Mass spectrometry-based biomarkers to detect prostate cancer: a multicentric study based on non-invasive urine collection without prior digital rectal examination

(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies

Peptidome and Proteome Peritoneal Dialysate Evolutionary Atlas (P3DEVOATLAS)

Peritoneal membrane (PM) failure in patients with end stage renal disease submitted to peritoneal dialysis (PD) cannot be predicted and does not occur in every patient in the same sequence and to the same extent. Moreover, long-term PD leads to morphological and functional alterations in the PM, reducing the lifespan of this dialysis up to five years, and forcing the replacement of PD by other renal replacement therapies. This represents a lower quality of life for the patients and extra cost of tens of million euros per year for the Portuguese National Health System. Peritoneal dialysis effluent (PDE) represents an underestimated biochemical window into the peritoneum and a useful reservoir of potential clinical biomarkers. Therefore, this work aims to develop longitudinal studies to unravel the evolution of the peptidome and proteome of the PDE with time, to identify specific molecular changes that can be particularly interesting for the understanding and early detection of long-term PM alterations. To achieve this goal, mass spectrometry (MS)-based methods are needed to improve PDE proteome and peptidome analysis and to overcome some drawbacks that can arise from such a complex biological sample that can hamper the proteome and peptidome coverage. For this reason, this thesis is focused also in the use of sample treatments and methodologies to reduce PDE sample complexity prior to MS analysis. Therefore, different methods of sample treatment were assessed with success as proteomics tools for getting insight into the PDE proteome and peptidome. Furthermore, this research constitutes the first proteome and peptidome-based longitudinal study of PD patient. In addition, the results represent the highest proteome and peptidome coverage ever achieved for this complex sample. Hence, this knowledge could be useful for the proteomic and clinical PD-devoted research community

Prediction of coronary artery disease using urinary proteomics

Aims: Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. Methods and results: Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78–0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66–0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47–0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80–0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26–1.89, P \u3c 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25–0.95, P = 0.001; 0.64, 95% CI: 0.28–0.98, P = 0.001, correspondingly). Conclusion: A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention

SBOL-OWL: An ontological approach for formal and semantic representation of synthetic biology information

Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies

Tissue Remodeling in Health and Disease Caused by Bacteria, Parasites, Fungi, and Viruses

Tissues undergo constant remodeling to maintain architecture during growth, in normal physiology and in response to disease. Interactions of the host with commensals and pathogens may affect immune responses and tissue remodeling, including for example through the generation of neo-epitopes and resulting in damage-associated molecular patterns. Roles for the microbiome, viriome, parasites, and fungi in host-pathogen interactions and in homeostasis is a current topic with considerable interest regarding effects relating to the gut-brain axis, chronic disease, cancer, dysbiosis, host metabolism, and drug metabolism. This E-book contains state-of-the-art primary research studies and review articles from international experts and diverse leading groups in the field to further current understanding of the contributions of commensals and pathogens in tissue remodeling in physiological and pathophysiological processes of the host

Exploration of urine and plasma biomarkers in liver fibrosis and hepatocellular carcinoma

Liver fibrosis is a major risk factor for development of hepatocellular carcinoma. Both liver fibrosis and hepatocellular carcinoma are associated with molecular pathogenic mechanisms involving alterations in the hepatocellular proteome, metabolome, and genome. Both liver fibrosis and hepatocellular carcinoma lack suitable biological predictive biomarkers in clinical practice. Therefore, to aid in identifying suitable biomarkers, three approaches were employed in patients with liver fibrosis and hepatocellular carcinoma. Firstly, proteomic analysis was applied to identify post-translational enzymatic protein modifications peripherally present in the urine. Secondly, metabolic profiling was applied to characterise small volatile organic compounds present in the urine. Thirdly, DNA methylation detection technology was applied, to identify methylated SEPTIN9 patterns among the circulating hepatocellular carcinoma DNA molecules within the cell-free DNA pool present peripherally in the plasma. Urinary proteomic analysis identified novel specific peptides for liver fibrosis and hepatocellular carcinoma. Additionally, proteases potentially involved in liver fibrosis and hepatocellular carcinoma were predicted from the peptides sequence with further demonstration of these proteases by immunohistochemistry in human normal liver tissue, liver fibrosis and hepatocellular carcinoma. The identified urinary peptides showed good diagnostic and prognostic performance in liver fibrosis and hepatocellular carcinoma. Urinary metabolic profiling technologies demonstrated that volatile organic compounds patterns can be used noninvasively to detect hepatocellular carcinoma and they also revealed chemical composition of novel volatile organic compounds related to liver fibrosis and hepatocellular carcinoma. DNA methylation analysis showed that methylated SEPTIN9 has good sensitivity and specificity for hepatocellular carcinoma. It was also a prognostic indicator in patients with liver disease and hepatocellular carcinoma. The methylated SEPTIN9 was also associated with other surrogate biomarkers for liver function, liver fibrosis and inflammation. Additionally, methylated SEPTIN9 was noted to incrementally increase in various stages of liver disease. The researched biomarkers in this work provided some insight into the pathogenic mechanisms of liver fibrosis and hepatocellular carcinoma. If further validated, the identified biomarkers in this work could offer cost-effective tools for screening, diagnosis, prognosis and/or surveillance, particularly in low resource settings where access to advanced imaging and invasive biopsy is not feasible

Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study

The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes

The Proteasix Ontology

Abstract Background The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides) Methods The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis. Results The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results. Availability The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone

The Proteasix Ontology

Background: The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides)Methods: The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis.Results: The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results.Availability: The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone