• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.701-714
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• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• Molecules and Cells
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• v.44 no.7
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• pp.500-516
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• 2021

Cardiac hypertrophic signaling cascades resulting in heart failure diseases are mediated by protein phosphorylation. Recent developments in mass spectrometry-based phosphoproteomics have led to the identification of thousands of differentially phosphorylated proteins and their phosphorylation sites. However, functional studies of these differentially phosphorylated proteins have not been conducted in a large-scale or high-throughput manner due to a lack of methods capable of revealing the functional relevance of each phosphorylation site. In this study, an integrated approach combining quantitative phosphoproteomics and cell-based functional screening using phosphorylation competition peptides was developed. A pathological cardiac hypertrophy model, junctate-1 transgenic mice and control mice, were analyzed using label-free quantitative phosphoproteomics to identify differentially phosphorylated proteins and sites. A cell-based functional assay system measuring hypertrophic cell growth of neonatal rat ventricle cardiomyocytes (NRVMs) following phenylephrine treatment was applied, and changes in phosphorylation of individual differentially phosphorylated sites were induced by incorporation of phosphorylation competition peptides conjugated with cell-penetrating peptides. Cell-based functional screening against 18 selected phosphorylation sites identified three phosphorylation sites (Ser-98, Ser-179 of Ldb3, and Ser-1146 of palladin) displaying near-complete inhibition of cardiac hypertrophic growth of NRVMs. Changes in phosphorylation levels of Ser-98 and Ser-179 in Ldb3 were further confirmed in NRVMs and other pathological/physiological hypertrophy models, including transverse aortic constriction and swimming models, using site-specific phospho-antibodies. Our integrated approach can be used to identify functionally important phosphorylation sites among differentially phosphorylated sites, and unlike conventional approaches, it is easily applicable for large-scale and/or high-throughput analyses.

• The Korean Journal of Pesticide Science
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• v.7 no.1
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• pp.1-11
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• 2003

Acoording to improvement of HTOS (high throughput organic synthesis) and HTS (high throughput screening) technique, the CoMFA (comparative molecular field analysis), CoMSIA (comparative molecular similarity indeces analysis) and molecular HQSAR (hologram quantitative structure-activity relationship) analysis techniques as methodology of computer assisted molecular design (CAMD) were introduced generally and summarized for some application cases.

• Mass Spectrometry Letters
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• v.2 no.3
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• pp.69-72
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• 2011

Defective synthesis of the steroid hormones by the adrenal cortex has profound effects on human development and homeostasis. Due to the time-consuming chromatography procedure combined with mass spectrometry, the matrix-assisted laser desorption ionization method coupled to the linear ion-trap tandem mass spectrometry (MALDI-LTQ-MS/MS) was developed for quantitative analysis of 10 adrenal steroids in human serum. Although MALDI-MS can be introduced for its applicability as a high-throughput screening method, it has a limitation on reproducibility within and between samples, which renders poor reproducibility for quantification. For quantitative MALDI-MS/MS analysis, the stable-isotope labeled internal standards were used and the conditions of crystallization were tested. The precision and accuracy were 3.1~35.5% and 83.8~138.5%, respectively, when a mixture of 10 mg/mL ${\alpha}$-cyano-4-hydroxycinnamic acid in 0.2% TFA of 70% acetonitrile was used as the MALDI matrix. The limit of quantification ranged from 5 to 340 ng/mL, and the linearity as a correlation coefficient was higher than 0.988 for all analytes in the calibration range. Clinical applications include quantitative analyses of patients with congenital adrenal hyperplasia. The devised MALDI-MS/MS technique could be successfully applied to diagnosis of clinical samples.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.123-137
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• 2018

A nonlinear mixed effects model is mainly used to analyze repeated measurement data in various fields. A nonlinear mixed effects model consists of two stages: the first-stage individual-level model considers intra-individual variation and the second-stage population model considers inter-individual variation. The individual-level model, which is the first stage of the nonlinear mixed effects model, estimates the parameters of the nonlinear regression model. It is the same as the general nonlinear regression model, and usually estimates parameters using the least squares estimation method. However, the least squares estimation method may have a problem that the estimated value of the parameters and standard errors become extremely large if the assumed nonlinear function is not explicitly revealed by the data. In this paper, a new estimation method is proposed to solve this problem by introducing the ridge regression method recently proposed in the nonlinear regression model into the first-stage individual-level model of the nonlinear mixed effects model. The performance of the proposed estimator is compared with the performance with the standard estimator through a simulation study. The proposed methodology is also illustrated using quantitative high throughput screening data obtained from the US National Toxicology Program.

• Genomics & Informatics
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• v.11 no.4
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.7
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• pp.1062-1071
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• 2018

The misuse of anabolic hormones or illegal drugs is a ubiquitous problem in animal husbandry and in food safety. The ban on growth promotants in food producing animals in the European Union is well controlled. However, application regimens that are difficult to detect persist, including newly designed anabolic drugs and complex hormone cocktails. Therefore identification of molecular endogenous biomarkers which are based on the physiological response after the illicit treatment has become a focus of detection methods. The analysis of the 'transcriptome' has been shown to have promise to discover the misuse of anabolic drugs, by indirect detection of their pharmacological action in organs or selected tissues. Various studies have measured gene expression changes after illegal drug or hormone application. So-called transcriptomic biomarkers were quantified at the mRNA and/or microRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology or by more modern 'omics' and high throughput technologies including RNA-sequencing (RNA-Seq). With the addition of advanced bioinformatical approaches such as hierarchical clustering analysis or dynamic principal components analysis, a valid 'biomarker signature' can be established to discriminate between treated and untreated individuals. It has been shown in numerous animal and cell culture studies, that identification of treated animals is possible via our transcriptional biomarker approach. The high throughput sequencing approach is also capable of discovering new biomarker candidates and, in combination with quantitative RT-qPCR, validation and confirmation of biomarkers has been possible. These results from animal production and food safety studies demonstrate that analysis of the transcriptome has high potential as a new screening method using transcriptional 'biomarker signatures' based on the physiological response triggered by illegal substances.

• Journal of Plant Biotechnology
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• v.34 no.3
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• pp.207-212
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• 2007

This study describes a simple and rapid method for quantitative determination of $\gamma$-aminobutyric acid (GABA) using $^1H$ NMR spectroscopy from whole cell extracts of plant suspension cultures. When 9 cell lines derived from 8 species of higher plants maintained in liquid Marashige and Skoog (MS) medium supplemented with 1 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) were subjected to $^1H$ NMR, a cell line of Coriandrum sativum L. exhibited the highest level of GABA. The level reached up to 16.9 mg/dry wt when cells were cultured in MS medium supplemented with 0.5 mg/L 2,4-D after 3 weeks of incubation. The method for quantitative determination of GABA using $^1H$ NMR established in this study could be applied to high-throughput screening of various plant resources for GABA production and the cell suspension culture system of C. sativum could be further developed for commercial production of GABA.

• Interdisciplinary Bio Central
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• v.4 no.3
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• pp.6.1-6.12
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• 2012

Parkinson's disease (PD) is a complicated neurodegenerative disorder although it is oftentimes defined by clinical motor symptoms originated from age dependent and progressive loss of dopaminergic neurons in the midbrain. The pathogenesis of PD involves dopaminergic and nondopaminergic neurons in many brain regions and the molecular mechanisms underlying the death of different cell types still remain to be elucidated. There are indications that PD causing disease processes occur in a global scale ranging from DNA to RNA, and proteins. Several PD-associated genes have been reported to play diverse roles in controlling cellular functions in different levels, such as chromatin structure, transcription, processing of mRNA, translational modulation, and posttranslational modification of proteins. The advent of quantitative high throughput screening (HTS) tools makes it possible to monitor systemic changes in DNA, RNA and proteins in PD models. Combined with dopamine neuron isolation or derivation of dopamine neurons from PD patient specific induced pluripotent stem cells (PD iPSCs), HTS techonologies will provide opportunities to draw PD causing sequences of molecular events in pathologically relevant PD samples. Here I discuss previous studies that identified molecular functions in which PD genes are involved, especially those signaling pathways that can be efficiently studied using HTS methodologies. Brief descriptions of quantitative and systemic tools looking at DNA, RNA and proteins will be followed. Finally, I will emphasize the use and potential benefits of PD iPSCs-derived dopaminergic neurons to screen signaling pathways that are initiated by PD linked gene mutations and thus causative for dopaminergic neurodegneration in PD.

• Parasites, Hosts and Diseases
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• v.53 no.1
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• pp.85-93
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• 2015

Proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. For detection of antigens from Haemaphysalis longicornis, 1-dimensional electrophoresis (1-DE) quantitative immunoblotting technique combined with 2-dimensional electrophoresis (2-DE) immunoblotting was used for whole body proteins from unfed and partially fed female ticks. Reactivity bands and 2-DE immunoblotting were performed following 2-DE electrophoresis to identify protein spots. The proteome of the partially fed female had a larger number of lower molecular weight proteins than that of the unfed female tick. The total number of detected spots was 818 for unfed and 670 for partially fed female ticks. The 2-DE immunoblotting identified 10 antigenic spots from unfed females and 8 antigenic spots from partially fed females. Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF) of relevant spots identified calreticulin, putative secreted WC salivary protein, and a conserved hypothetical protein from the National Center for Biotechnology Information and Swiss Prot protein sequence databases. These findings indicate that most of the whole body components of these ticks are non-immunogenic. The data reported here will provide guidance in the identification of antigenic proteins to prevent infestation and diseases transmitted by H. longicornis.