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Respiratory Mucosal Proteome Quantification in Human Influenza Infections.

Marion T, Elbahesh H, Thomas PG, DeVincenzo JP, Webby R, Schughart K - PLoS ONE (2016)

Bottom Line: Our results illustrate the utility of micro-proteomic technology for analysis of proteins in small volumes of respiratory mucosal samples.Most of the identified proteins were associated with the host immune response to infection, and changes in protein levels of 151 of the DEPs were significantly correlated with viral load.It establishes a precedent for micro-proteomic quantification of proteins that reflect ongoing response to respiratory infection.

View Article: PubMed Central - PubMed

Affiliation: University of Tennessee Health Science Center, Department of Microbiology, Immunology and Biochemistry, Memphis, United States of America.

ABSTRACT
Respiratory influenza virus infections represent a serious threat to human health. Underlying medical conditions and genetic make-up predispose some influenza patients to more severe forms of disease. To date, only a few studies have been performed in patients to correlate a selected group of cytokines and chemokines with influenza infection. Therefore, we evaluated the potential of a novel multiplex micro-proteomics technology, SOMAscan, to quantify proteins in the respiratory mucosa of influenza A and B infected individuals. The analysis included but was not limited to quantification of cytokines and chemokines detected in previous studies. SOMAscan quantified more than 1,000 secreted proteins in small nasal wash volumes from infected and healthy individuals. Our results illustrate the utility of micro-proteomic technology for analysis of proteins in small volumes of respiratory mucosal samples. Furthermore, when we compared nasal wash samples from influenza-infected patients with viral load ≥ 2(8) and increased IL-6 and CXCL10 to healthy controls, we identified 162 differentially-expressed proteins between the two groups. This number greatly exceeds the number of DEPs identified in previous studies in human influenza patients. Most of the identified proteins were associated with the host immune response to infection, and changes in protein levels of 151 of the DEPs were significantly correlated with viral load. Most important, SOMAscan identified differentially expressed proteins heretofore not associated with respiratory influenza infection in humans. Our study is the first report for the use of SOMAscan to screen nasal secretions. It establishes a precedent for micro-proteomic quantification of proteins that reflect ongoing response to respiratory infection.

No MeSH data available.


Related in: MedlinePlus

PCA analysis of normalized protein expression values.Principle component analysis (PCA) was performed with quantile normalized log2–transformed protein expression values from nasal washes for all 24 samples. The first two principal components are shown representing 24% and 17%, respectively, of the total variation. Healthy controls are labeled gray and IAV-positive samples (in which influenza A or B was detected by PCR) are labeled red. In addition, sample identities (e.g. ID_4043) are shown. Horizontal and vertical axis represent principle component 1 and 2, respectively.
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pone.0153674.g001: PCA analysis of normalized protein expression values.Principle component analysis (PCA) was performed with quantile normalized log2–transformed protein expression values from nasal washes for all 24 samples. The first two principal components are shown representing 24% and 17%, respectively, of the total variation. Healthy controls are labeled gray and IAV-positive samples (in which influenza A or B was detected by PCR) are labeled red. In addition, sample identities (e.g. ID_4043) are shown. Horizontal and vertical axis represent principle component 1 and 2, respectively.

Mentions: The nasal washes were analyzed with the 1.2k version of SOMAscan with 1,129 SOMAmers that simultaneously quantified 1,030 different human proteins [24]. Quantitative expression signals were log2-transformed and quantile-normalized and then examined by principle component analysis (PCA) to visualize variation and grouping of samples. PCA is a mathematical transformation that reduces variation in a large data set to a few dimensions that project differences determined by the strongest variables. The first two dimensions that represent the highest variations of a PCA analysis can be visualized in a 2D plot. In this way, samples that are most disparate with respect to protein expression levels are found more distantly located from each other in the 2D PCA plot, and samples with similar protein expression levels are closer to each other in the plot. As shown in Fig 1, most infected patients (red circles) segregated together indicating that their protein expression levels were similar. Most influenza virus-positive patients segregated to the right of PCA1 = 0 suggesting that these patients had similar changes in their mucosal proteome compared to others in the cohort. The healthy controls (grey circles) segregated to the left except for ID_3058 that segregated with the leftmost group of virus-infected patients. Three patients who were diagnosed as virus-positive (red circles), based on qRT-PCR [28], segregated with healthy controls (grey circles). Patient ID_3045 segregated independently of all other patients. The imperfect PCA distribution of infected versus healthy patients is in part due to the small sample size but also reflects the large uncontrollable heterogeneity which is an intrinsic property of human cohorts. Here, many confounding factors, such as adverse health conditions, time after infection, genetics, and general environment, may influence mucosal protein expression levels and are difficult or impossible to control.


Respiratory Mucosal Proteome Quantification in Human Influenza Infections.

Marion T, Elbahesh H, Thomas PG, DeVincenzo JP, Webby R, Schughart K - PLoS ONE (2016)

PCA analysis of normalized protein expression values.Principle component analysis (PCA) was performed with quantile normalized log2–transformed protein expression values from nasal washes for all 24 samples. The first two principal components are shown representing 24% and 17%, respectively, of the total variation. Healthy controls are labeled gray and IAV-positive samples (in which influenza A or B was detected by PCR) are labeled red. In addition, sample identities (e.g. ID_4043) are shown. Horizontal and vertical axis represent principle component 1 and 2, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835085&req=5

pone.0153674.g001: PCA analysis of normalized protein expression values.Principle component analysis (PCA) was performed with quantile normalized log2–transformed protein expression values from nasal washes for all 24 samples. The first two principal components are shown representing 24% and 17%, respectively, of the total variation. Healthy controls are labeled gray and IAV-positive samples (in which influenza A or B was detected by PCR) are labeled red. In addition, sample identities (e.g. ID_4043) are shown. Horizontal and vertical axis represent principle component 1 and 2, respectively.
Mentions: The nasal washes were analyzed with the 1.2k version of SOMAscan with 1,129 SOMAmers that simultaneously quantified 1,030 different human proteins [24]. Quantitative expression signals were log2-transformed and quantile-normalized and then examined by principle component analysis (PCA) to visualize variation and grouping of samples. PCA is a mathematical transformation that reduces variation in a large data set to a few dimensions that project differences determined by the strongest variables. The first two dimensions that represent the highest variations of a PCA analysis can be visualized in a 2D plot. In this way, samples that are most disparate with respect to protein expression levels are found more distantly located from each other in the 2D PCA plot, and samples with similar protein expression levels are closer to each other in the plot. As shown in Fig 1, most infected patients (red circles) segregated together indicating that their protein expression levels were similar. Most influenza virus-positive patients segregated to the right of PCA1 = 0 suggesting that these patients had similar changes in their mucosal proteome compared to others in the cohort. The healthy controls (grey circles) segregated to the left except for ID_3058 that segregated with the leftmost group of virus-infected patients. Three patients who were diagnosed as virus-positive (red circles), based on qRT-PCR [28], segregated with healthy controls (grey circles). Patient ID_3045 segregated independently of all other patients. The imperfect PCA distribution of infected versus healthy patients is in part due to the small sample size but also reflects the large uncontrollable heterogeneity which is an intrinsic property of human cohorts. Here, many confounding factors, such as adverse health conditions, time after infection, genetics, and general environment, may influence mucosal protein expression levels and are difficult or impossible to control.

Bottom Line: Our results illustrate the utility of micro-proteomic technology for analysis of proteins in small volumes of respiratory mucosal samples.Most of the identified proteins were associated with the host immune response to infection, and changes in protein levels of 151 of the DEPs were significantly correlated with viral load.It establishes a precedent for micro-proteomic quantification of proteins that reflect ongoing response to respiratory infection.

View Article: PubMed Central - PubMed

Affiliation: University of Tennessee Health Science Center, Department of Microbiology, Immunology and Biochemistry, Memphis, United States of America.

ABSTRACT
Respiratory influenza virus infections represent a serious threat to human health. Underlying medical conditions and genetic make-up predispose some influenza patients to more severe forms of disease. To date, only a few studies have been performed in patients to correlate a selected group of cytokines and chemokines with influenza infection. Therefore, we evaluated the potential of a novel multiplex micro-proteomics technology, SOMAscan, to quantify proteins in the respiratory mucosa of influenza A and B infected individuals. The analysis included but was not limited to quantification of cytokines and chemokines detected in previous studies. SOMAscan quantified more than 1,000 secreted proteins in small nasal wash volumes from infected and healthy individuals. Our results illustrate the utility of micro-proteomic technology for analysis of proteins in small volumes of respiratory mucosal samples. Furthermore, when we compared nasal wash samples from influenza-infected patients with viral load ≥ 2(8) and increased IL-6 and CXCL10 to healthy controls, we identified 162 differentially-expressed proteins between the two groups. This number greatly exceeds the number of DEPs identified in previous studies in human influenza patients. Most of the identified proteins were associated with the host immune response to infection, and changes in protein levels of 151 of the DEPs were significantly correlated with viral load. Most important, SOMAscan identified differentially expressed proteins heretofore not associated with respiratory influenza infection in humans. Our study is the first report for the use of SOMAscan to screen nasal secretions. It establishes a precedent for micro-proteomic quantification of proteins that reflect ongoing response to respiratory infection.

No MeSH data available.


Related in: MedlinePlus