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Comparative proteomics: assessment of biological variability and dataset comparability

BACKGROUND: Comparative proteomics in bacteria are often hampered by the differential nature of dataset quality and/or inherent biological deviations. Although common practice compensates by reproducing and normalizing datasets from a single sample, the degree of certainty is limited in comparison o...

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Autores principales: Kim, Sa Rang, Nguyen, Tuong Vi, Seo, Na Ri, Jung, Seunghup, An, Hyun Joo, Mills, David A, Kim, Jae Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704264/
https://www.ncbi.nlm.nih.gov/pubmed/25888384
http://dx.doi.org/10.1186/s12859-015-0561-9
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author Kim, Sa Rang
Nguyen, Tuong Vi
Seo, Na Ri
Jung, Seunghup
An, Hyun Joo
Mills, David A
Kim, Jae Han
author_facet Kim, Sa Rang
Nguyen, Tuong Vi
Seo, Na Ri
Jung, Seunghup
An, Hyun Joo
Mills, David A
Kim, Jae Han
author_sort Kim, Sa Rang
collection PubMed
description BACKGROUND: Comparative proteomics in bacteria are often hampered by the differential nature of dataset quality and/or inherent biological deviations. Although common practice compensates by reproducing and normalizing datasets from a single sample, the degree of certainty is limited in comparison of multiple dataset. To surmount these limitations, we introduce a two-step assessment criterion using: (1) the relative number of total spectra (R (TS)) to determine if two LC-MS/MS datasets are comparable and (2) nine glycolytic enzymes as internal standards for a more accurate calculation of relative amount of proteins. Lactococcus lactis HR279 and JHK24 strains expressing high or low levels (respectively) of green fluorescent protein (GFP) were used for the model system. GFP abundance was determined by spectral counting and direct fluorescence measurements. Statistical analysis determined relative GFP quantity obtained from our approach matched values obtained from fluorescence measurements. RESULTS: L. lactis HR279 and JHK24 demonstrates two datasets with an R (TS) value less than 1.4 accurately reflects relative differences in GFP levels between high and low expression strains. Without prior consideration of R (TS) and the use of internal standards, the relative increase in GFP calculated by spectral counting method was 3.92 ± 1.14 fold, which is not correlated with the value determined by the direct fluorescence measurement (2.86 ± 0.42 fold) with the p = 0.024. In contrast, 2.88 ± 0.92 fold was obtained by our approach showing a statistically insignificant difference (p = 0.95). CONCLUSIONS: Our two-step assessment demonstrates a useful approach to: (1) validate the comparability of two mass spectrometric datasets and (2) accurately calculate the relative amount of proteins between proteomic datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0561-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-47042642016-01-08 Comparative proteomics: assessment of biological variability and dataset comparability Kim, Sa Rang Nguyen, Tuong Vi Seo, Na Ri Jung, Seunghup An, Hyun Joo Mills, David A Kim, Jae Han BMC Bioinformatics Research Article BACKGROUND: Comparative proteomics in bacteria are often hampered by the differential nature of dataset quality and/or inherent biological deviations. Although common practice compensates by reproducing and normalizing datasets from a single sample, the degree of certainty is limited in comparison of multiple dataset. To surmount these limitations, we introduce a two-step assessment criterion using: (1) the relative number of total spectra (R (TS)) to determine if two LC-MS/MS datasets are comparable and (2) nine glycolytic enzymes as internal standards for a more accurate calculation of relative amount of proteins. Lactococcus lactis HR279 and JHK24 strains expressing high or low levels (respectively) of green fluorescent protein (GFP) were used for the model system. GFP abundance was determined by spectral counting and direct fluorescence measurements. Statistical analysis determined relative GFP quantity obtained from our approach matched values obtained from fluorescence measurements. RESULTS: L. lactis HR279 and JHK24 demonstrates two datasets with an R (TS) value less than 1.4 accurately reflects relative differences in GFP levels between high and low expression strains. Without prior consideration of R (TS) and the use of internal standards, the relative increase in GFP calculated by spectral counting method was 3.92 ± 1.14 fold, which is not correlated with the value determined by the direct fluorescence measurement (2.86 ± 0.42 fold) with the p = 0.024. In contrast, 2.88 ± 0.92 fold was obtained by our approach showing a statistically insignificant difference (p = 0.95). CONCLUSIONS: Our two-step assessment demonstrates a useful approach to: (1) validate the comparability of two mass spectrometric datasets and (2) accurately calculate the relative amount of proteins between proteomic datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0561-9) contains supplementary material, which is available to authorized users. BioMed Central 2015-04-17 /pmc/articles/PMC4704264/ /pubmed/25888384 http://dx.doi.org/10.1186/s12859-015-0561-9 Text en © Kim et al.; licensee BioMed Central. 2016 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Kim, Sa Rang
Nguyen, Tuong Vi
Seo, Na Ri
Jung, Seunghup
An, Hyun Joo
Mills, David A
Kim, Jae Han
Comparative proteomics: assessment of biological variability and dataset comparability
title Comparative proteomics: assessment of biological variability and dataset comparability
title_full Comparative proteomics: assessment of biological variability and dataset comparability
title_fullStr Comparative proteomics: assessment of biological variability and dataset comparability
title_full_unstemmed Comparative proteomics: assessment of biological variability and dataset comparability
title_short Comparative proteomics: assessment of biological variability and dataset comparability
title_sort comparative proteomics: assessment of biological variability and dataset comparability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704264/
https://www.ncbi.nlm.nih.gov/pubmed/25888384
http://dx.doi.org/10.1186/s12859-015-0561-9
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