data were subjected to a mixed model analysis using the MIXED procedure of SAS.
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sections[22] "Data Availability" paragraph 2
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All relevant data are within the paper and its Supporting Information files.
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The LF diet stimulated beneficial bacteria and short-chain fatty acid production, whereas the HF diet favored potentially harmful bacterial groups; diet effects were assessed by quantitative real-time PCR, DNA fingerprinting, metaproteomics, SCFA and ammonia analyses, with repeated-measures data analyzed using SAS mixed models with REML-selected correlation structures and Kenward–Roger comparisons, and proteomics data available through ProteomeXchange identifier PXD003447.
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Results provide evidence that beginning from the start of the experiment, the LF diet stimulated beneficial bacteria and SCFA production, especially butyrate (P<0.05), while the HF diet fostered those bacterial groups which have been associated with a negative impact on health conditions.
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Results provide evidence that beginning from the start of the experiment, the LF diet stimulated beneficial bacteria and SCFA production, especially butyrate (P<0.05), while the HF diet fostered those bacterial groups which have been associated with a negative impact on health conditions.
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Diet effects on fecal microbiota were assessed using quantitative real-time PCR, DNA fingerprinting and metaproteomics.
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sections[0] "Abstract" paragraph 2
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Data are available via ProteomeXchange with identifier PXD003447.
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The LF diet stimulated beneficial bacteria and short-chain fatty acid production, whereas the HF diet favored potentially harmful bacterial groups; diet effects were assessed by quantitative real-time PCR, DNA fingerprinting, metaproteomics, SCFA and ammonia analyses, with repeated-measures data analyzed using SAS mixed models with REML-selected correlation structures and Kenward–Roger comparisons, and proteomics data available through ProteomeXchange identifier PXD003447.
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pig microbiota ob/le - Intestinal Microbiota and Microbial Metabolites Are Changed in a Pig Model Fed a High-Fat/Low-Fiber or a Low-Fat/High-Fiber Diet
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Ingredient composition, nutrient and energy content of the HF and LF diets.
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Study design
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Table 1
caption
Ingredient composition, nutrient and energy content of the HF and LF diets.
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[0] array · 3 items
[0]
Ingredient, g/kg
[1]
HF
[2]
LF
[1] array · 3 items
[0]
Wheat
[1]
184.9
[2]
477.3
[2] array · 3 items
[0]
Wheat floura
[1]
200.0
[2]
[3] array · 3 items
[0]
Wheat branb
[1]
50.0
[2]
350.0
[4] array · 3 items
[0]
Caseinc
[1]
152.0
[2]
120.5
[5] array · 3 items
[0]
Sunflower margarined
[1]
70.0
[2]
[6] array · 3 items
[0]
Sweet cream buttere
[1]
150.0
[2]
[7] array · 3 items
[0]
Soy oil
[1]
30.0
[2]
15.0
[8] array · 3 items
[0]
Fructosef
[1]
50.0
[2]
[9] array · 3 items
[0]
Dextroseg
[1]
50.0
[2]
[10] array · 3 items
[0]
Celluloseh
[1]
30.0
[2]
10.0
[11] array · 3 items
[0]
Vitamin and mineral premixi
[1]
17.0
[2]
13.4
[12] array · 3 items
[0]
Potassium chloride
[1]
1.4
[2]
[13] array · 3 items
[0]
Monocalciumphosphate
[1]
5.4
[2]
[14] array · 3 items
[0]
Sodium chloride
[1]
[2]
0.2
[15] array · 3 items
[0]
Calcium carbonate
[1]
4.3
[2]
8.5
[16] array · 3 items
[0]
TIO2
[1]
5.0
[2]
5.0
[17] array · 3 items
[0]
Analyzed nutrient content
[1]
[2]
[18] array · 3 items
[0]
Dry matter (DM), g/kg
[1]
890.3
[2]
895.7
[19] array · 3 items
[0]
Crude protein, g/kg DM
[1]
210.2
[2]
244.3
[20] array · 3 items
[0]
Crude fat, g/kg DM
[1]
248.6
[2]
41.3
[21] array · 3 items
[0]
Neutral detergent fiber, g/kg DM
[1]
66.3
[2]
216.8
[22] array · 3 items
[0]
Gross energy, MJ/kg DM
[1]
23.3
[2]
19.2
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Development of bacterial numbers in feces of pigs (n = 4 per treatment) over time (log10 16S ribosomal RNA gene copies/g FM).
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PMC4839692
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16
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Effect of diet composition on the total fecal microbiota
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24
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13
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Table 2
caption
Development of bacterial numbers in feces of pigs (n = 4 per treatment) over time (log10 16S ribosomal RNA gene copies/g FM).
rows array · 24 items
[0] array · 7 items
[0]
[1]
[2]
[3]
[4]
Experimental Week
[5]
Pooled SEM
[6]
P-Values
[1] array · 13 items
[0]
[1]
[2]
[3]
1
[4]
2
[5]
3
[6]
4
[7]
5
[8]
6
[9]
7
[10]
[11]
Diet
[12]
Week*Diet (HF or LF)
[2] array · 13 items
[0]
[1]
Total bacteria
[2]
HF
[3]
10.3
[4]
10.3
[5]
10.3
[6]
10.3
[7]
10.2
[8]
10.1
[9]
10.2
[10]
0.08
[11]
0.322
[12]
0.361
[3] array · 13 items
[0]
[1]
[2]
LF
[3]
10.3
[4]
10.2
[5]
10.2
[6]
10.2
[7]
10.2
[8]
10.0
[9]
10.1
[10]
[11]
[12]
0.194
[4] array · 13 items
[0]
Firmicutes
[1]
Roseburia spp.
[2]
HF
[3]
8.0
[4]
8.2
[5]
7.9
[6]
7.8
[7]
8.2
[8]
7.9
[9]
7.9
[10]
0.22
[11]
0.847
[12]
0.574
[5] array · 13 items
[0]
[1]
[2]
LF
[3]
8.3z
[4]
8.2yz
[5]
7.9wz
[6]
7.8wxy
[7]
8.0xz
[8]
7.5w
[9]
7.7wx
[10]
[11]
[12]
0.014
[6] array · 13 items
[0]
[1]
Lactobacillus spp.
[2]
HF
[3]
7.9
[4]
7.2a
[5]
8.0
[6]
7.5
[7]
7.4a
[8]
7.3
[9]
7.4
[10]
0.22
[11]
<0.001
[12]
0.145
[7] array · 13 items
[0]
[1]
[2]
LF
[3]
8.2
[4]
8.2b
[5]
8.2
[6]
8.2
[7]
8.1b
[8]
7.7
[9]
7.9
[10]
[11]
[12]
0.609
[8] array · 13 items
[0]
[1]
Clostridium leptum
[2]
HF
[3]
7.3awx
[4]
6.9aw
[5]
8.3y
[6]
7.7x
[7]
8.3y
[8]
8.0xy
[9]
8.4y
[10]
0.31
[11]
0.016
[12]
0.004
[9] array · 13 items
[0]
[1]
[2]
LF
[3]
8.8b
[4]
8.7b
[5]
8.6
[6]
8.6
[7]
8.8
[8]
8.4
[9]
8.4
[10]
[11]
[12]
0.817
[10] array · 13 items
[0]
[1]
Clostridium XIVab
[2]
HF
[3]
9.2
[4]
9.3
[5]
9.4
[6]
9.2
[7]
9.3
[8]
9.2
[9]
9.2
[10]
0.15
[11]
0.360
[12]
0.925
[11] array · 13 items
[0]
[1]
[2]
LF
[3]
9.3
[4]
9.3
[5]
9.3
[6]
9.1
[7]
9.0
[8]
9.0
[9]
9.2
[10]
[11]
[12]
0.406
[12] array · 13 items
[0]
[1]
Enterococcus spp.
[2]
HF
[3]
7.0x
[4]
6.7wx
[5]
6.4w
[6]
6.6wx
[7]
6.8x
[8]
6.8x
[9]
6.4w
[10]
0.14
[11]
0.637
[12]
0.032
[13] array · 13 items
[0]
[1]
[2]
LF
[3]
7.0
[4]
6.8
[5]
6.6
[6]
6.5
[7]
6.6
[8]
6.5
[9]
6.5
[10]
[11]
[12]
0.218
[14] array · 13 items
[0]
[1]
F. prausnitzii
[2]
HF
[3]
5.4a
[4]
5.0a
[5]
6.1a
[6]
5.8a
[7]
6.3a
[8]
5.7
[9]
6.2
[10]
0.43
[11]
0.009
[12]
0.077
[15] array · 13 items
[0]
[1]
[2]
LF
[3]
7.8b
[4]
7.7b
[5]
7.5b
[6]
7.4b
[7]
7.7b
[8]
7.0
[9]
7.0
[10]
[11]
[12]
0.378
[16] array · 13 items
[0]
Bacteroidetes
[1]
Bacteroides group
[2]
HF
[3]
9.3w
[4]
9.4w
[5]
9.4w
[6]
9.4w
[7]
9.0x
[8]
8.8x
[9]
8.9x
[10]
0.10
[11]
0.490
[12]
<0.001
[17] array · 13 items
[0]
[1]
[2]
LF
[3]
9.4z
[4]
9.3yz
[5]
9.1xy
[6]
9.3yz
[7]
8.9wx
[8]
8.9wx
[9]
8.8w
[10]
[11]
[12]
<0.001
[18] array · 13 items
[0]
[1]
Prevotella spp.
[2]
HF
[3]
9.7
[4]
9.7
[5]
9.5
[6]
9.7
[7]
9.7
[8]
9.4
[9]
9.6
[10]
0.11
[11]
0.512
[12]
0.346
[19] array · 13 items
[0]
[1]
[2]
LF
[3]
9.8
[4]
9.7
[5]
9.4
[6]
9.6
[7]
9.6
[8]
9.6
[9]
9.5
[10]
[11]
[12]
0.291
[20] array · 13 items
[0]
Actinobacteria
[1]
Bifidobacterium spp.
[2]
HF
[3]
5.5aw
[4]
5.6aw
[5]
5.3aw
[6]
6.6ax
[7]
6.0awx
[8]
5.8awx
[9]
5.1aw
[10]
0.32
[11]
<0.001
[12]
0.033
[21] array · 13 items
[0]
[1]
[2]
LF
[3]
8.1b
[4]
8.3b
[5]
8.2b
[6]
8.3b
[7]
8.5b
[8]
7.7b
[9]
7.9b
[10]
[11]
[12]
0.506
[22] array · 13 items
[0]
Proteobacteria
[1]
Enterobacteriaceae
[2]
HF
[3]
8.3a
[4]
8.3a
[5]
7.4a
[6]
8.3a
[7]
8.3a
[8]
8.3a
[9]
7.8a
[10]
0.31
[11]
<0.001
[12]
0.097
[23] array · 13 items
[0]
[1]
[2]
LF
[3]
6.3bx
[4]
6.0bwx
[5]
5.4bw
[6]
6.3bx
[7]
5.5bw
[8]
6.7bx
[9]
6.1bwx
[10]
[11]
[12]
0.012
locator
section[16]/table-wrap[1]#pone.0154329.t002
[4] object · 16 fields
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Development of short-chain fatty acids (SCFA) and ammonia contents in feces of pigs (n = 4 per treatment) over time.
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PMC4839692
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18
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Effect of diet composition on fecal microbial metabolites
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Table 3
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Development of short-chain fatty acids (SCFA) and ammonia contents in feces of pigs (n = 4 per treatment) over time.
rows array · 13 items
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[2]
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Diet
[11]
Week*Diet (HF or LF)
[2] array · 12 items
[0]
SCFA, mmol/kg of DM
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[3] array · 12 items
[0]
Acetate
[1]
HF
[2]
110.5a
[3]
108.5a
[4]
134.6a
[5]
121.8a
[6]
123.0a
[7]
123.8a
[8]
147.8a
[9]
26.08
[10]
<0.001
[11]
0.837
[4] array · 12 items
[0]
[1]
LF
[2]
285.5bwxy
[3]
314.2bwy
[4]
268.9bwx
[5]
305.3bwxy
[6]
338.1by
[7]
248.8bx
[8]
253.8bx
[9]
[10]
[11]
0.029
[5] array · 12 items
[0]
Propionate
[1]
HF
[2]
77.9
[3]
74.6
[4]
63.7a
[5]
75.5
[6]
76.0a
[7]
76.4a
[8]
65.9
[9]
11.69
[10]
0.021
[11]
0.924
[6] array · 12 items
[0]
[1]
LF
[2]
91.6
[3]
102.8
[4]
105.7b
[5]
104.8
[6]
112.4b
[7]
111.6b
[8]
89.7
[9]
[10]
[11]
0.534
[7] array · 12 items
[0]
Butyrate
[1]
HF
[2]
39.5a
[3]
34.0a
[4]
30.2a
[5]
35.5a
[6]
43.9a
[7]
42.2
[8]
45.7
[9]
6.84
[10]
0.018
[11]
0.131
[8] array · 12 items
[0]
[1]
LF
[2]
61.9b
[3]
72.4b
[4]
54.4b
[5]
61.0b
[6]
67.9b
[7]
55.5
[8]
54.9
[9]
[10]
[11]
0.148
[9] array · 12 items
[0]
Total1
[1]
HF
[2]
270.0a
[3]
258.1a
[4]
255.9a
[5]
270.5a
[6]
283.4a
[7]
281.8a
[8]
293.4a
[9]
42.71
[10]
0.002
[11]
0.923
[10] array · 12 items
[0]
[1]
LF
[2]
481.4b
[3]
537.2b
[4]
469.5b
[5]
517.5b
[6]
569.1b
[7]
486.6b
[8]
442.5b
[9]
[10]
[11]
0.090
[11] array · 12 items
[0]
Ammonia, mmol/kg of DM
[1]
HF
[2]
12.7
[3]
10.9
[4]
9.1
[5]
11.7
[6]
13.8
[7]
13.0
[8]
13.3
[9]
1.31
[10]
0.249
[11]
0.176
[12] array · 12 items
[0]
[1]
LF
[2]
14.7
[3]
12.6
[4]
10.6
[5]
12.1
[6]
14.9
[7]
14.4
[8]
11.0
[9]
[10]
[11]
0.131
locator
section[18]/table-wrap[1]#pone.0154329.t003
gaps array · 2 items
[0]
asset:data:geo:PXD003447 not downloaded
[1]
asset:data:geo:PXD003447 licence not read
analysis object · 4 fields
state
partial
methods array · 2 items
[0] object · 10 fields
id
method-1
family
regression_modelling
specific
mixed_effects
label
Regression modelling
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
regression_modelling_mixed_effects
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictors_and_outcome
evidence object · 3 fields
score
0.8
band
high
quote
mixed model
[1] object · 10 fields
id
method-2
family
null
specific
null
label
Repeated-measures linear mixed-model analysis using the MIXED procedure in SAS
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
Using the selected correlation structure, the data were subjected to a mixed model analysis using the MIXED procedure of SAS.
targets array · 1 item
[0] object · 8 fields
id
target-published-estimate
claim_id
claim-target-result
operation
regression_modelling_mixed_effects
unit
p-value
tolerance
null
state
complete
note
The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
gaps array · 2 items
[0]
method.reader_named_unmapped
[1]
method.regression_modelling_mixed_effects
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
false
required_operations array · 0 items
missing_operations array · 1 item
[0]
regression_modelling_mixed_effects
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: mixed_effects.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 1 item
[0]
PXD003447
resolved_accessions array · 1 item
[0]
PXD003447
statements array · 1 item
[0]
All relevant data are within the paper and its Supporting Information files.
basis
The paper names PXD003447 in ProteomeXchange Consortium via the PRIDE partner repository and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 0 items
assets array · 5 items
[0]
source:pmid:27100182
[1]
data:geo:PXD003447
[2]
asset:table:pmid:27100182:1
[3]
asset:table:pmid:27100182:2
[4]
asset:table:pmid:27100182:3
steps array · 0 items
must_build object · 5 fields
dsl_operations array · 1 item
[0] object · 3 fields
operation
regression_modelling_mixed_effects
priority
null
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:PXD003447
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:28:28Z
independence
same_provider
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
8
usage object · 3 fields
prompt_tokens
130330
completion_tokens
4271
cached_tokens
1560
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:27100182
type
paper
label
pmid:27100182
[1] object · 3 fields
id
source:pmid:27100182
type
manuscript
label
PMID 27100182 full text
[2] object · 3 fields
id
data:geo:PXD003447
type
dataset
label
GEO PXD003447
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:27100182
type
paper
label
pmid:27100182
[1] object · 3 fields
id
source:pmid:27100182
type
manuscript
label
PMID 27100182 full text
[2] object · 3 fields
id
data:geo:PXD003447
type
dataset
label
GEO PXD003447
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-27100182
type
extraction
used array · 1 item
[0]
source:pmid:27100182
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-PXD003447
type
accession_resolution
used array · 1 item
[0]
data:geo:PXD003447
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:27100182
parent_hash
sha256:d9c4c114ea14a35b455906cad521df2295bec7a1ec59f6d706cb39aae1b5a344
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:PXD003447 licence not read
[1]
asset:data:geo:PXD003447 not downloaded
[2]
execution.not_run
[3]
method.reader_named_unmapped
[4]
method.regression_modelling_mixed_effects
[5]
review.human_critique
[6]
review.independent_reader
[7]
review.machine_critique
[8]
step:run_analysis blocked on method:regression_modelling_mixed_effects
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 2 items
[0]
microbiota were assessed using quantitative real-time PCR, DNA fingerprinting and metaproteomics. Furt
[1]
als S1 TableOligonucleotide primers used for real-time PCR.(DOCX)Click here for additional data file. S
order_basis
paper_text_order:773
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
regression_modelling
definition_version
f2132e96ae6c7a4e
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
mixed model
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:27100182", "kind": "science-record", "revision": 7, "content_hash": "sha256:d93e2abdf861f980f255d85b31c0a0144c2693a6c06cded848297dac270b8321", "created_at": "2026-11-01T15:40:17Z", "updated_at": "2026-11-01T21:59:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Intestinal Microbiota and Microbial Metabolites Are Changed in a Pig Model Fed a High-Fat/Low-Fiber or a Low-Fat/High-Fiber Diet.", "doi": "10.1371/journal.pone.0154329", "pmid": "27100182", "authors": ["Sonja N Heinritz", "Eva Weiss", "Meike Eklund", "Tobias Aumiller", "Sandrine Louis", "Andreas Rings", "Sabine Messner", "Amélia Camarinha-Silva", "Jana Seifert", "Stephan C Bischoff", "Rainer Mosenthin"], "journal": "PloS one", "year": 2016, "abstract": "The intestinal microbiota and its metabolites appear to be an important factor for gastrointestinal function and health. However, research is still needed to further elaborate potential relationships between nutrition, gut microbiota and host's health by means of a suitable animal model. The present study examined the effect of two different diets on microbial composition and activity by using the pig as a model for humans. Eight pigs were equally allotted to two treatments, either fed a low-fat/high-fiber (LF), or a high-fat/low-fiber (HF) diet for 7 weeks. Feces were sampled at day 7 of every experimental week. Diet effects on fecal microbiota were assessed using quantitative real-time PCR, DNA fingerprinting and metaproteomics. Furthermore, fecal short-chain fatty acid (SCFA) profiles and ammonia concentrations were determined. Gene copy numbers of lactobacilli, bifidobacteria (P<0.001) and Faecalibacterium prausnitzii (P<0.05) were higher in the LF pigs, while Enterobacteriaceae were more abundant in the HF pigs (P<0.001). Higher numbers of proteins affiliated to Enterobacteriaceae were also present in the HF samples. Proteins for polysaccharide breakdown did almost exclusively originate from Prevotellaceae. Total and individual fecal SCFA concentrations were higher for pigs of the LF treatment (P<0.05), whereas fecal ammonia concentrations did not differ between treatments (P>0.05). Results provide evidence that beginning from the start of the experiment, the LF diet stimulated beneficial bacteria and SCFA production, especially butyrate (P<0.05), while the HF diet fostered those bacterial groups which have been associated with a negative impact on health conditions. These findings correspond to results in humans and might strengthen the hypothesis that the response of the porcine gut microbiota to a specific dietary modulation is in support of using the pig as suitable animal model for humans to assess diet-gut-microbiota interactions. Data are available via ProteomeXchange with identifier PXD003447.", "claims": [{"id": "claim-method", "statement": "Using the selected correlation structure, the data were subjected to a mixed model analysis using the MIXED procedure of SAS.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27100182", "locator": "sections[12] \"Statistical analyses of quantitative real-time PCR and microbial metabolite data\" paragraph 2", "quote": "Using the selected correlation structure, the data were subjected to a mixed model analysis using the MIXED procedure of SAS."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All relevant data are within the paper and its Supporting Information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27100182", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "All relevant data are within the paper and its Supporting Information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The LF diet stimulated beneficial bacteria and short-chain fatty acid production, whereas the HF diet favored potentially harmful bacterial groups; diet effects were assessed by quantitative real-time PCR, DNA fingerprinting, metaproteomics, SCFA and ammonia analyses, with repeated-measures data analyzed using SAS mixed models with REML-selected correlation structures and Kenward–Roger comparisons, and proteomics data available through ProteomeXchange identifier PXD003447.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Results provide evidence that beginning from the start of the experiment, the LF diet stimulated beneficial bacteria and SCFA production, especially butyrate (P<0.05), while the HF diet fostered those bacterial groups which have been associated with a negative impact on health conditions."}, "evidence": [{"asset": "source:pmid:27100182", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Results provide evidence that beginning from the start of the experiment, the LF diet stimulated beneficial bacteria and SCFA production, especially butyrate (P<0.05), while the HF diet fostered those bacterial groups which have been associated with a negative impact on health conditions."}, {"asset": "source:pmid:27100182", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Diet effects on fecal microbiota were assessed using quantitative real-time PCR, DNA fingerprinting and metaproteomics."}, {"asset": "source:pmid:27100182", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Data are available via ProteomeXchange with identifier PXD003447."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The LF diet stimulated beneficial bacteria and short-chain fatty acid production, whereas the HF diet favored potentially harmful bacterial groups; diet effects were assessed by quantitative real-time PCR, DNA fingerprinting, metaproteomics, SCFA and ammonia analyses, with repeated-measures data analyzed using SAS mixed models with REML-selected correlation structures and Kenward–Roger comparisons, and proteomics data available through ProteomeXchange identifier PXD003447.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:27100182"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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