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Identification of early indicators of altered metabolism in normal development using a rodent model system.

ABSTRACT: Although the existence of a close relationship between the early maternal developmental environment, fetal size at birth and the risk of developing disease in adulthood has been suggested, most studies, however, employed experimentally induced intrauterine growth restriction as a model to link this with later adult disease. Because embryonic size variation also occurs under normal growth and differentiation, elucidating the molecular mechanisms underlying these changes and their relevance to later adult disease risk becomes important. The birth weight of rat pups vary according to the uterine horn positions. Using birth weight as a marker, we compared two groups of rat pups - lower birth weight (LBW, 5th to 25th percentile) and average birth weight (ABW, 50th to 75th percentile) - using morphological, biochemical and molecular biology, and genetic techniques. Our results show that insulin metabolism, Pi3k/Akt and Ppar? signaling and the genes regulating growth and metabolism are significantly different in these groups. Methylation at the promoter of the InsII (Ins2) gene and DNA methyltransferase 1 in LBW pups are both increased. Additionally, the Dnmt1 repressor complex, which includes Hdac1, Rb (Rb1) and E2f1, was also upregulated in LBW pups. We conclude that the Dnmt1 repressor complex, which regulates the restriction point of the cell cycle, retards the rate at which cells traverse the G1 or G0 phase of the cell cycle in LBW pups, thereby slowing down growth. This regulatory mechanism mediated by Dnmt1 might contribute to the production of small-size pups and altered physiology and pathology in adult life.

SUBMITTER: Prabakaran AD

PROVIDER: S-EPMC5897726 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Identification of early indicators of altered metabolism in normal development using a rodent model system.

Prabakaran Ashok Daniel AD Karakkat Jimsheena Valiyakath JV Vijayan Ranjit R Chalissery Jisha J Ibrahim Marwa F MF Kaimala Suneesh S Adeghate Ernest A EA Al-Marzouqi Ahmed Hassan AH Ansari Suraiya Anjum SA Mensah-Brown Eric E Emerald Bright Starling BS

Disease models & mechanisms 20180301 3

Although the existence of a close relationship between the early maternal developmental environment, fetal size at birth and the risk of developing disease in adulthood has been suggested, most studies, however, employed experimentally induced intrauterine growth restriction as a model to link this with later adult disease. Because embryonic size variation also occurs under normal growth and differentiation, elucidating the molecular mechanisms underlying these changes and their relevance to lat ...[more]

PMID: 29434026

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Project description:Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.

Project description:MotivationIdentifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individual's pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual are often unavailable in clinical situation. Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient's matched normal data are unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it with accumulated normal samples. We propose and examine personalized extensions of pathway statistics, overrepresentation analysis and functional class scoring, to generate individualized pathway aberrance score.ResultsCollected microarray data of normal tissue of lung and colon mucosa are served as reference to investigate a number of cancer individuals of lung adenocarcinoma (LUAD) and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival-related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified 'amino acid synthesis and interconversion' pathway is a good indicator of LUAD (Area Under the Curve (AUC) 0.982 at independent validation). Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.Availability and implementationThe method was implemented using the R software, available at our Web site: http://bibs.snu.ac.kr/ipas.Supplementary informationSupplementary data are available at Bioinformatics online.

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Identification of early indicators of altered metabolism in normal development using a rodent model system.

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Identification of early indicators of altered metabolism in normal development using a rodent model system.

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