Table 1.
Tool | Sample | Resulting data | Ref. |
---|---|---|---|
2DE silver MALDI-TOF | Plasma proteins/blood-sera in ob/ob mice that are obese because of the lack of leptin | EPS is a potent gene expression regulator (in ob/ob mice) in obesity, insulin resistance and DM. Ferritin and adiponectin as important factors for future DM2. | 33 |
Expression level of Apo A-I, IV, C-III, E, retinol-binding protein 4 and transferrin were shown to be altered and their levels are normalized after EPS treatment. | |||
Resistin is up-regulated while adiponectin is down-regulated in diabetes and obesity. | |||
2DE-DIGE MALDI-TOF | Adipose tissue | 9 higher expressed proteins in the adipocytes from old compared to young obese patients: | 92 |
Prohibitin 1 | |||
Protein disulphide isomerase A3 | |||
Beta actin | |||
Profilin | |||
Aldo-ketoreductase 1 C2 | |||
Alpha crystallin B | |||
Anexins A1, A5, A6 | |||
4 lower expressed proteins in the adipocytes from old compared to young obese patients: | |||
Keratin type 2 cytoskeletal 1 | |||
Keratin type 2 cytoskeletal 10 | |||
Haemoglobins A, B | |||
Signal transducer and activator of transcription 3 as the central molecule in the connectivity map and the apoptosis pathway | |||
iTRAQ LC-MS/MS | Heart tissue | 29 proteins up-regulated from a total of 1.627, while 84 were down-regulated in the db/db mice compared with the control group | 93 |
Calnexin was found to be decreased whereas integrin-linked protein kinase was decreased in the phlorizin treated DM group compared with the DM group | |||
SDS-PAGE LC-MS/MS/MS LTQ-FT ELISA | Subcellular fractionation of the mouse preadipocyte cell line 3T3-L1 with and without insulin treatment into cytosol, membrane, mitochondria and nuclear fractions and nuclear fractions | 3.287 identified proteins that form part of the adipocyte proteome | 94,95 |
Genetically modified animal models (bGH, GHA and GHR−/− mice and tissue-samples | Useful information to unravel the complexity of the adipocyte in obesity | ||
Addressed that adiponectin is generally negatively associated with GH activity, regardless of age | |||
Useful information about the associations of total and HMW adiponectin with insulin sensitivity and longevity | |||
Circulating adiponectin levels correlated strongly with inguinal fat mass, implying the effects of GH on adiponectin are depot-specific | |||
Phosphoproteomics SILAC anti-pY immunoprecipitation | Brown adipocytes | From the 40 insulin effectors identified, 7 (SDR, PKC binding protein, LRP-6 and PISP/PDZK11, a potential calcium ATPases binding protein | 96 |
2DE-gels stained by Sypro-Ruby | Platelet-free plasma from the patients | 53 differentially spot-proteins from which 51% were shown to be down-regulated comparing Vit D deficiency | 97 |
The HMW form of adiponectin is down-regulated in obese paediatric patients with Vit D deficiency | |||
Thrombospondin 1 (TSP1) is up-regulated while histone deacetylase 4 (HDAC4) is down-regulated | |||
SCX MS/MS | Peripheral blood mononuclear cells | TSP1 and HDAC4 recover their normal expression level due to physical exercises | 98 |
2DE-gels LC-MS/MS MALDI-TOF MS | Liver sample | A diet rich in n-3PUFA decreases the expression of regucalcin, aldehyde dehydrogenase | 99 |
A diet rich in n-3 PUFA increases | |||
the expression of a POLI protein-A-1, S-adenosylmethionine synthase, fructose 1,6 biphosphatase, ketohexokinase, malate dehydrogenase, GTP-specific succinyl CoAsynthase, Ornithine aminotransferase, protein disulfide isomerase A3 | |||
2DE-gels MALDI-TOF MS and MS/MS | Human subcutaneous (SQ) and white adipose tissue (WAT) | The levels of several proteins in human SQ-WAT are not homogeneous between different WAT depots | 100 |
Twenty-one proteins showed differential intensities among the six defined anatomical locations, and 14 between the superficial and the deep layer (such as vimentin, heat-shock proteins, superoxide-dismutase, fatty acid-binding protein, alpha-enolase, ATP-synthase among others) | |||
2DE-DIGE MALDI-TOF MS and MS/MS | Visceral adipose tissue (VAT) from pre-obese diabetic patients | The presence of diabetes influences the VAT abundance of several proteins | 83 |
Diabetic patients showed increased VAT abundance of glutathione S-transferase Mu 2, peroxiredoxin-2, antithrombin-III, apolipoprotein A-IV, Ig κ chain C region, mitochondrial aldehyde dehydrogenase and actin, and decreased abundance of annexin-A1, retinaldehyde dehydrogenase-1 and vinculin, compared with their non-diabetic counterparts. | |||
Label-free quantitative proteomics | Salivary samples from patients with diabetes | This study demonstrates that differences exist between salivary proteomic profiles in patients with diabetes based on the A1C levels | 84 |
2DE MALDI-TOF MS and MS/MS | Serum samples from obese children | This research study establishes the bases of the utility of proteomics to assess clinical improvements in obesity. | 85 |
Down-regulated proteins in obese patients: transthyretin apolipoprotein-A1, apo-J/clusterin and vitamin D binding protein. | |||
ApoA1 was further down-regulated under the presence of up-regulation insulin resistance, whereas weight reduction induced its up-regulation. | |||
Apolipoprotein-A1 and haptoglobin were validated via ELISA as true potential candidate biomarkers |
Representative assays detailing in each column –the goals, technologies, type of sample to be analysed and the resulting data– are placed schematically in this table. 2DE-electrophoresis is one the most common tools used in diabetes and obesity research studies when using proteomics. Nevertheless, currently, more scientific articles are appearing and showing the advantages when applying HPLC or nano-HPLC coupled directly to mass spectrometry (LC-MS) to avoid losing low expressed proteins or putative biomarkers. Biomarkers, adipocyte and insulin proteomes have been the most common goals followed by scientists to unravel diabetes and obesity pathologies. All of them –and many others– allowed us to advance and establish the right platforms and current technology-innovations will permit improve diagnoses and refine therapies via identifying new biomarkers by proteomics.