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Quantitative variability of 342 plasma proteins in a human twin population.

Liu Y, Buil A, Collins BC, Gillet LC, Blum LC, Cheng LY, Vitek O, Mouritsen J, Lachance G, Spector TD, Dermitzakis ET, Aebersold R - Mol. Syst. Biol. (2015)

Bottom Line: Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2-7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors.The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors.These results therefore have immediate implications for the effective design of blood-based biomarker studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland liu@imsb.biol.ethz.ch aebersold@imsb.biol.ethz.ch.

No MeSH data available.


Biological and biomedical insights derived from twin proteomic dataA Enrichment analysis of pathways and biological processes regulated by the four biological components was performed. The results were compiled into clusters according to the functional annotation of proteins.B Low correlation between plasma protein levels extracted from PeptideAtlas (www.peptideatlas.org) and their heritability contributing percentages in biological variance indicating the lack of an abundance bias. In contrast to that, concentration variability of more abundant proteins is generally less affected by longitudinal factors. The light red dashed line indicates the protein concentration of 1 μg/ml, which separates the proteins into two abundance classes.C Comparison of high-density lipoproteins (HDLs) and other proteins, using the heritability contributing percentages in biological variance of the plasma protein levels.D Comparison of heritability contributing percentages in biological variance between those proteins annotated as glycoproteins and other proteins. P-values are determined by Wilcoxon rank-sum test.E Decreasing trend of heritability control in plasma protein levels along with 5-year longitudinal process.F Clinically assayed proteins generally show lower quantitative variability compared to other plasma proteins with few exceptions, for example, CRP and APOA.G Examples of previously reported protein biomarker candidates, the plasma abundance levels of which were highly regulated by longitudinal effects. These include hexokinase-1 (HXK1), triosephosphate isomerase (TPIS), 14-3-3 protein zeta/delta (1433Z), platelet basic protein (CXCL7), monocyte differentiation antigen CD14 (CD14), biotinidase (BTD), serotransferrin (TRFE) and thyroxine-binding globulin (THBG).Data information: P-values are determined by Wilcoxon rank-sum test.
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fig04: Biological and biomedical insights derived from twin proteomic dataA Enrichment analysis of pathways and biological processes regulated by the four biological components was performed. The results were compiled into clusters according to the functional annotation of proteins.B Low correlation between plasma protein levels extracted from PeptideAtlas (www.peptideatlas.org) and their heritability contributing percentages in biological variance indicating the lack of an abundance bias. In contrast to that, concentration variability of more abundant proteins is generally less affected by longitudinal factors. The light red dashed line indicates the protein concentration of 1 μg/ml, which separates the proteins into two abundance classes.C Comparison of high-density lipoproteins (HDLs) and other proteins, using the heritability contributing percentages in biological variance of the plasma protein levels.D Comparison of heritability contributing percentages in biological variance between those proteins annotated as glycoproteins and other proteins. P-values are determined by Wilcoxon rank-sum test.E Decreasing trend of heritability control in plasma protein levels along with 5-year longitudinal process.F Clinically assayed proteins generally show lower quantitative variability compared to other plasma proteins with few exceptions, for example, CRP and APOA.G Examples of previously reported protein biomarker candidates, the plasma abundance levels of which were highly regulated by longitudinal effects. These include hexokinase-1 (HXK1), triosephosphate isomerase (TPIS), 14-3-3 protein zeta/delta (1433Z), platelet basic protein (CXCL7), monocyte differentiation antigen CD14 (CD14), biotinidase (BTD), serotransferrin (TRFE) and thyroxine-binding globulin (THBG).Data information: P-values are determined by Wilcoxon rank-sum test.

Mentions: Statistically significant heritability was observed for 80 proteins (i.e. 23% of 342, h2 > 0.25 or P < 0.01). This percentage is close to the result of Johansson et al (2013) who measured plasma samples in the parent–children context and thereby determined the abundance levels of 19% of the plasma peptides to be heritable. We confirmed the high heritability of protein level for 21 of the proteins discovered by Johansson et al (2013). Additionally, we determined 60 plasma proteins, the level of which was closely associated with longitudinal changes, 52 with familial environment and 47 with individual environment. Among these, 17 proteins appeared to be regulated by both familial and individual environments. To discern the biological processes associated with the four biological sources of variability, we annotated the protein lists by Gene Ontology (GO) and pathway enrichment analysis. This analysis identified several protein functional clusters that are significantly affected by either heritability, environment or the longitudinal effects (Fig4A). For example, a cluster of immune response proteins, consisting of proteins related to the innate immune response and inflammatory regulation (P-values between P = 0.00032 and P = 2.60e-6 for the enrichment significance in all relevant functional processes), the blood coagulation cluster (P-values between P = 0.035 and P = 0.00019) and a protein-processing cluster (P-values between P = 0.040 and P = 1.33e-6), were found to be more strongly heritable or familial than associating with individual environment and aging factors. Moreover, the clusters of proteins related to body fluid regulation (P-values between P = 0.053 and P = 1.16e-5), lipid metabolism (P-values between P = 0.065 and P = 0.00050) and protein secretion (P-values between P = 0.021 and P = 1.53e-12) were found to be not only heritable but also heavily interacting with individual environment. Interestingly, the functional cluster of hormone response was under tight regulation of the longitudinal effects (P-values between P = 0.030 and P = 0.016). These results are consistent with and extend previous literature reports. For example, Souto et al (2000) showed that the blood coagulation and fibrinolysis pathways are strongly determined by genetic factors in Spanish families, and Snieder et al (1999) noted the importance of genetic dependency of lipid system. Taken together, the twin proteomic data reveal that different biological processes are regulated by genetic control, and environmental or longitudinal factors to different degrees.


Quantitative variability of 342 plasma proteins in a human twin population.

Liu Y, Buil A, Collins BC, Gillet LC, Blum LC, Cheng LY, Vitek O, Mouritsen J, Lachance G, Spector TD, Dermitzakis ET, Aebersold R - Mol. Syst. Biol. (2015)

Biological and biomedical insights derived from twin proteomic dataA Enrichment analysis of pathways and biological processes regulated by the four biological components was performed. The results were compiled into clusters according to the functional annotation of proteins.B Low correlation between plasma protein levels extracted from PeptideAtlas (www.peptideatlas.org) and their heritability contributing percentages in biological variance indicating the lack of an abundance bias. In contrast to that, concentration variability of more abundant proteins is generally less affected by longitudinal factors. The light red dashed line indicates the protein concentration of 1 μg/ml, which separates the proteins into two abundance classes.C Comparison of high-density lipoproteins (HDLs) and other proteins, using the heritability contributing percentages in biological variance of the plasma protein levels.D Comparison of heritability contributing percentages in biological variance between those proteins annotated as glycoproteins and other proteins. P-values are determined by Wilcoxon rank-sum test.E Decreasing trend of heritability control in plasma protein levels along with 5-year longitudinal process.F Clinically assayed proteins generally show lower quantitative variability compared to other plasma proteins with few exceptions, for example, CRP and APOA.G Examples of previously reported protein biomarker candidates, the plasma abundance levels of which were highly regulated by longitudinal effects. These include hexokinase-1 (HXK1), triosephosphate isomerase (TPIS), 14-3-3 protein zeta/delta (1433Z), platelet basic protein (CXCL7), monocyte differentiation antigen CD14 (CD14), biotinidase (BTD), serotransferrin (TRFE) and thyroxine-binding globulin (THBG).Data information: P-values are determined by Wilcoxon rank-sum test.
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fig04: Biological and biomedical insights derived from twin proteomic dataA Enrichment analysis of pathways and biological processes regulated by the four biological components was performed. The results were compiled into clusters according to the functional annotation of proteins.B Low correlation between plasma protein levels extracted from PeptideAtlas (www.peptideatlas.org) and their heritability contributing percentages in biological variance indicating the lack of an abundance bias. In contrast to that, concentration variability of more abundant proteins is generally less affected by longitudinal factors. The light red dashed line indicates the protein concentration of 1 μg/ml, which separates the proteins into two abundance classes.C Comparison of high-density lipoproteins (HDLs) and other proteins, using the heritability contributing percentages in biological variance of the plasma protein levels.D Comparison of heritability contributing percentages in biological variance between those proteins annotated as glycoproteins and other proteins. P-values are determined by Wilcoxon rank-sum test.E Decreasing trend of heritability control in plasma protein levels along with 5-year longitudinal process.F Clinically assayed proteins generally show lower quantitative variability compared to other plasma proteins with few exceptions, for example, CRP and APOA.G Examples of previously reported protein biomarker candidates, the plasma abundance levels of which were highly regulated by longitudinal effects. These include hexokinase-1 (HXK1), triosephosphate isomerase (TPIS), 14-3-3 protein zeta/delta (1433Z), platelet basic protein (CXCL7), monocyte differentiation antigen CD14 (CD14), biotinidase (BTD), serotransferrin (TRFE) and thyroxine-binding globulin (THBG).Data information: P-values are determined by Wilcoxon rank-sum test.
Mentions: Statistically significant heritability was observed for 80 proteins (i.e. 23% of 342, h2 > 0.25 or P < 0.01). This percentage is close to the result of Johansson et al (2013) who measured plasma samples in the parent–children context and thereby determined the abundance levels of 19% of the plasma peptides to be heritable. We confirmed the high heritability of protein level for 21 of the proteins discovered by Johansson et al (2013). Additionally, we determined 60 plasma proteins, the level of which was closely associated with longitudinal changes, 52 with familial environment and 47 with individual environment. Among these, 17 proteins appeared to be regulated by both familial and individual environments. To discern the biological processes associated with the four biological sources of variability, we annotated the protein lists by Gene Ontology (GO) and pathway enrichment analysis. This analysis identified several protein functional clusters that are significantly affected by either heritability, environment or the longitudinal effects (Fig4A). For example, a cluster of immune response proteins, consisting of proteins related to the innate immune response and inflammatory regulation (P-values between P = 0.00032 and P = 2.60e-6 for the enrichment significance in all relevant functional processes), the blood coagulation cluster (P-values between P = 0.035 and P = 0.00019) and a protein-processing cluster (P-values between P = 0.040 and P = 1.33e-6), were found to be more strongly heritable or familial than associating with individual environment and aging factors. Moreover, the clusters of proteins related to body fluid regulation (P-values between P = 0.053 and P = 1.16e-5), lipid metabolism (P-values between P = 0.065 and P = 0.00050) and protein secretion (P-values between P = 0.021 and P = 1.53e-12) were found to be not only heritable but also heavily interacting with individual environment. Interestingly, the functional cluster of hormone response was under tight regulation of the longitudinal effects (P-values between P = 0.030 and P = 0.016). These results are consistent with and extend previous literature reports. For example, Souto et al (2000) showed that the blood coagulation and fibrinolysis pathways are strongly determined by genetic factors in Spanish families, and Snieder et al (1999) noted the importance of genetic dependency of lipid system. Taken together, the twin proteomic data reveal that different biological processes are regulated by genetic control, and environmental or longitudinal factors to different degrees.

Bottom Line: Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2-7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors.The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors.These results therefore have immediate implications for the effective design of blood-based biomarker studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland liu@imsb.biol.ethz.ch aebersold@imsb.biol.ethz.ch.

No MeSH data available.