alternative protein sources for monogastrics: composition ... · alternative protein sources for...
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![Page 1: Alternative protein sources for monogastrics: composition ... · Alternative protein sources for monogastrics: composition and functional assessment S K Kar, A J M Jansman, L Kruijt,](https://reader035.vdocuments.us/reader035/viewer/2022063011/5fc4cb660649c158b0068f13/html5/thumbnails/1.jpg)
Alternative protein sources for monogastrics:
composition and functional assessment
S K Kar, A J M Jansman, L Kruijt, E H Stolte, N Benis, D Schokker, M A Smits
66th EAAP, Warsaw 01-09-2015
[email protected] +31 (0) 687 500 105
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• To characterize the protein component of
new/alternative protein sources using proteomics
• To predict functionality of protein sources using
bioinformatics
• To assess functional properties of new/alternative
protein sources using animal models (mice and pigs)
• To elucidate underlying mechanisms
Aim of project
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• To characterize the protein component of
new/alternative protein sources using proteomics
• To predict functionality of protein sources using
bioinformatics
• To assess functional properties of new/alternative
protein sources using animal models (mice and pigs)
• To elucidate underlying mechanisms
Topics of today’s presentation
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Systemic effects
Cytokines Metabolites
Digestion of proteins in gastro-intestinal tract
Proteomics: MS
Genomics: NGS
Transcriptomics: Microarray
ELISA MS/ GC
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Feed Ingredients
CAS: Casein (feed grade)
DWP: Delactosed Whey Powder
SDPP: Spray Dried Plasma Protein
SBM: Soybean Meal
WGM: Wheat Gluten Meal
YMW: Yellow Meal Worm
Proteomics analysis
MS
Bioinformatic analysis
Proteomic analysis of feed ingredients
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List of identified
peptides and proteins
Selected top 90% of the of the total calculated protein
content
Amino acid composition
(ACC) prediction
Compare ACC values obtained with
conventional analytical method
Prediction of bioactivity
In silico digestion with pepsin, trypsin and
chymotrypsin
Bioinformatic analysis of feed ingredients
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CAS DWP SDPP SBM WGM YMW
MS
70 130 210 748 586 43
Number of identified proteins
3 3 25 68 24 19
Number of proteins forming 90% of total protein fraction
Results: proteomic analysis
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0
5
10
15
20
25
Arg
Val
His
Ile
Leu
Lys
Met
Phe
Thr
Tyr
Cys
Asx
GLx
Ser
Pro
Gly
Ala
TrpCAS
0
5
10
15
20
25
Arg
Val
His
Ile
Leu
Lys
Met
Phe
Thr
Tyr
Cys
Asx
GLx
Ser
Pro
Gly
Ala
Trp
--- MS-based predicted ACC
r: 0.94
--- Chemically defined ACC
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--- MS-based predicted ACC
--- Chemically defined ACC
0
10
20
30Arg
ValHis
Ile
Leu
Lys
Met
PheThr
TyrCys
Asx
GLx
Ser
Pro
Gly
AlaTrp
CAS
0
5
10
15
20Arg
ValHis
Ile
Leu
Lys
Met
PheThr
TyrCys
Asx
GLx
Ser
Pro
Gly
AlaTrp
DWP
0
5
10
15Arg
ValHis
Ile
Leu
Lys
Met
PheThr
TyrCys
Asx
GLx
Ser
Pro
Gly
AlaTrp
SDPP
0
5
10
15
20Arg
ValHis
Ile
Leu
Lys
Met
PheThr
TyrCys
Asx
GLx
Ser
Pro
Gly
AlaTrp
SBM
0
10
20
30
40Arg
ValHis
Ile
Leu
Lys
Met
PheThr
TyrCys
Asx
GLx
Ser
Pro
Gly
AlaTrp
WGM
0
5
10
15Arg
ValHis
Ile
Leu
Lys
Met
PheThr
TyrCys
Asx
GLx
Ser
Pro
Gly
AlaTrp
YMW
r: 0.94 r: 0.94
r: 0.86 r: 0.92
r: 0.94 r: 0.87
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Proportion of bio-functional properties of proteins
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• MS-based analysis provides more detailed information on the composition of complex protein sources compared to conventional (nutritional) analytical approaches.
• MS-based approach was effective in predicting the amino acid composition of protein sources.
• MS-based analysis allows the detection of individual proteins in complex matrices at very high resolution.
• MS-based analysis allows the prediction of bio-functional properties of protein.
Proteomics/bioinformatics analysis: conclusions
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Animals: C57BL/6
Sex of Animal : Male
Diets: 30% Protein fraction was replaced by the “protein
derived from new sources”. The diets were identical with
respect to all other nutrients compared to AIN 93-G
Chemical composition of the experimental diet
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Day 36-42 Day 43-49 Day 50-560
1
2
3
4
5SBM
CAS
DWP
SDPP
WGM
YMW
***
*** ***
*
***
Age of mice
Avara
ge
fe
ed
in
tak
e (
g/d
ay)
Day 35 Day 42 Day 49 Day 560
10
20
30
40SBM
CAS
DWP
SDPP
WGM
YMW
*** *****
Bo
dy w
eig
ht
(g)
Feed-Intake
Body weight gain
Bars and whiskers represents means values ± SD (n = 6). *P < 0.05 ** P < 0.01 *** P < 0.001
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Comparison of
experimental
diet vs SBM
Number
upregulated
gene-sets
Number down
regulated gene-
sets
Number of
significantly
enriched gene-
sets (FDR <
0.05)
CAS 559 0 82
DWP 561 0 124
SDPP 591 0 63
WGM 516 0 62
YMW 559 0 98
Local response: Ileal gene expression profile (microarray)
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Immune response
Metabolic response
Diet specific responses
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SBM
SBM
DW
P
WG
M
CA
S
YMW
SDP
P
DW
P
WG
M
CA
S
YMW
SDP
P
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• Panel of 23 biomarkers were analysed • Increase of granulocyte colony stimulating factor (G-CSF) in SBM fed mice • Increase of Eotaxin for DWP- and YMW-fed mice
• Increase of IL-12p70 in DWP-fed mice
• Decrease of G-CSF, granulocyte-macrophage colony-stimulating factor (GM-CSF),
IL-5, IL-6, IL-13 and monocyte chemotactic protein (MCP) in WGM-fed mice compared to SBM-fed mice
Systemic response: Cytokines and Chemokines in blood
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Metabolites
Amines (Urine) Acyl carnitines (Urine) Amines (Serum)
Number of
Analytes
53 16 41
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• Diets based on different protein sources affect host responses: • local (ileal microbiota, expression of (immune-related) genes) • systemic (serum cytokines/chemokines and urine metabolites)
• SBM differs clearly from the other experimental diets
• Diet specific effects identified (not shown here)
• DWP and YMW responses more similar to each other
• Knowledge may help to formulate monogastric diets
Mice experiment: conclusions and discussion