High dynamic range characterization of the trauma patient plasma proteome

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this "divide-and-conquer" strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3,654 different proteins with 1,494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 "classic" cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2,910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1,553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.     

Assessing Statistical Significance in Microarray Experiments Using the Distance Between Microarrays

We propose permutation tests based on the pairwise distances between microarrays to compare location, variability, or equivalence of gene expression between two populations. For these tests the entire microarray or some pre-specified subset of genes is the unit of analysis. The pairwise distances only have to be computed once so the procedure is not computationally intensive despite the high dimensionality of the data. An R software package, permtest, implementing the method is freely available from the Comprehensive R Archive Network at http://cran.r-project.org.     

Survival analysis of longitudinal microarrays

MOTIVATION: The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays. RESULTS: We describe an approach for the survival analysis of longitudinal gene expression data. We construct a measure of association between the time to an event and gene expressions collected over time. Statistical significance is addressed using permutations and control of the false discovery rate. Our proposed method is illustrated on a dataset from a multi-center research study of inflammation and response to injury that aims to uncover the biological reasons why patients can have dramatically different outcomes after suffering a traumatic injury (www.gluegrant.org).     

Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.     

Online Training on Skin Cancer Diagnosis in Rheumatologists: Results from a Nationwide Randomized Web-Based Survey

Patients with inflammatory rheumatisms, such as rheumatoid arthritis, are more prone to develop skin cancers than the general population, with an additional increased incidence when receiving TNF blockers. There is therefore a need that physicians treating patients affected with inflammatory rheumatisms with TNF blockers recognize malignant skin lesions, requiring an urgent referral to the dermatologist and a potential withdrawal or modification of the immunomodulatory treatment. We aimed to demonstrate that an online training dedicated to skin tumors increase the abilities of rheumatologists to discriminate skin cancers from benign skin tumors. A nationwide randomized web-based survey involving 141 French rheumatologists was conducted. The baseline evaluation included short cases with skin lesion pictures and multiple choice questions assessing basic knowledge on skin cancers. For each case, rheumatologists had to indicate the nature of skin lesion (benign; premalignant/malignant), their level of confidence in this diagnosis (10-points Likert scale), and the precise dermatological diagnosis among 5 propositions. Different scores were established. After randomization, only one group had access to the online formation consisting in 4 e-learning modules on skin tumors, of 15 minutes each (online training group). After reevaluation, the trained and the non-trained group (control group) were compared. The primary end-point was the number of adequate diagnoses of the nature of the skin lesions. The mean number of adequate diagnosis for the benign versus premalignant/malignant nature of the lesions was higher in the online training group (13.4 vs. 11.2 points; p value <0.0001). While the other knowledge scores were also significantly higher, no statistical difference was observed on the level of self-confidence between the 2 groups. In conclusion, the online formation was effective to improve the rheumatologists’ ability to diagnose skin cancer.     

Pleiotropy among Common Genetic Loci Identified for Cardiometabolic Disorders and C-Reactive Protein

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10^-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.     

Association of Residual Plasma Viremia and Intima-Media Thickness in Antiretroviral-Treated Patients with Controlled Human Immunodeficiency Virus Infection

Background

While residual plasma viremia is commonly observed in HIV-infected patients undergoing antiretroviral treatment (ART), little is known about its subclinical consequences.

Methods

This cross-sectional study included 47 male, never-smoking, non-diabetic patients with ≥4 years of ART and controlled HIV-replication (HIV-viral load, VL <20 copies/mL for ≥1 year). Residual HIV-VL was measured using an ultrasensitive assay (quantification limit: 1 copy/ml). Patients were categorized as having detectable (D; 1-20 copies/mL, n = 14) or undetectable (UD; <1 copies/mL, n = 33) HIV-VL. Linear regression was used to model the difference in total carotid intima-media thickness [c-IMT, measures averaged across common carotid artery (cca), bifurcation, and internal carotid artery] and cca-IMT alone across detection groups. Multivariable models were constructed for each endpoint in a forward-stepwise approach.

Results

No significant differences were observed between viremia groups with respect to median ART-duration (9.6 years, IQR = 6.8–10.9), nadir CD4+T-cell (208/mm³, IQR = 143–378), and CD4+T-cell count (555/mm³, IQR = 458–707). Median adjusted inflammatory markers tended to be higher in patients with D- than UD-viremia, with differences in IL-10 being significant (p = 0.03). After adjustment on age, systolic blood pressure, and insulin resistance, mean cca-IMT was significantly lower in patients with undetectable (0.668 mm±0.010) versus detectable viremia (0.727 mm±0.015, p = 0.002). Cca-IMT was also independently associated with age and insulin resistance. Mean adjusted total c-IMT was no different between viremia groups (p = 0.2), however there was large variability in bifurcation c-IMT measurements.

Conclusions

Higher cca-IMT was observed in patients with detectable, compared to undetectable, HIV-VL in never-smoking ART-controlled patients, suggesting that residual HIV viremia may be linked to atherosclerosis.     

Temporal cytokine profiles in severely burned patients: a comparison of adults and children

A severe burn leads to hypermetabolism and catabolism resulting in compromised function and structural changes of essential organs. The release of cytokines has been implicated in this hypermetabolic response. The severity of the hypermetabolic response following burn injury increases with age, as does the mortality rate. Due to the relationship between the hypermetabolic and inflammatory responses, we sought to compare the plasma cytokine profiles following a severe burn in adults and in children. We enrolled 25 adults and 24 children who survived a flame burn covering more than 20% of total body surface area (TBSA). The concentrations of 22 cytokines were measured using the Linco multiplex array system (St. Charles, MO, USA). Large perturbations in the expression of pro- and anti-inflammatory cytokines were seen following thermal injury. During the first week following burn injury, IFN-gamma, IL-10, IL-17, IL-4, IL-6, and IL-8 were detected at significantly higher levels in adults compared with children, P < 0.05. Significant differences were measured during the second week post-burn for IL-1beta (higher in children) and IL-5 (higher in adults), P < 0.05. IL-18 was more abundant in children compared with adults during the third week post-burn, P < 0.05. Between post-burn d 21 and d 66, IL-1alpha was detected at higher concentrations in pediatric compared with adult patients, P < 0.05. Only GM-CSF expression was significantly different at all time points; it was detected at lower levels in pediatric patients, P < 0.05. Eotaxin, G-CSF, IL-13, IL-15, IP-10, MCP-1, and MIP-1alpha were detected at significantly different concentrations in adult compared with pediatric patients at multiple time points, P < 0.05. There were no differences in IL-12, IL-2, IL-7, or TNF levels in adult compared with pediatric burn patients at any of these time points. Following severe flame burns, the cytokine profiles in pediatric patients differ compared with those in adult patients, which may provide insight with respect to the higher morbidity rate in adults. Furthermore, the dramatic discrepancies observed in plasma cytokine detection between children and adults suggest that these two patient populations may benefit from different therapeutic interventions to achieve attenuation of the post-burn inflammatory response.     

Identification and interpretation of longitudinal gene expression changes in trauma

Rationale

The relationship between leukocyte gene expression and recovery of respiratory function after injury may provide information on the etiology of multiple organ dysfunction.

Objectives

To find a list of genes for which expression after injury predicts respiratory recovery, and to identify which networks and pathways characterize these genes.

Methods

Blood was sampled at 12 hours and at 1, 4, 7, 21 and 28 days from 147 patients who had been admitted to the hospital after blunt trauma. Leukocyte gene expression was measured using Affymetrix oligonucleotide arrays. A linear model, fit to each probe-set expression value, was used to impute the gene expression trajectory over the entire follow-up period. The proportional hazards model score test was used to calculate the statistical significance of each probe-set trajectory in predicting respiratory recovery. A list of genes was determined such that the expected proportion of false positive results was less than 10%. These genes were compared to the Gene Ontology for ‘response to stimulus’ and, using Ingenuity software, were mapped into networks and pathways.

Measurements and Main Results

The median time to respiratory recovery was 6 days. There were 170 probe-sets representing 135 genes that were found to be related to respiratory recovery. These genes could be mapped to nine networks. Two known pathways that were activated were antigen processing and presentation and JAK- signaling.

Conclusions

The examination of the relationship of gene expression over time with a patient''s clinical course can provide information which may be useful in determining the mechanism of recovery or lack of recovery after severe injury.