• BMB Reports
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• v.45 no.6
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• pp.323-330
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• 2012

The glycome consists of all glycans (or carbohydrates) within a biological system, and modulates a wide range of important biological activities, from protein folding to cellular communications. The mining of the glycome for disease markers represents a new paradigm for biomarker discovery; however, this effort is severely complicated by the vast complexity and structural diversity of glycans. This review summarizes recent developments in analytical technology and methodology as applied to the fields of glycomics and glycoproteomics. Mass spectrometric strategies for glycan compositional profiling are described, as are potential refinements which allow structure-specific profiling. Analytical methods that can discern protein glycosylation at a specific site of modification are also discussed in detail. Biomarker discovery applications are shown at each level of analysis, highlighting the key role that glycoscience can play in helping scientists understand disease biology.

• KSBB Journal
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• v.26 no.4
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• pp.352-356
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• 2011

The detection of biomarkers is an important issue for disease diagnosis. However, many systems are not suitable to detect the biomarker itself directly. For direct detection of biomarker proteins in human serum, a new affinity-capture method using aptamers combined with the mass spectrometry was suggested. Since signals from protein samples cannot be amplified, modified chromatin immunoprecipitation (ChIP) and subsequent cross-linking with formaldehyde between aptamers and target proteins were used not to lose the captured target proteins, which allowed us to perform a harsh washing step to remove the non-specifically bound proteins. As a model system, a thrombin aptamer was used as a bait and thrombin as a target protein. Using our modified ChIP and affinity-capture method, non-specific binding proteins on the beads decreased significantly, suggesting that our new method is efficient and can be applied to developing diagnosis systems for various biomarkers.

• Molecules and Cells
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• v.43 no.4
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• pp.384-396
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• 2020

Breast cancer is one of the most common life-threatening malignancies and the top cause of cancer deaths in women. Although many conventional therapies exist for its treatment, breast cancer still has many handicaps to overcome. Cancer stem cells (CSCs) are a well-known cause of tumor recurrences due to the ability of CSCs for self-renewal and differentiation into cell subpopulations, similar to stem cells. To fully treat breast cancer, a strategy for the treatment of both cancer cells and CSCs is required. However, current strategies for the eradication of CSCs are non-specific and have low efficacy. Therefore, surface biomarkers to selectively treat CSCs need to be developed. Here, 34 out of 641 surface biomarkers on CSCs were identified by proteomic analysis between the human breast adenocarcinoma cell line MCF-7 and MCF-7-derived CSCs. Among them, carcinoembryonic antigen-related cell adhesion molecules 6 (CEACAM6 or CD66c), a member of the CEA family, was selected as a novel biomarker on the CSC surface. This biomarker was then experimentally validated and evaluated for use as a CSC-specific marker. Its biological effects were assessed by treating breast cancer stem cells (BCSCs) with short hairpin (sh)-RNA under oxidative cellular conditions. This study is the first to evaluate the biological function of CD66c as a novel biomarker on the surface of CSCs. This marker is available as a moiety for use in the development of targeted therapeutic agents against CSCs.

• Toxicological Research
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• v.6 no.2
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• pp.205-213
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• 1990

The regulation of neurotoxicants has usually been based upon setting reference doses by dividing a no observed adverse effect level (NOAEL) by uncertainty factors that theoretically account for interspecies and intraspecies extraploation of experimental results in animals to humans. Recently, we have proposed a four-step alternative procedure which provides quantitative estimates of risk as a function of dose. The first step is to establish a mathematical relationship between a biological effect or biomarker and the dose of chemical administered. The second step is to determine the distribution (variability) of individual measurements of biological effects or their biomarkers about the dose response curve. The third step is to define an adverse or abnormal level of a biological effect or biomarker in an untreated population. The fourth and final step is to combine the information from the first three steps to estimate the risk (proportion of individuals exceeding on adverse or abnormal level of a biological effect or biomarker) as a function of dose. The primary purpose of this report is to enhance the certainty of the first step of this procedure by improving our understanding of the relationship between a biomarker and dose of administered chemical. Several factors which need to be considered include: 1) the pharmacokinetics of the parent chemical, 2) the target tissue concentrations of the parent chemical or its bioactivated proximate toxicant, 3) the uptake kinetics of the parent chemical or metabolite into the target cell(s) and/or membrane interactions, and 4) the interaction of the chemical or metabolite with presumed receptor site(s). Because these theoretical factors each contain a saturable step due to definitive amounts of required enzyme, reuptake or receptor site(s), a nonlinear, saturable dose-response curve would be predicted. In order to exemplify this process, effects of the neurotoxicant, methlenedioxymethamphetamine (MDMA), were reviewed and analyzed. Our results and those of others indicate that: 1) peak concentrations of MDMA and metabolites are ochieved in rat brain by 30 min and are negligible by 24 hr, 2) a metabolite of MDMA is probably responsible for its neurotoxic effects, and 3) pretreatment with monoamine uptake blockers prevents MDMA neurotoxicity. When data generated from rats administerde MDMA were plotted as bilolgical effect (decreases in hippocampal serotonin concentrations) versus dose, a saturation curve best described the observed relationship. These results support the hypothesis that at least one saturable step is involved in MDMA neurotoxicity. We conclude that the mathematical relationship between biological effect and dose of MDMA, the first step of our quantitative neurotoxicity risk assessment procedure, should reflect this biological model information generated from the whole of the dose-response curve.

• Toxicological Research
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• v.24 no.4
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• pp.235-243
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• 2008

While in some instances, loss of diversity results from acute toxicity (e.g. major pollution incidents), in most cases it results from long-term sub-lethal effects that alter the relative competitive ability and fitness of certain organisms. In such cases the sub-lethal effects will cause a physiological response in the organism that ultimately leads to community level changes. Very sensitive tools are now available to study sub-lethal responses at the molecular level. However, relating such laboratory measurements to ecological effects represents a substantial challenge that can only be met by investigation at all scales (molecular, individual organism and community level) with an appropriate group of organisms. Among the various in vertebrates which can be used as model organisms in such a way, the soil nematode, Caenorhabditis elegans appear to be a promising biological model to diagnose environmental quality. This paper reviews the current status of multilevel biomarkers in environmental toxicology, and C. elegans as promising organisms for this approach.

• Journal of Life Science
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• v.19 no.1
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• pp.138-146
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• 2009

Garlic (Allium sativum) are an agronomically important genus because of their sulfur flavour components. The majority of the volatiles flavour principles are generated through the enzymatic hydrolysis of the non-volatile organosulfur compounds. However, these compounds may be possible sources of new novel bioacuve and therapeutic principles. Garlic has strong antioxidant activity, and epidemiological studies support the fad that diets rich of garlic may prevent some of the chronic diseases. The health cares of garlic likely arise from a wide variety of components, which may work synergistically. The chemical changes of garlic composition makes it plausible that a variation in processing can lead to acquisition of differential chemical compositions of garlic products. Especially highly unstable allicin can easily disappear during processing and are quickly transformed into a various organosulfur compounds. Various supplements of garlic, particularly aged garlic extract (AGE), are known to possess a promising antioxidant potential and are effective in prevention of chronic diseases because of the bioactive constituents. Although all of active ingredients of AGE are not elucidated, water-soluble components of AGE, including S-allylcysteine, S-allylmercaptane, steroid saponins, tetrahydro-${\beta}$-carboline derivatives, and fructosyl-arginine, appears to be associated with the pharmacological effects of AGE. Consequently, the allicin free garlic components such as S-allylcysteine, S-allylmercaptane, steroid saponins, tetrahydro-${\beta}$-carboline derivatives, and fructosyl-arginine can be applicable to standardization of the quality of commercial garlic products. This review provides an insight into garlic's biomarkers and presents evidence that they may either prevent or delay chronic disease associated with aging.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.271-276
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• 2017

Cancer biomarkers are using in the diagnosis, staging, prognosis and prediction of disease progression. But, there are not sufficiently profiled and validated in early detection and risk classification of prostate cancer. In this study, we have devoted to finding a panel of serum biomarkers that are able to detect the diagnosis of prostate cancer. The serum samples were consisted of 111 prostate cancer and 343 control samples and examined. Eleven biomarkers were constructed in this study, and then nine biomarkers were relevant to candidate biomarkers by using t test. Finally, four biomarkers, PSA, ApoA2, CYFRA21.1 and TTR, were selected as the prostate cancer biomarker panel, logistic regression was used to identify algorithms for diagnostic biomarker combinations(AUC = 0.9697). A panel of combination biomarkers is less invasive and could supplement clinical diagnostic accuracy.

• Tuberculosis and Respiratory Diseases
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• v.83 no.3
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• pp.185-194
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• 2020

Blood eosinophil counts have emerged as a chronic obstructive pulmonary disease (COPD) biomarker that predict the effects of inhaled corticosteroids (ICS) in clinical practice. Post-hoc and prospective analysis of randomized control trials have shown that higher blood eosinophil counts at the start of the study predict a greater response to ICS. COPD patients with frequent exacerbations (2 or more moderate exacerbations/yr) or a history of hospitalization have a greater response to ICS. Ex-smokers also appear to have a greater ICS response. Blood eosinophil counts can be combined with clinical information such as exacerbation history and smoking status to enable a precision medicine approach to the use of ICS. Higher blood eosinophil counts are associated with increased eosinophilic lung inflammation, and other biological features that may contribute to the increased ICS response observed. Emerging data indicates that lower blood eosinophil counts are associated with an increased risk of bacterial infection, suggesting complex relationships between eosinophils, ICS response, and the airway microbiome.

• Journal of Veterinary Science
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• v.23 no.4
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• pp.51.1-51.10
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• 2022

Background: Due to the unavailability of an effective vaccine or antiviral drug against the African swine fever virus (ASFV), rapid diagnosis methods are needed to prevent highly contagious African swine fever. Objectives: The objective of this study was to establish the ladder-shape melting temperature isothermal amplification (LMTIA) assay for the detection of ASFV. Methods: LMTIA primers were designed with the p72 gene of ASFV as the target, and plasmid pUC57 was used to clone the gene. The LMTIA reaction system was optimized with the plasmid as the positive control, and the performance of the LMTIA assay was compared with that of the commercial real-time polymerase chain reaction (PCR) kit in terms of sensitivity and detection rate using 200 serum samples. Results: Our results showed that the LMTIA assay could detect the 10⁴ dilution of DNA extracted from the positive reference serum sample, which was the same as that of the commercial real-time PCR kit. The coincidence rate between the two assays was 100%. Conclusions: The LMTIA assay had high sensitivity, good detection, and simple operation. Thus, it is suitable for facilitating preliminary and cost-effective surveillance for the prevention and control of ASFV.

• Korean Journal of Biological Psychiatry
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• v.30 no.1
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• pp.24-30
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• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.