scholarly journals Glycans as Biomarkers: Status and Perspectives

2011 ◽  
Vol 30 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Miroslava Janković
Keyword(s):  
Large Scale ◽  
Biomarker Discovery ◽  
High Sensitivity ◽  
Disease Diagnosis ◽  
Protein Glycosylation ◽  
Post Translational Modification ◽  
Different Types ◽  
Progression Of Disease ◽  
New Biomarkers ◽  
Selection Of

Glycans as Biomarkers: Status and PerspectivesProtein glycosylation is a ubiquitous and complex co- and post-translational modification leading to glycan formation, i.e. oligosaccharide chains covalently attached to peptide backbones. The significance of changes in glycosylation for the beginning, progress and outcome of different human diseases is widely recognized. Thus, glycans are considered as unique structures to diagnose, predict susceptibility to and monitor the progression of disease. In the »omics« era, the glycome, a glycan analogue of the proteome and genome, holds considerable promise as a source of new biomarkers. In the design of a strategy for biomarker discovery, new principles and platforms for the analysis of relatively small amounts of numerous glycoproteins are needed. Emerging glycomics technologies comprising different types of mass spectrometry and affinity-based arrays are next in line to deliver new analytical procedures in the field of biomarkers. Screening different types of glycomolecules, selection of differentially expressed components, their enrichment and purification or identification are the most challenging parts of experimental and clinical glycoproteomics. This requires large-scale technologies enabling high sensitivity, proper standardization and validation of the methods to be used. Further progress in the field of applied glycoscience requires an integrated systematic approach in order to explore properly all opportunities for disease diagnosis.

Comparison of Various Systems for On-Site Wastewater Treatment

1990 ◽  
Vol 22 (3-4) ◽  
pp. 41-48 ◽  
Author(s):  
Frits A. Fastenau ◽  
Jaap H. J. M. van der Graaf ◽  
Gerard Martijnse
Keyword(s):  
Wastewater Treatment ◽  
Large Scale ◽  
Research Work ◽  
Field Research ◽  
Limiting Factors ◽  
Land Claims ◽  
Rotating Biological Contactor ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
Selection Of

Diffuse pollution, caused by direct discharges from individual houses, small built-up nuclei, farms, camp-sites, etc., for which connection to central wastewater treatment systems is unfeasible, may be significantly reduced by on-site treatment. Based on a large scale research, including intensive field-research work on 14 systems of different types and sizes in a range equal to population equivalents (p.e) of 5 - 200 persons, 8 different types of system were compared. The comparison involved technological features, such as removal efficiency, reliability, operational and maintenance aspects, environmental impacts and land claims, together with economical features showing significant differences. Advantages and disadvantages of each system are highlighted to enable a selection of suitable systems to be made. When no limiting factors are present, it was found that - in general-infiltration systems (infiltration pits; infiltration trenches) have the best features for on-site treatment up to 100 p.e. For larger capacities, or when infiltration is not possible, the rotating biological contactor will be the best solution mainly because of the lower costs.

scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

AbstractMotivationProtein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.ResultsTherefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.AvailabilityThe source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating [email protected] and [email protected] informationSupplementary data are available online.

scholarly journals ZIC-cHILIC Functionalized Magnetic Nanoparticle for Rapid and Sensitive Glycopeptide Enrichment from < 1 µL Serum

Nanomaterials ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2159
Author(s):  
Tiara Pradita ◽  
Yi-Ju Chen ◽  
Elias Gizaw Mernie ◽  
Sharine Noelle Bendulo ◽  
Yu-Ju Chen
Keyword(s):  
Magnetic Nanoparticles ◽  
High Performance ◽  
Large Scale ◽  
Structural Complexity ◽  
High Sensitivity ◽  
Protein Glycosylation ◽  
Rapid Separation ◽  
Glycopeptide Enrichment ◽  
Human Proteins ◽  
Differential Pattern

Due to their unique glycan composition and linkage, protein glycosylation plays significant roles in cellular function and is associated with various diseases. For comprehensive characterization of their extreme structural complexity occurring in >50% of human proteins, time-consuming multi-step enrichment of glycopeptides is required. Here we report zwitterionic n-dodecylphosphocholine-functionalized magnetic nanoparticles (ZIC-cHILIC@MNPs) as a highly efficient affinity nanoprobe for large-scale enrichment of glycopeptides. We demonstrate that ZIC-cHILIC@MNPs possess excellent affinity, with 80–91% specificity for glycopeptide enrichment, especially for sialylated glycopeptide (90%) from biofluid specimens. This strategy provides rapidity (~10 min) and high sensitivity (<1 μL serum) for the whole enrichment process in patient serum, likely due to the rapid separation using magnetic nanoparticles, fast reaction, and high performance of the affinity nanoprobe at nanoscale. Using this strategy, we achieved personalized profiles of patients with hepatitis B virus (HBV, n = 3) and hepatocellular carcinoma (HCC, n = 3) at the depth of >3000 glycopeptides, especially for the large-scale identification of under-explored sialylated glycopeptides. The glycoproteomics atlas also revealed the differential pattern of sialylated glycopeptides between HBV and HCC groups. The ZIC-cHILIC@MNPs could be a generic tool for advancing the glycoproteome analysis, and contribute to the screening of glycoprotein biomarkers.

scholarly journals A Log-Based Anomaly Detection Method with Efficient Neighbor Searching and Automatic K Neighbor Selection

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bingming Wang ◽  
Shi Ying ◽  
Zhe Yang
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Tree Model ◽  
Log Data ◽  
Neighbor Search ◽  
Different Types ◽  
Efficient Selection ◽  
Selection Of

Using the k-nearest neighbor (kNN) algorithm in the supervised learning method to detect anomalies can get more accurate results. However, when using kNN algorithm to detect anomaly, it is inefficient at finding k neighbors from large-scale log data; at the same time, log data are imbalanced in quantity, so it is a challenge to select proper k neighbors for different data distributions. In this paper, we propose a log-based anomaly detection method with efficient selection of neighbors and automatic selection of k neighbors. First, we propose a neighbor search method based on minhash and MVP-tree. The minhash algorithm is used to group similar logs into the same bucket, and MVP-tree model is built for samples in each bucket. In this way, we can reduce the effort of distance calculation and the number of neighbor samples that need to be compared, so as to improve the efficiency of finding neighbors. In the process of selecting k neighbors, we propose an automatic method based on the Silhouette Coefficient, which can select proper k neighbors to improve the accuracy of anomaly detection. Our method is verified on six different types of log data to prove its universality and feasibility.

scholarly journals Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis

2019 ◽  
Author(s):  
Nicholas M. Riley ◽  
Alexander S. Hebert ◽  
Michael S. Westphall ◽  
Joshua J. Coon
Keyword(s):  
Large Scale ◽  
Electron Transfer Dissociation ◽  
Protein Glycosylation ◽  
Analytical Challenge ◽  
Post Translational Modification ◽  
Structural Domains ◽  
Site Specific ◽  
Future Studies ◽  
Site Heterogeneity ◽  
Degree Of Heterogeneity

ABSTRACTProtein glycosylation is a highly important, yet a poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity and analytical challenge. A lack of suitable analytical methods for large-scale analyses of intact glycopeptides has ultimately limited our abilities to both address the degree of heterogeneity across the glycoproteome and to understand how it contributes biologically to complex systems. Here we show that N-glycoproteome site-specific microheterogeneity can be captured via large-scale glycopeptide profiling with methods enabled by activated ion electron transfer dissociation (AI-ETD), ultimately characterizing 1,545 N-glycosites (>5,600 unique N-glycopeptides) from mouse brain tissue. Moreover, we have used this large-scale glycoproteomic data to develop several new visualizations that will prove useful for analyzing intact glycopeptides in future studies. Our data reveal that N-glycosylation profiles can differ between subcellular regions and structural domains and that N-glycosite heterogeneity manifests in several different forms, including dramatic differences in glycosites on the same protein.

scholarly journals DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nasim Bararpour ◽  
Federica Gilardi ◽  
Cristian Carmeli ◽  
Jonathan Sidibe ◽  
Julijana Ivanisevic ◽  
...  
Keyword(s):  
Large Scale ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Model Performance ◽  
Analytical Signal ◽  
R Package ◽  
Statistical Tools ◽  
Statistical Tool ◽  
Signal Drift ◽  
Selection Of

AbstractAs a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

Abstract Motivation Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Results Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. Availability and implementation The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery

2020 ◽  
Author(s):  
Jeroen H.A. Creemers ◽  
Willem J. Lesterhuis ◽  
Niven Mehra ◽  
Winald R. Gerritsen ◽  
Carl G. Figdor ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Patient Survival ◽  
Predictive Biomarkers ◽  
Tipping Point ◽  
Success Rates ◽  
Molecular Approaches ◽  
Patient Status ◽  
New Biomarkers ◽  
The Right ◽  
Selection Of

ABSTRACTBackgroundIn the field of immuno-oncology, predicting treatment response or survival of cancer patients remains a challenge. Efforts to overcome these challenges focus mainly on the discovery of new biomarkers. Owing to the complexity of cancers and their tumor microenvironment, only a limited number of candidate biomarkers eventually enters clinical practice, despite advances in cellular and molecular approaches.MethodsA computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and show its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we extracted fundamental principles underlying the success rates of biomarker discovery programs.ResultsOur main finding is the prediction of a tipping point – a sharp state transition between immune control and immune evasion – that follows a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.ConclusionThese findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as the distance of a patient’ status from the tipping point cannot, in general, be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics could help to optimize strategies in biomarker discovery to ensure accurate selection of the right patient for the right treatment.

scholarly journals From Targeted Quantification to Untargeted Metabolomics

2021 ◽  
Author(s):  
Veronica Lelli ◽  
Antonio Belardo ◽  
Anna Maria Timperio
Keyword(s):  
Biomarker Discovery ◽  
Biological Fluid ◽  
Biological Fluids ◽  
Disease Diagnosis ◽  
Untargeted Metabolomics ◽  
Metabolomic Analysis ◽  
Targeted Metabolomics ◽  
Two Phases ◽  
Technology Tool ◽  
New Biomarkers

Metabolomics is an emerging and rapidly evolving technology tool, which involves quantitative and qualitative metabolite assessments science. It offers tremendous promise for different applications in various fields such as medical, environmental, nutrition, and agricultural sciences. Metabolomic approach is based on global identification of a high number of metabolites present in a biological fluid. This allows to characterize the metabolic profile of a given condition and to identify which metabolites or metabolite patterns may be useful in the discrimination between different groups. The use of one mass spectrometry (MS) platform from targeted quantification to untargeted metabolomics will make more efficient workflows in many fields and should allow projects to be more easily undertaken and realized. Metabolomics can be divided into non-targeted and targeted. The first one can analyze metabolites derived from the organisms comprehensively and systematically, so it is an unbiased metabolomics analysis that can discover new biomarkers. Targeted metabolomics, on the other hand, is the study and analysis of specific metabolites. Both have their own advantages and disadvantages, and are often used in combination for discovery and accurate weight determination of differential metabolites, and allow in-depth research and analysis of subsequent metabolic molecular markers. Targeted and non-targeted metabolomics are involved in food identification, disease research, animal model verification, biomarker discovery, disease diagnosis, drug development, drug screening, drug evaluation, clinical plant metabolism and microbial metabolism research. The aim of this chapter is to highlight the versatility of metabolomic analysis due to both the enormous variety of samples and the no strict barriers between quantitative and qualitative analysis. For this purpose, two examples from our group will be considered. Using non-targeted metabolomics in opposite Antarctic cryptoendolytic communities exposed to the sun, we revealed specific adaptations. Instead, through the targeted metabolomics applied to the urine during childbirth, we identified a different distribution of specific metabolites and the metabolic differences allowed us to discriminate between the two phases of labor, highlighting the metabolites most involved in the discrimination. The choice of these two approaches is to highlight that metabolomic analysis can be applied to any sample, even physiologically and metabolomically very distant, as can be microorganisms living on Antarctic rocks and biological fluids such as urine.

Large-scale rapid detection of circulating microRNAs in plasma for diagnosis and screening of specific diseases

Nanoscale ◽  
2019 ◽  
Vol 11 (36) ◽  
pp. 16879-16885 ◽  
Author(s):  
Han Ding ◽  
Yi An ◽  
Tao Zhao ◽  
Bing Liu ◽  
Yin Wang ◽  
...  
Keyword(s):  
Rapid Detection ◽  
Large Scale ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Circulating Micrornas ◽  
New Biomarkers

miRNAs may serve as new biomarkers that can be used for disease diagnosis. This study will contribute to the development of a universal, large-scale technology for direct in situ detection of miRNAs in patients with heart diseases.

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