Genomics in Metabolomics PDF

Metabolic Engineering-BIOT 7340 Metabolic characterization Genome Dr. Gaurav Kumar Assistant Professor Department of Life Sciences and Biotechnology CSJMU, Kanpur Blind Man and the Elephant Systems biology is an approach in research to understanding the larger picture—be it at the level of the...

Metabolic Engineering-BIOT 7340 Metabolic characterization Genome Dr. Gaurav Kumar Assistant Professor Department of Life Sciences and Biotechnology CSJMU, Kanpur Blind Man and the Elephant Systems biology is an approach in research to understanding the larger picture—be it at the level of the organism, tissue, or cell—by putting its pieces together. Genomics in Metabolomics studies Metabolic Pathway Identification MetaboliteGenome Integration Sustainable Agriculture Crop Improvement Metabolite Annotation Stress Responses Pathway Engineering How Genomics is different from the Genetics Genetics look like a single gene one at a time like a picture or snap shot Genomics look at the big picture and examine all the genes at the entire system Genomics is sub discipline part of genetics which is devoted for • Mapping • Sequencing • Functional analysis of Genomics Genomics is the study of all of a person's genes (the genome), including interactions of those genes with each other and with the person's environment.. Genomics is a broad and interdisciplinary field that encompasses various aspects of genetics, molecular biology, bioinformatics • • • • • • • • • • • • • Gene Identification Gene Structure Gene Function Gene Regulation Comparative Genomics Genomic Variations Genome Size and Organization Genetic Markers Evolutionary History Biomedical Insights/Crop Improvement Population Genetics Functional Elements Conservation Biology Sanger Sequencing Illumina Solexa Large-scale sequencing and de novo sequencing Ion Torrrent and Illumina miseq The Encyclopedia of DNA Elements (ENCODE) project • A functional element is defined as a discrete genome segment that either encodes a product (e.g. protein or noncoding RNA) or displays a reproducible biochemical signature (e.g. protein binding, or a specific chromatin structure). • ENCODE, initiated in 2003 to characterize 1% of the human genome, the scope of ENCODE has been broadened since 2007 to study DNA elements in the whole human genome. • Based on the analysis, about 80% of the genome was assigned some kind of genetic function, either RNA- associated or chromatin-associated. • About 95% of the genome was found to lie within 8 kb of a DNA • protein interaction, and 99% within 1.7 kb of at least one of the biochemical events measured by ENCODE. The analysis annotated 8801 small RNA and 9640 long noncoding RNAcoding loci. Most transcribed bases were found to be within annotated genes or in overlapping annotated gene boundaries; that is, in noncoding DNA. Also, 11,224 pseudogenes were annotated, of which 863 are transcribed and associated with active chromatin. • Functional Non-Coding DNA: These functions include regulatory roles in gene expression, controlling the structure and packaging of DNA, and contributing to the stability and maintenance of the genome. • Regulatory Elements: Non-coding DNA contains regulatory elements, such as promoters, enhancers, and silencers, which play crucial roles in the control of gene expression. • • • • • • • • Transcripts and RNAs: Transcribe ribosomal RNAs, transfer RNAs, microRNAs, and long noncoding RNAs. Structural and Repetitive Elements: Non-coding DNA includes structural elements, like telomeres and centromeres, which are essential for chromosome stability and segregation. Evolutionary Significance: Some non-coding DNA has evolutionary significance. Disease Associations: Mutations or variations in non-coding regions can be associated with various diseases and genetic disorders. Comparative Genomics Genome wide association studies • Goal: find connections between: – A phenotype: height, type-I diabetes, etc., known to be heritable – Whole-genome genotype • Specific goals are distinct: 1. Identify statistical connections between points (or areas) in the genome and the phenotype • Drive hypotheses for biological studies of specific genes/regions in specific context 2. Generate insights on genetic architecture of phenotype • Many small genetic effects dispersed across the genome? • Few large effects concentrated in one area (MHC?) 3. Build statistical models to predict phenotype from genotype • “Show me your genome and I will tell you what diseases you will get” • Gene: A sequence of DNA that contains instructions for a cell to make a specific protein that the body needs. • Genome: The sum of all of your genes. Humans carry an estimated 20,000-25,000 protein-coding genes. Polymorphisms within those genes make one person different from the next. • Phenotype: The physiological characteristic(s) imparted by a genetic variation. • Genotype: The version of a gene that a person carries, i.e. whether a polymorphism affects its sequence. Genotype is the output of a genetic test. • Polymorphism: A DNA sequence variation that alters the gene in some way. Often, it is a simple substitution of one letter (nucleotide) for another (e.g. A –> C). Less commonly, the gene can be duplicated or deleted, or contain an inserted or deleted piece of DNA. Polymorphisms do not independently cause disease, but they can be risk factors. They can also have health benefits. • Mutation: A rare DNA sequence variation that creates a different version of a gene. Like SNPs, mutations can involve one nucleotide (point mutations). Alternatively, they can involve deletions of large sections of DNA or entire chromosomes. In contrast to polymorphisms, mutations have potentially serious phenotypes that are more difficult to modify through diet, lifestyle and environment. • Penetrance: The proportion of carriers with a genetic variation that express the associated trait (phenotype). Polymorphisms have low penetrance, while mutations have high penetrance. Single Nucleotide Polymorphism (SNP): A DNA sequence variation in which one nucleotide is substituted for another. Sometimes this affects the encoded protein, but most of the time, it has no effect. SNPs of medical significance cause a substitution in the regulatory or coding regions that affects the expression or function of the protein A growing number of polymorphisms are clearly associated with a clinical trait. Examples include CYP1A2 rs762551 and PEMT rs12325817, which alter caffeine and choline metabolism, respectively. Figure 2. Prevalence of metabolic gene clusters in plants. A and B, Number of all predicted metabolic gene clusters of ... Plant Physiol, Volume 173, Issue 4, April 2017, Pages 2041–2059, https://doi.org/10.1104/pp.16.01942 The content of this slide may be subject to copyright: please see the slide notes for details. Pharmacogenetics and drug development • Pharmacogenetics was first used in relation to phenotypic variation in metabolism and response to certain drugs. • As gene cloning advanced to sequencing of the entire human genome, the term pharmacogenomics, which was first used in 1997 started to be used in addition to pharmacogenetics. • The two terms are now used interchangeably though the scope of pharmacogenomics is broader and extends to the development of new drugs to target specific disease genes. • Pharmacogenomics uses information about a person's genetic makeup, or genome, to choose the drugs and drug doses that are likely to work best for that particular person. This new field combines the science of how drugs work, called pharmacology, with the science of the human genome, called genomics. Receptors Breast Cancer and T-DM1. Some breast cancers make too much HER2, a receptor, and this extra HER2 helps the cancer develop and spread. The drug T-DM1 can be used to treat this type of breast cancer and works by attaching to HER2 on cancerous cells and killing them. If you have breast cancer, your doctor may test a sample of your tumor to determine if T-DM1 is the right treatment for you. If your tumor has a high amount of HER2 (HER2 positive), your doctor may prescribe T-DM1. If your tumor does not have enough HER2 (HER2 negative), T-DM1 will not work for you. Bioinformatic approaches have recently identified hundreds of metabolic gene clusters in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa), and sorghum (Sorghum bicolor; Chae et al., 2014) and collocated gene pairs between terpene synthases and oxidoreductases in several plant species (Boutanaev et al., 2015). The phenomenon of clustering may point to and assist in the discovery of unknown metabolic pathways and novel enzymes. Nevertheless, the prevalence and genesis of metabolic gene clustering in plants remain open questions. what biological roles do these metabolites play in how plants adapt to their environmental niches, and how do plants communicate with their beneficial partners and in the ongoing warfare against viruses, pathogens, and parasites? How did plants evolve to gain and maintain the metabolic repertoire? Finally, how can we use this knowledge to produce more and better food, industrial materials, and medicine?