• Molecular & Cellular Toxicology
• /
• v.4 no.3
• /
• pp.260-266
• /
• 2008

Recently, there has been scientific discussion on the utility of -omics techniques such as genomics, proteomics, and metabolomics within toxicological research and mechanism-based risk assessment. Toxicogenomics is a novel approach integrating the expression analysis of genes (genomic) or proteins (proteomic) with traditional toxicological methods. Since 1999, the toxicogenomic approach has been extensively applied for regulatory purposes in order to understand the potential toxic mechanisms that result from chemical compound exposures. Therefore, this article's purpose was to consider the utility of toxicogenomic profiles for improved risk assessment, explore the current limitations in applying toxicogenomics to regulation, and finally, to rationalize possible avenues to resolve some of the major challenges. Based on many recent works, the significant impact toxicogenomic techniques would have on human health risk assessment is better identification of toxicity pathways or mode-of-actions (MOAs). In addition, the application of toxicogenomics in risk assessment and regulation has proven to be cost effective in terms of screening unknown toxicants prior to more extensive and costly experimental evaluation. However, to maximize the utility of these techniques in regulation, researchers and regulators must resolve many parallel challenges with regard to data collection, integration, and interpretation. Furthermore, standard guidance has to be prepared for researchers and assessors on the scientifically appropriate use of toxicogenomic profiles in risk assessment. The National Institute of Toxicological Research (NITR) looks forward to an ongoing role as leader in addressing the challenges associated with the scientifically sound use of toxicogenomics data in risk assessment.

• Food Science of Animal Resources
• /
• v.40 no.1
• /
• pp.1-10
• /
• 2020

Various processing methods have a great impact on the physiochemical and nutritional properties of meat that are of health concern. Hence, the postmortem processing of meat by different methods is likely to intensify the potential effects on protein oxidation. The influence of meat protein oxidation on the modulation of the systemic redox status and underlying mechanism is well known. However, the effects of processed meat proteins isolated from different sources on gut microbiota, oxidative stress biomarkers, and metabolomic markers associated with metabolic syndromes are of growing interest. The application of advanced methodological approaches based on OMICS, and mass spectrometric technologies has enabled to better understand the molecular basis of the effect of processed meat oxidation on human health and the aging process. Animal studies indicate the involvement of dietary proteins isolated from different sources on health disorders, which emphasizes the impact of processed meat protein on the richness of bacterial taxa such as (Mucispirillum, Oscillibacter), accompanied by increased expression of lipogenic genes. This review explores the most recent evidences on meat processing techniques, meat protein oxidation, underlying mechanisms, and their potential effects on nutritional value, gut microbiota composition and possible implications on human health.

• Genomics & Informatics
• /
• v.17 no.4
• /
• pp.41.1-41.12
• /
• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• BMB Reports
• /
• v.41 no.8
• /
• pp.609-614
• /
• 2008

The concentrations and catalytic activities of enzymes control metabolic rates. Previous studies have focused on enzyme concentrations because there are no genome-wide techniques used for the measurement of enzyme activity. We propose a method for evaluating the significance of enzyme activity by integrating metabolic network topologies and genome-wide microarray gene expression profiles. We quantified the enzymatic activity of reactions and report the 388 significant reactions in five perturbation datasets. For the 388 enzymatic reactions, we identified 70 that were significantly regulated (P-value < 0.001). Thirty-one of these reactions were part of anaerobic metabolism, 23 were part of low-pH aerobic metabolism, 8 were part of high-pH anaerobic metabolism, 3 were part of low-pH aerobic reactions, and 5 were part of high-pH anaerobic metabolism.

• BMB Reports
• /
• v.53 no.6
• /
• pp.299-310
• /
• 2020

Chronic liver disease progresses through several stages, fatty liver, steatohepatitis, cirrhosis, and eventually, it leads to hepatocellular carcinoma (HCC) over a long period of time. Since a large proportion of patients with HCC are accompanied by cirrhosis, it is considered to be an important factor in the diagnosis of liver cancer. This is because cirrhosis leads to an irreversible harmful effect, but the early stages of chronic liver disease could be reversed to a healthy state. Therefore, the discovery of biomarkers that could identify the early stages of chronic liver disease is important to prevent serious liver damage. Biomarker discovery at liver cancer and cirrhosis has enhanced the development of sequencing technology. Next generation sequencing (NGS) is one of the representative technical innovations in the biological field in the recent decades and it is the most important thing to design for research on what type of sequencing methods are suitable and how to handle the analysis steps for data integration. In this review, we comprehensively summarized NGS techniques for identifying genome, transcriptome, DNA methylome and 3D/4D chromatin structure, and introduced framework of processing data set and integrating multi-omics data for uncovering biomarkers.

• Research in Plant Disease
• /
• v.21 no.3
• /
• pp.161-179
• /
• 2015

Plant have developed sophisticated defence mechanisms against microbial pathogens. The recent accumulated information allow us to understand the nature of plant immune responses followed by recognition of microbial factors/determinants through cutting-edge genomics and multi-omics techniques. However, the practical approaches to sustain plant health using enhancement of plant immunity is yet to be fully appreciated. Here, we overviewed the general concept and representative examples on the plant immunity. The fungal, bacterial, and viral determinants that was previously reported as the triggers of plant immune responses are introduced and described as the potential protocol of biological control. Specifically, the role of chitin, glucan, lipopolysaccharides/extracellular polysaccharides, microbe/pathogen-associated molecular pattern, antibiotics, mimic-phytohormones, N-acyl homoserine lactone, harpin, vitamins, and volatile organic compounds are considered. We hope that this review stimulates scientific community and farmers to broaden their knowledge on the microbial determinant-based biological control and to apply the technology on the integrated pest management program.

• Journal of the Korean Magnetic Resonance Society
• /
• v.22 no.4
• /
• pp.91-100
• /
• 2018

Benzisothiazolinone (BIT) is the preservative that is widely used in industrial and household products. In this study, zebrafish (Danio rerio) was exposed to BIT at different concentrations (control, 0.5 g/L, 1.0 g/L and 2.0 g/L) for 72 hours. The techniques of nuclear magnetic resonance (NMR) spectroscopy were applied to analyze the effects of BIT on zebrafish. The advantages of NMR are the minimal sample preparation and high reproducibility of experimental results. With the multivariate statistical analysis, dimethylamine, N-acetylaspartate, glycine and histidine were identified as an important metabolite in differentiating between the control and BIT-exposed group. This study will improve the understanding the metabolite changes in the zebrafish in response to BIT exposure.

• Journal of Life Science
• /
• v.28 no.10
• /
• pp.1244-1253
• /
• 2018

Rumen microbiome consists of a wide variety of microorganisms, such as bacteria, archaea, protozoa, fungi, and viruses, that are in a symbiotic relationship in a strict anaerobic environment in the rumen. These rumen microbiome, a vital maker, play a significant role in feed fermentation within the rumen and produce different volatile fatty acids (VFAs). VFAs are essential for energy metabolism and protein synthesis of the host animal, even though emission of methane gas after feed fermentation is considered a negative indicator of loss of dietary energy of the host animal. To improve rumen microbial efficiency, a variety of approaches, such as feed formulation, the addition of natural feed additives, dietary feed-microbes, etc., have taken to increase ruminant performance. Recently with the application of high-throughput sequencing or next-generation sequencing technologies, especially for metagenomics and metatranscriptomics of rumen microbiomes, our understanding of rumen microbial diversity and function has significantly increased. The metaproteome and metabolome provide deeper insights into the complicated microbial network of the rumen ecosystem and its response to different ruminant diets to improve efficiency in animal production. This review summarized some recent advances of rumen microbiome techniques, especially "meta-omics," viz. metagenomic, metatranscriptomic, metaproteomic, and metabolomic techniques to increase feed fermentation and utilization in ruminants.

• Journal of Plant Biotechnology
• /
• v.37 no.1
• /
• pp.12-24
• /
• 2010

Plant metabolomics is a research field for identifying all of the metabolites found in a certain plant cell, tissue, organ, or whole plant in a given time and conditions and for studying changes in metabolic profiling as time goes or conditions change. Metabolomics is one of the most recently developed omics for holistic approach to biology and is a kind of systems biology. Metabolomics or metabolite fingerprinting techniques usually involves collecting spectra of crude solvent extracts without purification and separation of pure compounds or not in standardized conditions. Therefore, that requires a high degree of reproducibility, which can be achieved by using a standardized method for sample preparation and data acquisition and analysis. In plant biology, metabolomics is applied for various research fields including rapid discrimination between plant species, cultivar and GM plants, metabolic evaluation of commercial food stocks and medicinal herbs, understanding various physiological, stress responses, and determination of gene functions. Recently, plant metabolomics is applied for characterization of gene function often in combination with transcriptomics by analyzing tagged mutants of the model plants of Arabidopsis and rice. The use of plant metabolomics combined by transcriptomics in functional genomics will be the challenge for the coming year. This review paper attempted to introduce current status and prospects of plant metabolomics research.

• Journal of Ginseng Research
• /
• v.48 no.2
• /
• pp.149-162
• /
• 2024

Ginseng, the roots of Panax species, is an important medicinal herb used as a tonic. As ginsenosides are key bioactive components of ginseng, holistic chemical profiling of them has provided many insights into understanding ginseng. Mass spectrometry has been a major methodology for profiling, which has been applied to realize numerous goals in ginseng research, such as the discrimination of different species, geographical origins, and ages, and the monitoring of processing and biotransformation. This review summarizes the various applications of ginsenoside profiling in ginseng research over the last three decades that have contributed to expanding our understanding of ginseng. However, we also note that most of the studies overlooked a crucial factor that influences the levels of ginsenosides: genetic variation. To highlight the effects of genetic variation on the chemical contents, we present our results of untargeted and targeted ginsenoside profiling of different genotypes cultivated under identical conditions, in addition to data regarding genome-level genetic diversity. Additionally, we analyze the other limitations of previous studies, such as imperfect variable control, deficient metadata, and lack of additional effort to validate causation. We conclude that the values of ginsenoside profiling studies can be enhanced by overcoming such limitations, as well as by integrating with other -omics techniques.