Low-grade inflammatory diseases revealed metabolic perturbations that have been linked to various phenotypes, including gut microbiota dysbiosis. Metaproteomics has been used to investigate protein composition profiles at specific steps and in specific healthy/pathologic conditions. Metaproteomics allows researchers to build a more complete overview on protein composition at a specific time (fingerprint) and in specific health conditions, especially when used in combination with meta-omics approaches. Metaproteomics approach and pathway modulation in obesity and diabetes are described.
Authors and Year | Disease | Protein Origin | ↑ Proteins | ↓ Proteins | Metabolic Pathway/Functionality | ||||||||
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NOS Score | |||||||||||||
Gavin et al., 2018 | T1DM | Microbial | 1. Enzymes for mucin degradation 2. Elongation factor 3. Ferredoxin reductase |
4. Transferases (butyrate synthesis) | |||||||||
Gavin et al., 2018 | 101 subjects: 33 NO, 17 SP, 29 SN, 22 CO | Denver, Colorado 46 females and 55 males Age: 9–12 | 3. | ↑ | Investigate functional interactions host-microbiota in subjects with T1D risk | Ferredoxin catabolism 4. |
Cross-sectional | ↑ Butyrate anabolism | |||||
LC-MS/MS | No information about dietary intake. | Wide age range. | 7 | Human | 1. Galectin-3 2. Fibrillin |
3. CELA-3A, | |||||||
Pinto et al., 2017 | 6 subjects: 3 healthy and 3 T1D children | Portugal 2 males and 4 females Age: T1D children 9.3 ± 1.5 and control children 9.3 ± 0.6 years | 4. CUZD1 5. CLCA1 |
Identify differences in the activity of intestinal microbiota between healthy and T1D children | 6. Neutral ceraminidase 7. IGHA1 |
6. | Case-control | ↓ Sphingosine (SPH) and sphingosine 1-phosphate (S1P) 3.4.5. ↓ exocrine pancreas functionality 7.↓ IgA |
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SDS-PAGE and LC-MS/MS (using LTQ Orbitrap) | Small number of T1D children. | 6 | Pinto et al., 2017 | T1DM | Microbial | 1. ilvE (BCAA transaminase) 2. Glutamate dehydrogenase (AA degradation) | |||||||
Heintz et al., 2016 | 20 subjects from 4 families of at least 2 generations presenting at least 2 cases of T1D | Luxembourg 7 males 13 females Age: 5–62 | 3. Bifunctional GMP synthase 4. Glutamine amido transferase 5. Chaperonin GroEL |
6. Phosphoketolas 7. Glyceraldehyde-3-phosphate dehydrogenase, 8. Transketolase |
1.6.8. ↓ Via penthos phosphate → ↑ BCAA synthesis (Shikmic Acid Pathway) | Resolution of the taxonomic and functional attributes of gut microbiota and evaluation of the effect of family on gut microbiota composition | ↓ glycolysis 2.↑ NH4+ (Urea) 7. ↓ Glycolysis →↓ Piruvate ↓ SCFAs |
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Longitudinal study (4 month) | LC (Nano-2D-UPLC-Orbirtap MS system) and MS (TopN-MS/MS method) | Metagenomics and metatranscriptomics | Need for large-scale studies. | 6 | Human | MUC2 precursor | CELA-3A | ↑ Intestinal mucin-2 ↓ Exocrine pancreas functionality |
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Heintz et al., 2016 | T1DM | Microbial | |||||||||||
Singh et al., 2017 | 223 subjects: 110 T1D children/adolescents and 113 healthy siblings | Washington D.C. 115 males 108 females Age: 13.9–14.5 |
Detection of gut microbial differences and evaluation of lysosomal dysfunctions | Case-control | LC-MS/MS | Imperfect glycemic control or subclinical inflammation in T1D patients. No information about eating habits and lifestyle. | 7 | Thiamine synthesis cofactor | |||||
Zhong et al., 2019 | 254 subjects: 77 TN-T2D, 80 Pre-DM, and 97 NGT |
Suzhou, China 173 females 81 males Age: 41–86 | ↓ | Thiamine synthesis | |||||||||
Investigate compositional and functional changes of the gut microbiota to characterize different disease stages | Cross-sectional | iTRAQ-coupled- LC-MS/MS | Metagenomics | Limitations of MS-based proteomics. | Confounding variables: age, drugs (CCB, hypertension, and dyslipidemia), diet, and health conditions. | 7 | Human | ||||||
Zhou et al., 2019 | 106 subjects: healthy and pre-diabetic adults | Standford, California 55 females and 51 males Age: 25–75 |
Understand how healthy individuals and those at risk of T2D, change over time, in response to perturbations and in relation to different molecules and microorganism | ↓ AMY2A, AMY2B, CPA1, and CUDZ1 | ↓ Complex sugar degradation | ||||||||
Longitudinal study (4 years) | SWATH-MS | Metagenomics, metatranscriptomics, and metabolomics | Limited studies of microbial changes. No information about diet and exercise. | Heterogeneous data. | 6 | Singh et al., 2017 | T1DM | Human urinary proteome | 1. LGR1 2. CD14 3. CPE 4. CTSB 5. CTSD 6. NAGA |
7. Fibronectin-1 8. Pancreatic α-amylase 9. MUC1 10. PTPRN |
1. ↑Inflammatory pathways (TGF-β) 3.↑AA degradation (↑urea production) | ||
Ferrer et al., 2013 | 2 subjects: 1 lean, 1 obese | Spain 1 female (lean) and 1 male (obese) | 8. | ↓ Exocrine pancreas functionality and ↓ complex sugar metabolism | |||||||||
Age: 15 | Identify and analyze active bacterial members and proteins expressed in lean and obese microbiota | Case-control | 1D-gel electrophoresis and UPLC-LTQ Orbitrap-MS/MS | Metagenetics | No information about dietary intake. | 7 | Zhong et al., 2019 | T2DM | Microbial | ||||
Kolmeder et al., 2015 | 29 subjects: 9 lean, 4 overweight, 16 obese | Spain 21 females | 1. PTS 2. ABC transporter 3. FMO3 (TMAO producing enzyme) |
8 males Age: 23.1 ± 2.2 (non-obese); 38.6 ± 2.4 (obese) | 4. Ferredoxin oxidoreductase 5. Bacterial ribosomal proteins |
1.↑Phosphorylation and transport of sugar in microbial cells 2.↑ |
Characterization of non-obese and obese fecal metaproteome | HDL cholesterol 3.↑TMAO synthesis |
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Case-control | 1D-gel electrophoresis RP-HPLC online coupled to MS/MS | Regular medication between obese and non-obese group. | 6 | Zhou et al., 2019 | T2M | Human | 1. IL-1RA 2. CRP 3. A1C |
1. | |||||
Sanchez-Carrillo et al., 2020 | 40 severely obese adults subjected to BS | Spain Age: 47–60 | ↓ | IL-1 | 2.↑immune defense mechanism |
Investigation the impact of bariatric surgery | 3.↑ glycaemia | ||||||
Longitudinal study (3 months) | LC-ESI-MS/MS analysis | Metabolomics | Results biased for using pooling strategy. | 6 | Ferrer et al., 2013 | Obesity | Microbial | 1. Glycoprotein containing FN3 2. Cobaltochelatases 3. B12-dependent methylmalonyl-CoA mutase 4. PduB 5. 3-hydroxybutyryl-CoA dehydratase | |||||
Hernandez et al., 2013 | 13 subjects: 2 adults (β-lactam-therapy), 7 obese adolescents, 5 lean adolescents | Germany Obese: 3 females and 4 males Lean: 2 males and 3 females Age: 13–16 | 6. Butyryl-CoA dehydrogenases 7. Acetyl-CoA acetyltransferases |
7. Pectate lyase 8. Aldose 1-epimerase 9. SOD 10. Pyridoxal biosynthesis lyases |
1.↑ Fibrin and proteoglycans 2.3. ↑ Vitamin B12 and propionate production 4.↑ Propanediol catabolism 5. Butyrate 10. ↓ Vitamin B6 |
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Evaluation of microbial shifts in relation to antibiotic treatment and obesity and measurement of carbohydrate activate enzymes | Cross-sectional | 96-well plates using a BioTek Synergy HT spectrometer in a colorimetric assay | No information about dietary intake. | Wide age range. | Small number of subjects. |
Kolmeder et al., 2015 | Obesity | Microbial | 1. α-glucosidase 2. Pectate lyase 3. Aminoacyl-histidine dipeptidase 4. Bacteroidetes proteins |
1.2. ↑Starch and pectin metabolism 3. ↑AA metabolism 4. ↑SCFAs |
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Human | 1. Trehalase (intestinal injury and inflammation marker) 2. Alkaline phosphatase (AP) 3. Serpins (serina protease inhibitors) 4. α-amylase |
1.↑ Threalosie 4.↑Starch digestion |
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6 | Sanchez-Carrillo et al., 2020 | Obesity | Microbial (pre-BS) | 1. Enzymes involved in gluconeogenesis (glyceraldehyde 3-phosphate dehydrogenase, pyruvate orthophosphate dikinase, PEP carboxykinase, fructose-bisphosphate aldolase, glutamate dehydrogenase) 2. Enzymes involved in Acetyl-CoA synthesis (Formate C-acetyltransferase, acetyl-CoA synthase, carbon-monoxide dehydrogenase) 3. Ferredoxin oxidoreductase |
4. Ferritin 5. Ferrous ion transport protein 6. Porphobilinogen synthase |
1.↑Pyruvate 2.↑ Acetyl-CoA (WL pathway) 4.5. ↓Iron synthesis |
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Microbial (post-BS) | 1. AdhE 2. OhyA 3. SOD and perodoxins (involved in maintenance of redox balance) |
1. ↑ Acetyl Acteyl-CoA → ethanol 1. Saturated fatty acid |
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Hernandez et al., 2013 | Obesity | Microbial | 1. α-polyglucose (refined carbohydrate digestion) 2. Proteins involved in pentose phosphate metabolism (PPP) 3. Proteins involved in TCA cycle |
1.2. ↑ Fructose, mannose, galactose, sucrose, starch, amino sugar, and nucleotide sugar metabolism 3. ↑ Via pentose phosphate |
Authors and Year | Size Sample and Characteristics | Subjects Characteristics (Sex, Age, Country) | Scope of Study | Study Design | Metaproteomics Techniques Used | Other “Omics” Techniques Used | Limitations |
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