Napredna pretraga

Pregled bibliografske jedinice broj: 895285

Pretreatment strategies for mining the serum proteome in canine babesiosis by TMT-based quantitative proteomic analysis


Horvatić, Anita; Bilić, Petra; Kuleš, Josipa; Gullemin, Nicolas; Galan, Asier; Mrljak, Vladimir; Eckersall, David
Pretreatment strategies for mining the serum proteome in canine babesiosis by TMT-based quantitative proteomic analysis // Proceedings of the 11th Central and Eastern European Proteomic Conference
Košice, 2017. (poster, međunarodna recenzija, sažetak, znanstveni)


Naslov
Pretreatment strategies for mining the serum proteome in canine babesiosis by TMT-based quantitative proteomic analysis

Autori
Horvatić, Anita ; Bilić, Petra ; Kuleš, Josipa ; Gullemin, Nicolas ; Galan, Asier ; Mrljak, Vladimir ; Eckersall, David

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Proceedings of the 11th Central and Eastern European Proteomic Conference / - Košice, 2017

ISBN
978-80-972017-5-3

Skup
The 11th Central and Eastern European Proteomic Conference, CEEPC 2017

Mjesto i datum
Košice, Slovačka, 27.-29.09.2017

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Babesiosis, depletion, serum pretreatment, TMT, quantitative proteomics

Sažetak
Serum proteome is an invaluable source of prognostic and diagnostic disease biomarkers. However, due to its complexity and wide dynamic range, serum proteomics presents a major analytical challenge. The objective of our study was to determine the optimal pretreatment strategy for canine serum samples derived from healthy and Babesia-infected dogs that yields the most effective results in terms of quantifiable proteins using tandem mass tag (TMT)-based shotgun proteomics. We compared non-depleted to albumin depleted, combined albumin and IgG depleted and ProteoMiner treated serum samples. Protein identities and TMT-based relative quantification data were obtained using Proteome Discoverer mining Canis lupus familiaris NCBI fasta files for at least 2 unique peptides and 5% FDR. Bioinformatic analysis was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The greatest number of quantifiable proteins was identified after ProteoMiner pretreatment (317), 27% more compared to non-depleted samples (247). Albumin and combined albumin and IgG depletion resulted in lower number of identified proteins (234 and 223, respectively) compared to non-depleted serum sample. Apparently, the effect of depletion was weakened due to non-specific binding and co-precipitation of serum proteins during the depletion procedure. To test the best pretreatment strategy, six samples (healthy versus Babesia-infected canine serum) were analyzed before and after ProteoMiner treatment. Obtained results showed that ProteoMiner treatment enabled identification of 48 significantly deregulated proteins (p<0.05) present at 1.4-fold or greater concentrations (as well as 0.6-fold or lower), whereas the number of significantly deregulated proteins in non-treated samples reached 30. Subsequent functional annotation and pathway analyses of deregulated proteins indicate that the protein abundance equalization using ProteoMiner in combination with TMT-based shotgun proteomic analysis has an advantage of detection and quantification of low abundant proteins involved in different pathophysiological processes that occur in canine babesiosis and can be applied for identification of potential biomarkers of this parasitic disease. Acknowledgements: This work was supported by VetMedZg (#621394, European Commission) and BioDog (#4135, Croatian Science Foundation) projects.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Veterinarska medicina



POVEZANOST RADA


Ustanove
Veterinarski fakultet, Zagreb