Does urinary peptide content differ between COPD patients with and without inherited alpha-1 antitrypsin deficiency?

Differentiating between chronic obstructive pulmonary disease (COPD) patients with normal (PiMM) or deficient (PiZZ) genetic variants of alpha-1 antitrypsin (A1AT) is important not only for understanding the pathobiology of disease progression but also for improving personalized therapies. This pilot study aimed to investigate whether urinary peptides reflect the A1AT-related phenotypes of COPD. Urine samples from 19 clinically stable COPD cases (7 PiMM and 12 PiZZ A1AT) were analyzed by capillary electrophoresis coupled to mass spectrometry. We identified 66 peptides (corresponding to 36 unique proteins) that differed between PiZZ and PiMM COPD. Among these, peptides from the collagen family were the most abundant and divergent. A logistic regression model based on COL1A1 or COL5A3 peptides enabled differentiation between PiMM and PiZZ groups, with a sensitivity of 100% and specificity of 85.71% for COL1A1 and a sensitivity of 91.67% and specificity of 85.71% for COL5A3. Furthermore, patients with PiZZ presented low levels of urinary peptides involved in lipoproteins/lipids and retinoic acid metabolism, such as apolipoprotein A-I and C4, retinol-binding protein 4 and prostaglandin-H2 d-isomerase. However, peptides of MDS1 and EVII complex locus, gelsolin and hemoglobin alpha were found in the urine of COPD cases with PiZZ, but not with PiMM. These capillary electrophoresis coupled to mass spectrometry-based results provide the first evidence that urinary peptide content differs between PiMM and PiZZ patients with COPD

Left ventricular diastolic function in relation to the urinary proteome: a proof-of-concept study in a general population

Background: In previous studies, we identified two urinary proteomic classifiers, termed HF1 and HF2, which discriminated subclinical diastolic left ventricular (LV) dysfunction from normal. HF1 and HF2 combine information from 85 and 671 urinary peptides, mainly up- or down-regulated collagen fragments. We sought to validate these classifiers in a population study. Methods: In 745 people randomly recruited from a Flemish population (49.8 years; 51.3% women), we measured early and late diastolic peak velocities of mitral inflow (E and A) and mitral annular velocities (e' and a') by conventional and tissue Doppler echocardiography, and the urinary proteome by capillary electrophoresis coupled with mass spectrometry. Results: In the analyses adjusted for sex, age, body mass index, blood pressure, heart rate, LV mass index and intake of medications, we expressed effect sizes per 1-SD increment in the classifiers. HF1 was associated with 0.204 cm/s lower e' peak velocity (95% confidence interval, 0.057–0.351; p = 0.007) and 0.145 higher E/e' ratio (0.023–0.268; p = 0.020), while HF2 was associated with a 0.174 higher E/e' ratio (0.046–0.302; p = 0.008). According to published definitions, 67 (9.0%) participants had impaired LV relaxation and 96 (12.9%) had elevated LV filling pressure. The odds of impaired relaxation associated with HF1 was 1.38 (1.01–1.88; p = 0.043) and that of increased LV filling pressure associated with HF2 was 1.38 (1.00–1.90; p = 0.052). Conclusions: In a general population, the urinary proteome correlated with diastolic LV dysfunction, proving its utility for early diagnosis of this condition

Urinary proteomics pilot study for biomarker discovery and diagnosis in heart failure with reduced ejection fraction

Background Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF. Methods and Results Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin. Conclusion CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data

The use of urinary proteomics in the assessment of suitability of mouse models for ageing

Ageing is a complex process characterised by a systemic and progressive deterioration of biological functions. As ageing is associated with an increased prevalence of age-related chronic disorders, understanding its underlying molecular mechanisms can pave the way for therapeutic interventions and managing complications. Animal models such as mice are commonly used in ageing research as they have a shorter lifespan in comparison to humans and are also genetically close to humans. To assess the translatability of mouse ageing to human ageing, the urinary proteome in 89 wild-type (C57BL/6) mice aged between 8–96 weeks was investigated using capillary electrophoresis coupled to mass spectrometry (CE-MS). Using age as a continuous variable, 295 peptides significantly correlated with age in mice were identified. To investigate the relevance of using mouse models in human ageing studies, a comparison was performed with a previous correlation analysis using 1227 healthy subjects. In mice and humans, a decrease in urinary excretion of fibrillar collagens and an increase of uromodulin fragments was observed with advanced age. Of the 295 peptides correlating with age, 49 had a strong homology to the respective human age-related peptides. These ortholog peptides including several collagen (N = 44) and uromodulin (N = 5) fragments were used to generate an ageing classifier that was able to discriminate the age among both wild-type mice and healthy subjects. Additionally, the ageing classifier depicted that telomerase knock-out mice were older than their chronological age. Hence, with a focus on ortholog urinary peptides mouse ageing can be translated to human ageing

Acute exposure to nocturnal train noise induces endothelial dysfunction and pro-thromboinflammatory changes of the plasma proteome in healthy subjects

Nocturnal train noise exposure has been associated with hypertension and myocardial infarction. It remains unclear whether acute nighttime train exposure may induce subclinical atherosclerosis, such as endothelial dysfunction and other functional and/or biochemical changes. Thus, we aimed to expose healthy subjects to nocturnal train noise and to assess endothelial function, changes in plasma protein levels and clinical parameters. In a randomized crossover study, we exposed 70 healthy volunteers to either background or two different simulated train noise scenarios in their homes during three nights. After each night, participants visited the study center for measurement of vascular function and assessment of other biomedical and biochemical parameters. The three nighttime noise scenarios were exposure to either background noise (control), 30 or 60 train noise events (Noise30 or Noise60), with average sound pressure levels of 33, 52 and 54 dB(A), respectively. Flow-mediated dilation (FMD) of the brachial artery was 11.23 ± 4.68% for control, compared to 8.71 ± 3.83% for Noise30 and 8.47 ± 3.73% for Noise60 (p < 0.001 vs. control). Sleep quality was impaired after both Noise30 and Noise60 nights (p < 0.001 vs. control). Targeted proteomic analysis showed substantial changes of plasma proteins after the Noise60 night, mainly centered on redox, pro-thrombotic and proinflammatory pathways. Exposure to simulated nocturnal train noise impaired endothelial function. The proteomic changes point toward a proinflammatory and pro-thrombotic phenotype in response to nocturnal train noise and provide a molecular basis to explain the increased cardiovascular risk observed in epidemiological noise studies

Urine proteomics in the diagnosis of stable angina.

BACKGROUND: We have previously described a panel of 238 urinary polypeptides specific for established severe coronary artery disease (CAD). Here we studied this polypeptide panel in patients with a wider range of CAD severity. METHODS: We recruited 60 patients who underwent elective coronary angiography for investigation of stable angina. Patients were selected for either having angiographic evidence of CAD or not (NCA) following coronary angiography (n = 30/30; age, 55 ± 6 vs. 56 ± 7 years, P = 0.539) to cover the extremes of the CAD spectrum. A further 66 patients with severe CAD (age, 64 ± 9 years) prior to surgical coronary revascularization were added for correlation studies. The Gensini score was calculated from coronary angiograms as a measure of CAD severity. Urinary proteomic analyses were performed using capillary electrophoresis coupled online to micro time-of-flight mass spectrometry. The urinary polypeptide pattern was classified using a predefined algorithm and resulting in the CAD238 score, which expresses the pattern quantitatively. RESULTS: In the whole cohort of patients with CAD (Gensini score 60 [40; 98]) we found a close correlation between Gensini scores and CAD238 (ρ = 0.465, P < 0.001). After adjustment for age (β = 0.144; P = 0.135) the CAD238 score remained a significant predictor of the Gensini score (β =0.418; P < 0.001). In those with less severe CAD (Gensini score 40 [25; 61]), however, we could not detect a difference in CAD238 compared to patients with NCA (-0.487 ± 0.341 vs. -0.612 ± 0.269, P = 0.119). CONCLUSIONS: In conclusion the urinary polypeptide CAD238 score is associated with CAD burden and has potential as a new cardiovascular biomarker

Unraveling the microbiome of a thermophilic biogas plant by metagenome and metatranscriptome analysis complemented by characterization of bacterial and archaeal isolates

One of the most promising technologies to sustainably produce energy and to mitigate greenhouse gas emissions from combustion of fossil energy carriers is the anaerobic digestion and biomethanation of organic raw material and waste towards biogas by highly diverse microbial consortia. In this context, the microbial systems ecology of thermophilic industrial-scale biogas plants is poorly understood