Nutritional genomics: the next frontier in the postgenomic era

2004 ◽  
Vol 16 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Jim Kaput ◽  
Raymond L. Rodriguez
Keyword(s):  
Gene Expression ◽  
Chronic Disease ◽  
Dietary Intervention ◽  
Molecular Genetic ◽  
Genomic Research ◽  
Nutritional Requirement ◽  
Disease States ◽  
Nutritional Environment ◽  
Nutritional Genomics ◽  
Genetic Makeup

The interface between the nutritional environment and cellular/genetic processes is being referred to as “nutrigenomics.” Nutrigenomics seeks to provide a molecular genetic understanding for how common dietary chemicals (i.e., nutrition) affect health by altering the expression and/or structure of an individual’s genetic makeup. The fundamental concepts of the field are that the progression from a healthy phenotype to a chronic disease phenotype must occur by changes in gene expression or by differences in activities of proteins and enzymes and that dietary chemicals directly or indirectly regulate the expression of genomic information. We present a conceptual basis and specific examples for this new branch of genomic research that focuses on the tenets of nutritional genomics: 1) common dietary chemicals act on the human genome, either directly or indirectly, to alter gene expression or structure; 2) under certain circumstances and in some individuals, diet can be a serious risk factor for a number of diseases; 3) some diet-regulated genes (and their normal, common variants) are likely to play a role in the onset, incidence, progression, and/or severity of chronic diseases; 4) the degree to which diet influences the balance between healthy and disease states may depend on an individual’s genetic makeup; and 5) dietary intervention based on knowledge of nutritional requirement, nutritional status, and genotype (i.e., “individualized nutrition”) can be used to prevent, mitigate, or cure chronic disease.

Neuroserpin in Bipolar Disorder

2020 ◽  
Vol 20 (7) ◽  
pp. 518-523
Author(s):  
Rugül Köse Çinar
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Polymerase Chain Reaction Method ◽  
Type I ◽  
Healthy Controls ◽  
Neuroprotective Effects ◽  
Expression Levels ◽  
Disease States ◽  
Cord Injury ◽  
System Functioning

Objective: Neuroserpin is a serine protease inhibitor predominantly expressed in the nervous system functioning mainly in neuronal migration and axonal growth. Neuroprotective effects of neuroserpin were shown in animal models of stroke, brain, and spinal cord injury. Postmortem studies confirmed the involvement of neuroserpin in Alzheimer’s disease. Since altered adult neurogenesis was postulated as an aetiological mechanism for bipolar disorder, the possible effect of neuroserpin gene expression in the disorder was evaluated. Methods: Neuroserpin mRNA expression levels were examined in the peripheral blood of bipolar disorder type I manic and euthymic patients and healthy controls using the polymerase chain reaction method. The sample comprised of 60 physically healthy, middle-aged men as participants who had no substance use disorder. Results: The gene expression levels of neuroserpin were found lower in the bipolar disorder patients than the healthy controls (p=0.000). The neuroserpin levels did not differ between mania and euthymia (both 96% down-regulated compared to the controls). Conclusion: Since we detected differences between the patients and the controls, not the disease states, the dysregulation in the neuroserpin gene could be interpreted as a result of the disease itself.

scholarly journals Breast Cancer Type Classification Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Jiande Wu ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Genomic Research ◽  
Support Vector ◽  
Cancer Type ◽  
Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

scholarly journals Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

Genes ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 525 ◽  
Author(s):  
Samar Tareen ◽  
Michiel Adriaens ◽  
Ilja Arts ◽  
Theo de Kok ◽  
Roel Vink ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Weight Loss ◽  
Adipose Tissue ◽  
Molecular Mechanisms ◽  
Dietary Intervention ◽  
Network Biology ◽  
Analysis Tool ◽  
Cellular Processes ◽  
Time Series Gene Expression

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

scholarly journals miRNAome expression profiles in the gonads of adultMelopsittacus undulatus

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4615 ◽  
Author(s):  
Lan Jiang ◽  
Qingqing Wang ◽  
Jue Yu ◽  
Vinita Gowda ◽  
Gabriel Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Early Development ◽  
Expression Profiles ◽  
Critical Role ◽  
Genomic Research ◽  
Go Annotation ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Mirnas Expression ◽  
Paired End Sequencing

The budgerigar (Melopsittacus undulatus) is one of the most widely studied parrot species, serving as an excellent animal model for behavior and neuroscience research. Until recently, it was unknown how sexual differences in the behavior, physiology, and development of organisms are regulated by differential gene expression. MicroRNAs (miRNAs) are endogenous short non-coding RNA molecules that can post-transcriptionally regulate gene expression and play a critical role in gonadal differentiation as well as early development of animals. However, very little is known about the role gonadal miRNAs play in the early development of birds. Research on the sex-biased expression of miRNAs in avian gonads are limited, and little is known aboutM. undulatus. In the current study, we sequenced two small non-coding RNA libraries made from the gonads of adult male and female budgerigars using Illumina paired-end sequencing technology. We obtained 254 known and 141 novel miRNAs, and randomly validated five miRNAs. Of these, three miRNAs were differentially expressed miRNAs and 18 miRNAs involved in sexual differentiation as determined by functional analysis with GO annotation and KEGG pathway analysis. In conclusion, this work is the first report of sex-biased miRNAs expression in the budgerigar, and provides additional sequences to the avian miRNAome database which will foster further functional genomic research.

scholarly journals Metabolic and molecular-genetic changes in the liver during carbon tetrachloride intoxication

2020 ◽  
Vol 99 (9) ◽  
pp. 996-1000
Author(s):  
Denis O. Karimov ◽  
Tatyana G. Kutlina ◽  
Guzel’ F. Mukhammadiyeva ◽  
Yana V. Valova ◽  
Samat S. Baygildin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Serum ◽  
Pearson Correlation ◽  
Molecular Genetic ◽  
Experimental Studies ◽  
Pcr Amplification ◽  
Statistical Processing ◽  
Male Rats ◽  
Clinical Test ◽  
Genetic Changes

Introduction. Toxic hepatitis (TH) is a complex and multifaceted disease, the development of which is mediated by a complex of biochemical and molecular genetic interactions. The current understanding of the pathogenesis of TH and, as a consequence, its treatment is based on standardization of the phenotype of the disease, often without taking into account metabolic disorders within the cells. Material and methods. experimental studies were performed on white outbred male rats weighing 200-220 g. A 50% solution of TCM was used as a toxicant. Biochemical studies were performed on a laboratory medical photometer “Stat Fax 3300” using clinical test kits and control materials manufactured by Vector-Best LLC. Liver tissue for histological examination was subjected to the standard histological procedure and paraffin embedding. Sections 5-7 μm thick were stained with hematoxylin-eosin. Gene expression analysis was performed using real-time PCR amplification on a RotorGene instrument (QIAGEN). Statistical processing of experimental data was performed using the Pearson correlation coefficient and one-way analysis of variance (ANOVA). The results were considered reliable at p <0.05. Results. As a result of the analysis of the correlation of the expression of the studied genes and the level of biochemical parameters, it was found that the correlation of the expression of the Nfe2l2 and Gstm1 genes was r = 0.812 (p = 0.0001). The dynamics of gene expression of Chek, Gstm1, Gstp1, Nfe2l2, had a negative correlation with the level of AST activity in blood serum. And the expression of the genes Chek, Gclc, Gstm1, Nfe2l2, Ripk, Sod1 with an index of ALT activity in the blood serum. After 72 hours, the expression of almost all of the studied genes became multidirectional. And the correlation between indices is often not determined. An analysis of the relationship between the level of cytolysis enzymes and the correlation level of the studied genes showed that after 72 hours the correlation was observed in the Gstm1, Hmox, and Sod1 genes with the levels of AST and ALT.

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