Antibiotics taken within the first year of life are linked to infant gut microbiome disruption and elevated atopic dermatitis risk.

BACKGROUND: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease in both pediatric and adult populations. The development of AD has been linked to antibiotic usage, which causes perturbation of the microbiome and has been associated with abnormal immune system function. However, imbalances in the gut microbiome itself associated with antibiotic usage have been inconsistently linked to AD. OBJECTIVES: This study aimed to elucidate the timing and specific factors mediating the relationship between systemic (oral or intravenous) antibiotic usage and AD. METHODS: We used statistical modeling and differential analysis to link CHILD Cohort Study participants' history of antibiotic usage and early-life gut microbiome alterations to AD. RESULTS: Here we report that systemic antibiotics during the first year of life, as compared to later, are associated with AD risk (adjusted odds ratio [aOR] = 1.81; 95% CI: 1.28-2.57; P < .001), with an increased number of antibiotic courses corresponding to a dose response-like increased risk of AD risk (1 course: aOR: 1.67; 95% CI: 1.17-2.38; 2 or more courses: aOR: 2.16; 95% CI: 1.30-3.59). Further, we demonstrate that microbiome alterations associated with both AD and systemic antibiotic usage fully mediate the effect of antibiotic usage on the development of AD (ßindirect = 0.072; P < .001). Alterations in the 1-year infant gut microbiome of participants who would later develop AD included increased Tyzzerella nexilis, increased monosaccharide utilization, and parallel decreased Bifidobacterium and Eubacterium spp, and fermentative pathways. CONCLUSIONS: These findings indicate that early-life antibiotic usage, especially in the first year of life, modulates key gut microbiome components that may be used as markers to predict and possibly prevent the development of AD.

Delayed gut microbiota maturation in the first year of life is a hallmark of pediatric allergic disease.

Allergic diseases affect millions of people worldwide. An increase in their prevalence has been associated with alterations in the gut microbiome, i.e., the microorganisms and their genes within the gastrointestinal tract. Maturation of the infant immune system and gut microbiota occur in parallel; thus, the conformation of the microbiome may determine if tolerant immune programming arises within the infant. Here we show, using deeply phenotyped participants in the CHILD birth cohort (n = 1115), that there are early-life influences and microbiome features which are uniformly associated with four distinct allergic diagnoses at 5 years: atopic dermatitis (AD, n = 367), asthma (As, n = 165), food allergy (FA, n = 136), and allergic rhinitis (AR, n = 187). In a subset with shotgun metagenomic and metabolomic profiling (n = 589), we discover that impaired 1-year microbiota maturation may be universal to pediatric allergies (AD p = 0.000014; As p = 0.0073; FA p = 0.00083; and AR p = 0.0021). Extending this, we find a core set of functional and metabolic imbalances characterized by compromised mucous integrity, elevated oxidative activity, decreased secondary fermentation, and elevated trace amines, to be a significant mediator between microbiota maturation at age 1 year and allergic diagnoses at age 5 years (ßindirect = -2.28; p = 0.0020). Microbiota maturation thus provides a focal point to identify deviations from normative development to predict and prevent allergic disease.

Breastfeeding enrichment of B. longum subsp. infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk.

BACKGROUND: Early antibiotic exposure is linked to persistent disruption of the infant gut microbiome and subsequent elevated pediatric asthma risk. Breastfeeding acts as a primary modulator of the gut microbiome during early life, but its effect on asthma development has remained unclear. METHODS: We harnessed the CHILD cohort to interrogate the influence of breastfeeding on antibiotic-associated asthma risk in a subset of children (n = 2,521). We then profiled the infant microbiomes in a subset of these children (n = 1,338) using shotgun metagenomic sequencing and compared human milk oligosaccharide and fatty acid composition from paired maternal human milk samples for 561 of these infants. FINDINGS: Children who took antibiotics without breastfeeding had 3-fold higher asthma odds, whereas there was no such association in children who received antibiotics while breastfeeding. This benefit was associated with widespread "re-balancing" of taxonomic and functional components of the infant microbiome. Functional changes associated with asthma protection were linked to enriched Bifidobacterium longum subsp. infantis colonization. Network analysis identified a selection of fucosylated human milk oligosaccharides in paired maternal samples that were positively associated with B. infantis and these broader functional changes. CONCLUSIONS: Our data suggest that breastfeeding and antibiotics have opposing effects on the infant microbiome and that breastfeeding enrichment of B. infantis is associated with reduced antibiotic-associated asthma risk. FUNDING: This work was supported in part by the Canadian Institutes of Health Research; the Allergy, Genes and Environment Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

Longitudinal body mass index trajectories at preschool age: children with rapid growth have differential composition of the gut microbiota in the first year of life.

BACKGROUND/OBJECTIVE: The steep rise in childhood obesity has emerged as a worldwide public health problem. The first 4 years of life are a critical window where long-term developmental patterns of body mass index (BMI) are established and a critical period for microbiota maturation. Understanding how the early-life microbiota relate to preschool growth may be useful for identifying preventive interventions for childhood obesity. We aim to investigate whether longitudinal shifts within the bacterial community between 3 months and 1 year of life are associated with preschool BMI z-score trajectories. METHODS: BMI trajectories from birth to 5 years of age were identified using group-based trajectory modeling in 3059 children. Their association with familial and environmental factors were analyzed. Infant gut microbiota at 3 months and 1 year was defined by 16S RNA sequencing and changes in diversity and composition within each BMIz trajectory were analyzed. RESULTS: Four BMIz trajectories were identified: low stable, normative, high stable, and rapid growth. Infants in the rapid growth trajectory were less likely to have been breastfed, and gained less microbiota diversity in the first year of life. Relative abundance of Akkermansia increased with age in children with stable growth, but decreased in those with rapid growth, abundance of Ruminococcus and Clostridium at 1 year were elevated in children with rapid growth. Children who were breastfed at 6 months had increased levels of Sutterella, and decreased levels of Ruminococcus and Clostridium. CONCLUSION: This study provides new insights into the relationship between the gut microbiota in infancy and patterns of growth in a cohort of preschool Canadian children. We highlight that rapid growth since birth is associated with bacteria shown in animal models to have a causative role in weight gain. Our findings support a novel avenue of research targeted on tangible interventions to reduce childhood obesity.

Early-life cytomegalovirus infection is associated with gut microbiota perturbations and increased risk of atopy.

BACKGROUND: The "old friends" hypothesis posits that reduced exposure to previously ubiquitous microorganisms is one factor involved in the increased rates of allergic diseases. Cytomegalovirus (CMV) may be one of the "old friends" hypothesized to help prevent allergic diseases. We sought to elucidate whether early-life CMV infection is associated with childhood atopy via perturbations of the gut microbiota. METHODS: Participants were recruited from a population-based birth cohort (CHILD study) and followed prospectively until age 5 years in four Canadian cities. A total of 928 participants provided stool microbiome data, urine for CMV testing, skin prick tests, and questionnaire-based detailed environmental exposures. Cytomegalovirus infection was assessed in the first year of life while the main outcome was defined by persistent sensitization to any allergen at ages 1, 3, and 5 years. RESULTS: Early CMV infection was associated with increased beta and decreased alpha diversity of the gut microbiota. Both changes in diversity measures and early CMV infection were associated with persistent allergic sensitization at age 5 years (aOR = 2.08; 95% CI: 1, 4.33). Mediation analysis demonstrated that perturbation of gut microbial composition explains 30% of the association. CONCLUSIONS: Early-life CMV infection is associated with an alteration in the intestinal microbiota, which mediates the effect of the infection on childhood atopy. This work indicates that preventing CMV infection would not put children at increased risk of developing atopy. Rather, a CMV vaccine, in addition to preventing CMV-associated morbidity and mortality, might reduce the risk of childhood allergic diseases.

A rich meconium metabolome in human infants is associated with early-life gut microbiota composition and reduced allergic sensitization.

Microbiota maturation and immune development occur in parallel with, and are implicated in, allergic diseases, and research has begun to demonstrate the importance of prenatal influencers on both. Here, we investigate the meconium metabolome, a critical link between prenatal exposures and both early microbiota and immune development, to identify components of the neonatal gut niche that contribute to allergic sensitization. Our analysis reveals that newborns who develop immunoglobulin E (IgE)-mediated allergic sensitization (atopy) by 1 year of age have a less-diverse gut metabolome at birth, and specific metabolic clusters are associated with both protection against atopy and the abundance of key taxa driving microbiota maturation. These metabolic signatures, when coupled with early-life microbiota and clinical factors, increase our ability to accurately predict whether or not infants will develop atopy. Thus, the trajectory of both microbiota colonization and immune development are significantly affected by metabolites present in the neonatal gut at birth.

HEARTBiT: A Transcriptomic Signature for Excluding Acute Cellular Rejection in Adult Heart Allograft Patients.

BACKGROUND: Nine mRNA transcripts associated with acute cellular rejection (ACR) in previous microarray studies were ported to the clinically amenable NanoString nCounter platform. Here we report the diagnostic performance of the resulting blood test to exclude ACR in heart allograft recipients: HEARTBiT. METHODS: Blood samples for transcriptomic profiling were collected during routine post-transplantation monitoring in 8 Canadian transplant centres participating in the Biomarkers in Transplantation initiative, a large (n = 1622) prospective observational study conducted between 2009 and 2014. All adult cardiac transplant patients were invited to participate (median age = 56 [17 to 71]). The reference standard for rejection status was histopathology grading of tissue from endomyocardial biopsy (EMB). All locally graded ISHLT ≥ 2R rejection samples were selected for analysis (n = 36). ISHLT 1R (n = 38) and 0R (n = 86) samples were randomly selected to create a cohort approximately matched for site, age, sex, and days post-transplantation, with a focus on early time points (median days post-transplant = 42 [7 to 506]). RESULTS: ISHLT ≥ 2R rejection was confirmed by EMB in 18 and excluded in 92 samples in the test set. HEARTBiT achieved 47% specificity (95% confidence interval [CI], 36%-57%) given ≥ 90% sensitivity, with a corresponding area under the receiver operating characteristic curve of 0.69 (95% CI, 0.56-0.81). CONCLUSIONS: HEARTBiT's diagnostic performance compares favourably to the only currently approved minimally invasive diagnostic test to rule out ACR, AlloMap (CareDx, Brisbane, CA) and may be used to inform care decisions in the first 2 months post-transplantation, when AlloMap is not approved, and most ACR episodes occur.

Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies.

BACKGROUND: Childhood asthma incidence is decreasing in some parts of Europe and North America. Antibiotic use in infancy has been associated with increased asthma risk. In the present study, we tested the hypothesis that decreases in asthma incidence are linked to reduced antibiotic prescribing and mediated by changes in the gut bacterial community. METHODS: This study comprised population-based and prospective cohort analyses. At the population level, we used administrative data from British Columbia, Canada (population 4·7 million), on annual rates of antibiotic prescriptions and asthma diagnoses, to assess the association between antibiotic prescribing (at age <1 year) and asthma incidence (at age 1-4 years). At the individual level, 2644 children from the Canadian Healthy Infant Longitudinal Development (CHILD) prospective birth cohort were examined for the association of systemic antibiotic use (at age <1 year) with the diagnosis of asthma (at age 5 years). In the same cohort, we did a mechanistic investigation of 917 children with available 16S rRNA gene sequencing data from faecal samples (at age ≤1 year), to assess how composition of the gut microbiota relates to antibiotic exposure and asthma incidence. FINDINGS: At the population level between 2000 and 2014, asthma incidence in children (aged 1-4 years) showed an absolute decrease of 7·1 new diagnoses per 1000 children, from 27·3 (26·8-28·3) per 1000 children to 20·2 (19·5-20·8) per 1000 children (a relative decrease of 26·0%). Reduction in incidence over the study period was associated with decreasing antibiotic use in infancy (age <1 year), from 1253·8 prescriptions (95% CI 1219·3-1288·9) per 1000 infants to 489·1 (467·6-511·2) per 1000 infants (Spearman's r=0·81; p<0·0001). Asthma incidence increased by 24% with each 10% increase in antibiotic prescribing (adjusted incidence rate ratio 1·24 [95% CI 1·20-1·28]; p<0·0001). In the CHILD cohort, after excluding children who received antibiotics for respiratory symptoms, asthma diagnosis in childhood was associated with infant antibiotic use (adjusted odds ratio [aOR] 2·15 [95% CI 1·37-3·39]; p=0·0009), with a significant dose-response; 114 (5·2%) of 2182 children unexposed to antibiotics had asthma by age 5 years, compared with 23 (8·1%) of 284 exposed to one course, five (10·2%) of 49 exposed to two courses, and six (17·6%) of 34 exposed to three or more courses (aOR 1·44 [1·16-1·79]; p=0·0008). Increasing α-diversity of the gut microbiota, defined as an IQR increase (25th to 75th percentile) in the Chao1 index, at age 1 year was associated with a 32% reduced risk of asthma at age 5 years (aOR for IQR increase 0·68 [0·46-0·99]; p=0·046). In a structural equation model, we found the gut microbiota at age 1 year, characterised by α-diversity, ß-diversity, and amplicon sequence variants modified by antibiotic exposure, to be a significant mediator between outpatient antibiotic exposure in the first year of life and asthma diagnosis at age 5 years (ß=0·08; p=0·027). INTERPRETATION: Our findings suggest that the reduction in the incidence of paediatric asthma observed in recent years might be an unexpected benefit of prudent antibiotic use during infancy, acting via preservation of the gut microbial community. FUNDING: British Columbia Ministry of Health, Pharmaceutical Services Branch; Canadian Institutes of Health Research; Allergy, Genes and Environment (AllerGen) Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

Ensembling Electrical and Proteogenomics Biomarkers for Improved Prediction of Cardiac-Related 3-Month Hospitalizations: A Pilot Study.

BACKGROUND: Many risk models for predicting mortality, hospitalizations, or both in patients with heart failure have been developed but do not have sufficient discriminatory ability. The purpose of this study was to identify predictive biomarkers of hospitalizations in heart failure patients using omics-based technologies applied to blood and electrical monitoring of the heart. METHODS: Blood samples were collected from 58 heart failure patients during enrollment into this study. Each patient wore a 48-hour Holter monitor that recorded the electrical activity of their heart. The blood samples were profiled for gene expression using microarrays and protein levels using multiple reaction monitoring. Statistical deconvolution was used to estimate cellular frequencies of common blood cells. Classification models were developed using clinical variables, Holter variables, cell types, gene transcripts, and proteins to predict hospitalization status. RESULTS: Of the 58 patients recruited, 13 were hospitalized within 3 months after enrollment. These patients had lower diastolic and systolic blood pressures, higher brain natriuretic peptide levels, most had higher blood creatinine levels, and had been diagnosed with heart failure for a longer time period. The best-performing clinical model had an area under the receiver operating characteristic curve of 0.76. An ensemble biomarker panel consisting of Holter variables, cell types, gene transcripts, and proteins had an area under the receiver operating characteristic curve of 0.88. CONCLUSIONS: Molecular-based analyses as well as sensory data might provide sensitive biomarkers for the prediction of hospitalizations in heart failure patients. These approaches may be combined with traditional clinical models for the development of improved risk prediction models for heart failure.

Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease.

BACKGROUND: Cholesterol efflux capacity (CEC) is a measure of HDL function that, in cell-based studies, has demonstrated an inverse association with cardiovascular disease. The cell-based measure of CEC is complex and low-throughput. We hypothesized that assessment of the lipoprotein proteome would allow for precise, high-throughput CEC prediction. METHODS: After isolating lipoprotein particles from serum, we used LC-MS/MS to quantify 21 lipoprotein-associated proteins. A bioinformatic pipeline was used to identify proteins with univariate correlation to cell-based CEC measurements and generate a multivariate algorithm for CEC prediction (pCE). Using logistic regression, protein coefficients in the pCE model were reweighted to yield a new algorithm predicting coronary artery disease (pCAD). RESULTS: Discovery using targeted LC-MS/MS analysis of 105 training and test samples yielded a pCE model comprising 5 proteins (Spearman r = 0.86). Evaluation of pCE in a case-control study of 231 specimens from healthy individuals and patients with coronary artery disease revealed lower pCE in cases (P = 0.03). Derived within this same study, the pCAD model significantly improved classification (P < 0.0001). Following analytical validation of the multiplexed proteomic method, we conducted a case-control study of myocardial infarction in 137 postmenopausal women that confirmed significant separation of specimen cohorts in both the pCE (P = 0.015) and pCAD (P = 0.001) models. CONCLUSIONS: Development of a proteomic pCE provides a reproducible high-throughput alternative to traditional cell-based CEC assays. The pCAD model improves stratification of case and control cohorts and, with further studies to establish clinical validity, presents a new opportunity for the assessment of cardiovascular health.

Performance of a Prostate Cancer Genomic Classifier in Predicting Metastasis in Men with Prostate-specific Antigen Persistence Postprostatectomy.

BACKGROUND: Prostate cancer patients who have a detectable prostate-specific antigen (PSA) postprostatectomy may harbor pre-existing metastatic disease. To our knowledge, none of the commercially available genomic biomarkers have been investigated in such men. OBJECTIVE: To evaluate if a 22-gene genomic classifier can independently predict development of metastasis in men with PSA persistence postoperatively. DESIGN, SETTING, AND PARTICIPANTS: A multi-institutional study of 477 men who underwent radical prostatectomy (RP) between 1990 and 2015 from three academic centers. Patients were categorized as detectable PSA (n=150) or undetectable (n=327) based on post-RP PSA nadir ≥0.1 ng/ml. OUTCOME MEASUREMENTS AND STATISITICAL ANALYSIS: Cumulative incidence curves for metastasis were constructed using Fine-Gray competing risks analysis. Penalized Cox univariable and multivariable (MVA) proportional hazards models were performed to evaluate the association of the genomic classifier with metastasis. RESULTS AND LIMITATIONS: The median follow-up for censored patients was 57 mo. The median time from RP to first postoperative PSA was 1.4 mo. Detectable PSA patients were more likely to have higher adverse pathologic features compared with undetectable PSA patients. On MVA, only genomic high-risk (hazard ratio [HR]: 5.95, 95% confidence interval [CI]: 2.02-19.41, p=0.001), detectable PSA (HR: 4.26, 95% CI: 1.16-21.8, p=0.03), and lymph node invasion (HR: 12.2, 95% CI: 2.46-70.7, p=0.003) remained prognostic factors for metastasis. Among detectable PSA patients, the 5-yr metastasis rate was 0.90% for genomic low/intermediate and 18% for genomic high risk (p<0.001). Genomic high risk remained independently prognostic on MVA (HR: 5.61, 95% CI: 1.48-22.7, p=0.01) among detectable PSA patients. C-index for Cancer of the Prostate Risk Assessment Postsurgical score, Gandaglia nomogram, and the genomic classifier plus either Cancer of the Prostate Risk Assessment Postsurgical score or Gandaglia were 0.69, 0.68, and 0.82 or 0.81, respectively. Sample size was a limitation. CONCLUSIONS: Despite patients with a detectable PSA harboring significantly higher rates of aggressive clinicopathologic features, Decipher independently predicts for metastasis. Prospective validation of these findings is warranted and will be collected as part of the ongoing randomized trial NRG GU-002. PATIENT SUMMARY: Decipher independently predicted metastasis for patients with detectable prostate-specific antigen after prostatectomy.

Multiple reaction monitoring mass spectrometry to identify novel plasma protein biomarkers of treatment response in cystic fibrosis pulmonary exacerbations.

BACKGROUND: Systemic inflammation decreases with IV antibiotics during the treatment of CF pulmonary exacerbations (PEx). We used multiple reaction monitoring mass spectrometry and immunoassays to monitor blood proteins during PEx treatment to determine if early changes could be used to predict PEx outcomes following treatment. METHODS: Blood samples from 25 PEx (22 unique adults) were collected within 24h of admission, day 5, day 10, and at IV antibiotic completion. Ninety-two blood proteins involved in host immunity and inflammation were measured. RESULTS: Levels of several blood proteins changed from admission to end of IV antibiotics, most increasing with treatment. Early changes (admission to day 5) in fibrinogen levels had the strongest correlation with overall improvement in CFRSD-CRISS and FEV1% predicted by the end of treatment. CONCLUSIONS: Several plasma proteins changed significantly with IV antibiotics. Future studies will evaluate fibrinogen as an early biomarker of PEx treatment response in CF.

Circulating biomarker responses to medical management vs. mechanical circulatory support in severe inotrope-dependent acute heart failure.

BACKGROUND: Severe inotrope-dependent acute heart failure (AHF) is associated with poor clinical outcomes. There are currently no well-defined blood biomarkers of response to treatment that can guide management or identify recovery in this patient population. In the present study, we characterized the levels of novel and emerging circulating biomarkers of heart failure in patients with AHF over the first 30 days of medical management or mechanical circulatory support (MCS). We hypothesized a shared a plasma proteomic treatment response would be identifiable in both patient groups, representing reversal of the AHF phenotype. METHODS AND RESULTS: Time course plasma samples of the first 30 days of therapy, obtained from patients managed medically (n = 8) or with implantable MCS (n = 5), underwent semi-targeted and candidate biomarker analyses, using multiple reaction monitoring (MRM) mass spectrometry, antibody arrays, and enzyme-linked immunosorbent assays. Differentially expressed proteins were identified using robust limma for MRM and antibody array data. Patients managed medically or with implantable MCS had a shared proteomic signature of six plasma proteins: circulating cardiotrophin 1, cardiac troponin T, clusterin, and dickopff 1 increased, while levels of C-reactive protein and growth differentiation factor 15 decreased in both groups over the 30 day time course. CONCLUSIONS: We have characterized the temporal proteomic signature of clinical recovery in AHF patients managed medically or with MCS, over the first 30 days of treatment. Changes in biomarker expression over the time course of treatment may provide a basis for understanding the biological basis of AHF, potentially identifying novel markers and pathophysiologic mechanisms of recovery.

Airway hyperresponsiveness in chronic obstructive pulmonary disease: A marker of asthma-chronic obstructive pulmonary disease overlap syndrome?

BACKGROUND: The impact of airway hyperreactivity (AHR) on respiratory mortality and systemic inflammation among patients with chronic obstructive pulmonary disease (COPD) is largely unknown. We used data from 2 large studies to determine the relationship between AHR and FEV1 decline, respiratory mortality, and systemic inflammation. OBJECTIVES: We sought to determine the relationship of AHR with FEV1 decline, respiratory mortality, and systemic inflammatory burden in patients with COPD in the Lung Health Study (LHS) and the Groningen Leiden Universities Corticosteroids in Obstructive Lung Disease (GLUCOLD) study. METHODS: The LHS enrolled current smokers with mild-to-moderate COPD (n = 5887), and the GLUCOLD study enrolled former and current smokers with moderate-to-severe COPD (n = 51). For the primary analysis, we defined AHR by a methacholine provocation concentration of 4 mg/mL or less, which led to a 20% reduction in FEV1 (PC20). RESULTS: The primary outcomes were FEV1 decline, respiratory mortality, and biomarkers of systemic inflammation. Approximately 24% of LHS participants had AHR. Compared with patients without AHR, patients with AHR had a 2-fold increased risk of respiratory mortality (hazard ratio, 2.38; 95% CI, 1.38-4.11; P = .002) and experienced an accelerated FEV1 decline by 13.2 mL/y in the LHS (P = .007) and by 12.4 mL/y in the much smaller GLUCOLD study (P = .079). Patients with AHR had generally reduced burden of systemic inflammatory biomarkers than did those without AHR. CONCLUSIONS: AHR is common in patients with mild-to-moderate COPD, affecting 1 in 4 patients and identifies a distinct subset of patients who have increased risk of disease progression and mortality. AHR may represent a spectrum of the asthma-COPD overlap phenotype that urgently requires disease modification.

Discovery of novel plasma protein biomarkers to predict imminent cystic fibrosis pulmonary exacerbations using multiple reaction monitoring mass spectrometry.

BACKGROUND: Despite the significant morbidity and mortality related to pulmonary exacerbations in cystic fibrosis (CF), there remains no reliable predictor of imminent exacerbation. OBJECTIVE: To identify blood-based biomarkers to predict imminent (<4 months from stable blood draw) CF pulmonary exacerbations using targeted proteomics. METHODS: 104 subjects provided plasma samples when clinically stable and were randomly split into discovery (n=70) and replication (n=34) cohorts. Multiple reaction monitoring mass spectrometry (MRM-MS) was used to measure 117 peptides (79 proteins) from plasma. Plasma proteins with differential abundance between subjects who did versus did not develop an imminent exacerbation were analysed and proteins with fold difference >1.5 between the groups were included in an MRM-MS classifier model to predict imminent exacerbations. Performance characteristics were compared with clinical predictors and candidate plasma protein biomarkers. RESULTS: Six proteins were included in the final MRM-MS protein panel. The area under the curve (AUC) for the prediction of imminent exacerbations was highest for the MRM-MS protein panel (AUC 0.74) in comparison to FEV1% predicted (AUC 0.55) and the top candidate plasma protein biomarkers, including C-reactive protein (AUC 0.61) and interleukin-6 (AUC 0.60). The MRM-MS protein panel performed similarly in the replication cohort (AUC 0.73). CONCLUSIONS: Using MRM-MS, a six-protein panel measured from plasma can distinguish individuals with versus without an imminent exacerbation. With further replication and assay development, this biomarker panel may be clinically applicable for prediction of exacerbations in individuals with CF.

Serum proteomics in multiple sclerosis disease progression.

Multiple sclerosis (MS) is associated with chronic degeneration of the central nervous system and may cause permanent neurological problems and considerable disability. While its causes remain unclear, its extensive phenotypic variability makes its prognosis and treatment difficult. The identification of serum proteomic biomarkers of MS progression could further our understanding of the molecular mechanisms related to MS disease processes. In the current study, we used isobaric tagging for relative and absolute protein quantification (iTRAQ) methodology and advanced multivariate statistical analysis to quantify and identify potential serum biomarker proteins of MS progression. We identified a panel of 11 proteins and combined them into a classifier that best classified samples into the two disease groups. The estimated area under the receiver operating curve of this classifier was 0.88 (p-value=0.017), with 86% sensitivity and specificity. The identified proteins encompassed processes related to inflammation, opsonization, and complement activation. Results from this study are in particular valuable to design a targeted Multiple Reaction Monitoring mass spectrometry based (MRM-MS) assay to conduct an external validation in an independent and larger cohort of patients. Validated biomarkers may result in the development of a minimally-invasive tool to monitor MS progression and complement current clinical practices. BIOLOGICAL SIGNIFICANCE: A hallmark of multiple sclerosis is the unpredictable disease course (progression). There are currently no clinically useful biomarkers of MS disease progression; most work has focused on the analysis of CSF, which requires an invasive procedure. Here, we explore the potential of proteomics to identify panels of serum biomarkers of disease progression in MS. By comparing the protein signatures of two challenging to obtain, but well-defined, MS phenotypic groups at the extremes of progression (benign and aggressive cases of MS), we identified proteins that encompass processes related to inflammation, opsonization, and complement activation. Findings require validation, but are an important step on the pathway to clinically useful biomarker discovery. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Gender-specific plasma proteomic biomarkers in patients with Anderson-Fabry disease.

AIMS: Anderson-Fabry disease (AFD) is an important X-linked metabolic disease resulting in progressive end-organ involvement, with cardiac disease being the dominant determinant of survival in a gender-dependent manner. Recent epidemiological screening for AFD suggests the prevalence is much higher than previously recognized, with estimates of 1:3000. Our aim was to discover novel plasma biomarker signatures in adult patients with AFD. METHODS AND RESULTS: We used an unbiased proteomic screening approach to discover novel plasma biomarker signatures. In the discovery cohort of 46 subjects, 14 healthy controls and 32 patients with AFD, we used a mass spectrometry iTRAQ proteomic approach followed by multiple reaction monitoring (MRM) assays to identify biomarkers. Of the 38 protein groups discovered by iTRAQ, 18 already had existing MRM assays. Based on MRM, we identified an eight-protein biomarker panel (22 kDa protein, afamin, α1 antichymotrypsin, apolipoprotein E, ß-Ala His dipeptidase, haemoglobin α-2, isoform 1 of sex hormone-binding globulin, and peroxiredoxin 2) that was very specific and sensitive for male AFD patients. In female AFD patients, we identified a nine-marker panel of proteins with only three proteins, apolipoprotein E, haemoglobin α-2, and peroxiredoxin 2, common to both genders, suggesting a gender-specific alteration in plasma biomarkers in patients with AFD. The biomarkers were validated in plasma samples from 48 subjects using MRM, and they performed inferiorly in patients with heart failure. CONCLUSIONS: We have identified gender-specific plasma protein biomarker panels that are specific and sensitive for the AFD phenotype. The gender-specific panels offer important insight into potential differences in pathophysiology and prognosis between males and females with AFD.