This document provides an overview of bioinformatics and computational genomics. It discusses key topics including DNA structure and function, genetic code, DNA replication, mutations, epigenetics, chromatin structure, histone modifications, DNA methylation, cancer stem cells, personalized medicine using biomarkers, and molecular profiling. The document contains diagrams explaining concepts like DNA packaging into chromatin, basic epigenetic mechanisms involving histone modifications and DNA methylation, and how epigenetic changes can alter chromatin structure and regulate gene expression.
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
Genomics and Proteomics provides an overview of genomics and proteomics methods and their applications in medicine. It discusses how the fields of genomics and molecular biology emerged through key advances in DNA structure, recombinant DNA technology, PCR, and automated DNA sequencing. The document also reviews epigenetics, genomic and proteomic techniques including blotting, PCR, microarrays, and mass spectrometry. Applications of these methods in clinical settings are described such as genomic tests for disease diagnosis, prognosis, and personalized medicine. Proteomics uses mass spectrometry to discover biomarkers for diseases like cancer.
This document discusses using whole genome sequencing and variant prioritization tools to diagnose rare genetic diseases in individuals. It describes how sequencing the whole genome reveals millions of variants, but focusing on the exome and protein-coding regions reduces this to tens of thousands of variants. Further prioritization by effects on protein function, rarity, predicted damage, and inheritance patterns can narrow this down to a handful of likely disease-causing genes. The document presents a case study of a child with liver disease who was diagnosed with a rare genetic condition called PFIC2 after family-wide exome sequencing and analysis with the VAAST software prioritization tool. It concludes that genome sequencing should be a first-line test for undiagnosed
Applications of genomics and proteomics pptIbad khan
Applications of genomics and proteomics ppt
genomics and proteomics ppt
in the field of health genomics and proteomics ppt
oncology ppt
biomedical application of genomics and proteomics ppt
agriculture application of genomics and proteomics ppt
proteomics in agriculture ppt
diagnosis of infectious disease ppt
personalized medicine ppt
The study of nucleic acids began with the discovery of DNA, progressed to the study of genes and small fragments, and has now exploded to the field of genomics. Genomics is the study of entire genomes, including the complete set of genes, their nucleotide sequence and organization, and their interactions within a species and with other species. The advances in genomics have been made possible by DNA sequencing technology. [Source: https://opentextbc.ca/biology/chapter/10-3-genomics-and-proteomics/]
This document discusses the history and development of personalized medicine from early genetics concepts to current applications in cancer treatment. It covers Mendel's principles of heredity, Darwin's theory of natural selection, discoveries in molecular biology, and completion of the human genome project. It describes how genome sequencing provides a "parts list" of genes and how different cell types express different gene sets. It outlines how knowledge of a patient's genetic and somatic mutations can guide targeted cancer treatments. Finally, it discusses considerations for building personalized medicine programs, including access to samples, developing technology platforms, integrating information, conducting research, engaging stakeholders, and communicating with communities.
Genomics is the study of genomes, including sequencing genomes and determining the complete set of proteins and genes in an organism. The first genomes sequenced included Haemophilus influenzae in 1995 and the human genome was completed in 2003, taking 13 years. Genomics provides information on genes, metabolic pathways, and the functioning of organisms through approaches like genome sequencing, structural genomics, functional genomics, comparative genomics, and proteomics.
The 'omics' revolution: How will it improve our understanding of infections a...WAidid
This slideset explains the ‘Omics’ technology and its role in the study of infections and vaccination. It is a revolution as it offers powerful tools to interrogate the animal / human immune response to vaccines and infections.
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
Genomics and Proteomics provides an overview of genomics and proteomics methods and their applications in medicine. It discusses how the fields of genomics and molecular biology emerged through key advances in DNA structure, recombinant DNA technology, PCR, and automated DNA sequencing. The document also reviews epigenetics, genomic and proteomic techniques including blotting, PCR, microarrays, and mass spectrometry. Applications of these methods in clinical settings are described such as genomic tests for disease diagnosis, prognosis, and personalized medicine. Proteomics uses mass spectrometry to discover biomarkers for diseases like cancer.
This document discusses using whole genome sequencing and variant prioritization tools to diagnose rare genetic diseases in individuals. It describes how sequencing the whole genome reveals millions of variants, but focusing on the exome and protein-coding regions reduces this to tens of thousands of variants. Further prioritization by effects on protein function, rarity, predicted damage, and inheritance patterns can narrow this down to a handful of likely disease-causing genes. The document presents a case study of a child with liver disease who was diagnosed with a rare genetic condition called PFIC2 after family-wide exome sequencing and analysis with the VAAST software prioritization tool. It concludes that genome sequencing should be a first-line test for undiagnosed
Applications of genomics and proteomics pptIbad khan
Applications of genomics and proteomics ppt
genomics and proteomics ppt
in the field of health genomics and proteomics ppt
oncology ppt
biomedical application of genomics and proteomics ppt
agriculture application of genomics and proteomics ppt
proteomics in agriculture ppt
diagnosis of infectious disease ppt
personalized medicine ppt
The study of nucleic acids began with the discovery of DNA, progressed to the study of genes and small fragments, and has now exploded to the field of genomics. Genomics is the study of entire genomes, including the complete set of genes, their nucleotide sequence and organization, and their interactions within a species and with other species. The advances in genomics have been made possible by DNA sequencing technology. [Source: https://opentextbc.ca/biology/chapter/10-3-genomics-and-proteomics/]
This document discusses the history and development of personalized medicine from early genetics concepts to current applications in cancer treatment. It covers Mendel's principles of heredity, Darwin's theory of natural selection, discoveries in molecular biology, and completion of the human genome project. It describes how genome sequencing provides a "parts list" of genes and how different cell types express different gene sets. It outlines how knowledge of a patient's genetic and somatic mutations can guide targeted cancer treatments. Finally, it discusses considerations for building personalized medicine programs, including access to samples, developing technology platforms, integrating information, conducting research, engaging stakeholders, and communicating with communities.
Genomics is the study of genomes, including sequencing genomes and determining the complete set of proteins and genes in an organism. The first genomes sequenced included Haemophilus influenzae in 1995 and the human genome was completed in 2003, taking 13 years. Genomics provides information on genes, metabolic pathways, and the functioning of organisms through approaches like genome sequencing, structural genomics, functional genomics, comparative genomics, and proteomics.
The 'omics' revolution: How will it improve our understanding of infections a...WAidid
This slideset explains the ‘Omics’ technology and its role in the study of infections and vaccination. It is a revolution as it offers powerful tools to interrogate the animal / human immune response to vaccines and infections.
The document provides an overview of genomics and molecular profiling techniques. It discusses:
- The lab for bioinformatics and computational genomics which has 10 "genome hackers" and 42 scientists.
- An introduction to personalized medicine and biomarkers.
- First generation molecular profiling techniques like gene sequencing, microarrays, PCR.
- Next generation sequencing techniques like Roche 454, Illumina, SOLID which allow high throughput sequencing.
- Next generation applications like RNA sequencing, exome sequencing, epigenetic profiling.
- The role of bioinformatics in analyzing large genomic and molecular profiling data.
The document discusses transcriptomics and the relationship between transcriptome size and organism complexity. It questions how gene expression contributes to transcriptome size and what new studies reveal about size and complexity. Specifically, it notes that alternative splicing and RNA editing increase transcriptome size and complexity. It also discusses that the human genome is pervasively transcribed, with one stretch of DNA encoding many RNAs, including microRNAs, which control mRNA expression and are involved in development, gene regulation, and diseases like cancer.
This document provides an overview of genetic polymorphism and its relationship to periodontal disease. It begins with definitions of key genetic terms like allele, chromosome, DNA and discusses different types of genetic disorders. It then examines various human gene polymorphisms that have been associated with periodontal diseases, such as IL-1, IL-10, TNF-α, and FcγR gene polymorphisms. The document reviews studies that have investigated the relationship between these polymorphisms and chronic or aggressive periodontitis. It concludes by stating that identifying genetic risk factors could allow for more personalized prevention and treatment approaches for periodontal diseases in the future.
Genomics, Transcriptomics, Proteomics, Metabolomics - Basic concepts for clin...Prasenjit Mitra
This set of slides gives an overview regarding the various omics technologies available and how they can be used for improvement in clinical setting or research
The document discusses genome sequencing in vegetable crops. It provides an overview of the history and different generations of sequencing including Sanger sequencing, second generation sequencing using platforms like Roche 454 and Illumina, and third generation sequencing. It then summarizes key vegetables whose genomes have been sequenced like potato, melon, cabbage, and discusses findings from their sequencing projects including genome size, number of predicted genes, and genes of interest identified.
This document provides an overview of exome sequence analysis. It begins with definitions of key terms like genome, genetic variants, and exome sequencing. It then describes the exome sequencing workflow, which involves fragmentation, hybridization to capture exonic regions, sequencing, mapping reads to reference genome, variant calling, and variant annotation. Challenges of finding causal variants are discussed. The document also compares benefits and challenges of exome sequencing versus whole genome sequencing or traditional methods. Finally, it discusses how exome sequencing has helped identify novel disease genes and expand knowledge of known disease genes.
This document discusses plant system biology, which analyzes plant systems as a whole by studying the interactions between their biological components. It describes various omics approaches like genomics, epigenomics, transcriptomics, proteomics, and metabolomics that are used to study different levels of biological organization. Integration of multi-omics data using bioinformatics tools and modeling approaches provides insights into how plants respond to stimuli by understanding the individual components and their interactions as a complex network.
Genome sequencing involves obtaining blocks of DNA sequences and assembling them into contiguous stretches of sequence and ultimately the whole genome. This provides a starting point for research into thousands of diseases with a genetic basis. Automated DNA sequencing still uses Sanger's chain termination method but is now more accurate and faster. Emerging methods include sequencing by hybridization, mass spectrophotometry, and single molecule techniques. Future applications include individual genome sequencing using nanotechnology to study gene expression and interactions on a large scale.
This document provides an overview of the field of bioinformatics. It discusses that bioinformatics is the analysis of biological information using computers and statistical techniques, and involves organizing, storing, analyzing and visualizing genomic data. It also discusses various databases used in bioinformatics, including nucleotide sequence databases like GenBank, protein sequence databases like Swiss-Prot, structure databases like PDB, and species-oriented databases. Examples of analyzing genomic sequences, predicting protein structures, and correlating gene expression and disease are also provided.
Whole genome sequencing of arabidopsis thalianaBhavya Sree
This document summarizes the genome sequencing of Arabidopsis thaliana. It discusses that genome sequencing approaches began being discussed in 1984 and the Human Genome Project officially began in 1990. The Arabidopsis genome project was initiated in 1990 and was completed in 2000, sequencing approximately 115.4 Mb and predicting 25,498 genes. The outcomes of the sequencing project included characterization of coding regions, comparative analysis between accessions and other plant genera, and integration of the three plant genomes.
Genetic toxicology involves assessing the effects of physical and chemical agents on DNA and genetic processes in living cells. It examines the health impacts of genetic alterations in somatic and germ cells, mechanisms that induce alterations like DNA damage and repair, and formation of gene mutations. A variety of assays are used to detect genetic alterations, with goals of identifying mutagenic chemicals and repair mechanisms. These assays examine DNA damage, mutations in nonmammalian and mammalian models, and chromosomal aberrations. Germ cell mutagenesis is also evaluated through assays measuring gene mutations and chromosomal alterations.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome or cell. It involves the large-scale study of proteins, including their structures and functions. The document discusses key aspects of proteomics, including what constitutes a proteome, why studying the proteome is important, and how proteomics compares and contrasts with genomics. It also describes various techniques used in proteomics, such as sample preparation, two-dimensional gel electrophoresis, detection technologies like mass spectrometry, and bioinformatics tools for protein identification and analysis of expression profiles.
Applications of Genomic and Proteomic ToolsRaju Paudel
This document provides an overview of genomic and proteomic tools. It discusses topics like genomics, which is the study of genomes including structural and functional genomics. Proteomics is defined as the large-scale study of proteins, their structures and functions. Several techniques are described briefly, including DNA gel electrophoresis, polymerase chain reaction (PCR), real-time PCR, DNA sequencing, microarray technology, enzyme-linked immunosorbent assay (ELISA), and blotting techniques like Southern blotting, Northern blotting and Western blotting. Applications of these various tools are also mentioned.
This document provides an overview of genetic toxicity testing guidelines. It discusses the history and aims of toxicity studies. Various in vitro and in vivo genetic toxicology tests are described, including tests for gene mutation, chromosomal abnormalities, and primary DNA damage. Key tests covered include the mammalian erythrocyte micronucleus test, mammalian bone marrow chromosomal aberration test, rodent dominant lethal assay, and mouse heritable translocation assay. The principles, procedures, and parameters of these tests are summarized. References on genetic toxicology guidance documents and studies are also provided.
This document provides an overview of genomics and related fields. It discusses the historical discoveries that laid the foundations of genomics. It then defines key genomics terms and describes different areas of genomics research like comparative genomics, metagenomics, structural genomics, functional genomics, transcriptomics, proteomics and metabolomics. The document also discusses genome sequencing techniques, genome organization of different organisms like bacteria, plants and humans. It concludes with an overview of genome mapping methods.
This document provides an introduction to genomics and proteomics. It outlines key topics including the tree of life, genes, and genomics definitions. The tree of life section distinguishes between prokaryotic and eukaryotic genomes, noting that prokaryotes like bacteria contain single circular DNA molecules while eukaryotes have more complex genomes. The document also compares genome sizes across various species and describes genes and exons and introns in eukaryotes. It discusses identifying genes in genomes through similarity to known genes or ab initio methods examining DNA sequence properties.
The document summarizes the sequencing of the yeast Saccharomyces cerevisiae genome. Key points:
1) The yeast genome was sequenced between 1989-1996 by over 35 European laboratories in a collaborative effort. By 1996, the entire 12 megabase genome across 16 chromosomes had been sequenced.
2) The genome contains approximately 6,000 open reading frames that were annotated after sequencing. About 30% of yeast genes have homologs in human genes.
3) Sequencing involved creating ordered cosmid libraries, shotgun sequencing, and assembling overlapping sequences into contigs. Genes were identified and analyzed after full genome assembly.
Genomics refers to the study of the entire genome of an organism. It deals with mapping genes on chromosomes and sequencing entire genomes. While work on genomics began with prokaryotes like bacteria, research has now been conducted on crop plants like rice and Arabidopsis thaliana. Genomics is an interdisciplinary field that uses tools from molecular biology, robotics, and computing to study genomes. It provides information on genome size, gene number, gene function, and evolution. Genomics has applications in crop improvement through gene mapping, marker-assisted selection, and transgenic breeding. However, genomic research also faces limitations due to high costs, technical challenges, and complexity of traits.
This document summarizes a seminar on genomics presented by Komal Rajgire. It defines genomics as the study of all genes in an organism, including their mapping, sequencing, and functional analysis. The key differences between genetics and genomics are outlined. The document discusses approaches in functional genomics like homology searching and expression analysis. It also covers related fields like structural genomics, epigenomics, metagenomics, pharmacogenomics, and the applications and future impact of genomics on medicine, drug discovery, and personalized treatment.
Comparative genomics involves comparing genomes to discover similarities and differences. It can provide insights into evolutionary relationships, help predict gene function, and aid in drug discovery. The first step is often aligning genome sequences using tools like BLAST or MUMmer. Genomes can then be compared at various levels, such as overall nucleotide statistics, genome structure, and coding/non-coding regions. Comparing gene and protein content across genomes helps predict functions. Conserved genomic features across species also aid prediction. Insights into genome evolution come from studying molecular events like inversions and duplications. Comparative genomics has impacted phylogenetics and drug target identification.
The document discusses a lab for bioinformatics and computational genomics at Ghent University. The lab has 10 "genome hackers" who are mostly engineers and 42 scientists, technicians, geneticists and clinicians. The lab focuses on bioinformatics, epigenetics, personal genomics and 3D printing. Bioinformatics is defined as the application of information technology to biological information, facilitated by computers. The document then discusses various topics related to genetics, genomics and personalized medicine.
This document provides information on genomics, proteomics, and metabolomics. It discusses that genomics is the study of genomes through sequencing and analysis. It involves various types of genomics like structural, functional, and comparative genomics. Proteomics is the large-scale study of the structure and function of proteins in organisms. Key proteomics methods include antibody detection and mass spectrometry. Metabolomics is the study of small molecule metabolites within cells and biofluids, which make up the metabolome. These "omics" fields provide insights into cellular processes and are applied in areas like disease diagnosis and drug development.
The document provides an overview of genomics and molecular profiling techniques. It discusses:
- The lab for bioinformatics and computational genomics which has 10 "genome hackers" and 42 scientists.
- An introduction to personalized medicine and biomarkers.
- First generation molecular profiling techniques like gene sequencing, microarrays, PCR.
- Next generation sequencing techniques like Roche 454, Illumina, SOLID which allow high throughput sequencing.
- Next generation applications like RNA sequencing, exome sequencing, epigenetic profiling.
- The role of bioinformatics in analyzing large genomic and molecular profiling data.
The document discusses transcriptomics and the relationship between transcriptome size and organism complexity. It questions how gene expression contributes to transcriptome size and what new studies reveal about size and complexity. Specifically, it notes that alternative splicing and RNA editing increase transcriptome size and complexity. It also discusses that the human genome is pervasively transcribed, with one stretch of DNA encoding many RNAs, including microRNAs, which control mRNA expression and are involved in development, gene regulation, and diseases like cancer.
This document provides an overview of genetic polymorphism and its relationship to periodontal disease. It begins with definitions of key genetic terms like allele, chromosome, DNA and discusses different types of genetic disorders. It then examines various human gene polymorphisms that have been associated with periodontal diseases, such as IL-1, IL-10, TNF-α, and FcγR gene polymorphisms. The document reviews studies that have investigated the relationship between these polymorphisms and chronic or aggressive periodontitis. It concludes by stating that identifying genetic risk factors could allow for more personalized prevention and treatment approaches for periodontal diseases in the future.
Genomics, Transcriptomics, Proteomics, Metabolomics - Basic concepts for clin...Prasenjit Mitra
This set of slides gives an overview regarding the various omics technologies available and how they can be used for improvement in clinical setting or research
The document discusses genome sequencing in vegetable crops. It provides an overview of the history and different generations of sequencing including Sanger sequencing, second generation sequencing using platforms like Roche 454 and Illumina, and third generation sequencing. It then summarizes key vegetables whose genomes have been sequenced like potato, melon, cabbage, and discusses findings from their sequencing projects including genome size, number of predicted genes, and genes of interest identified.
This document provides an overview of exome sequence analysis. It begins with definitions of key terms like genome, genetic variants, and exome sequencing. It then describes the exome sequencing workflow, which involves fragmentation, hybridization to capture exonic regions, sequencing, mapping reads to reference genome, variant calling, and variant annotation. Challenges of finding causal variants are discussed. The document also compares benefits and challenges of exome sequencing versus whole genome sequencing or traditional methods. Finally, it discusses how exome sequencing has helped identify novel disease genes and expand knowledge of known disease genes.
This document discusses plant system biology, which analyzes plant systems as a whole by studying the interactions between their biological components. It describes various omics approaches like genomics, epigenomics, transcriptomics, proteomics, and metabolomics that are used to study different levels of biological organization. Integration of multi-omics data using bioinformatics tools and modeling approaches provides insights into how plants respond to stimuli by understanding the individual components and their interactions as a complex network.
Genome sequencing involves obtaining blocks of DNA sequences and assembling them into contiguous stretches of sequence and ultimately the whole genome. This provides a starting point for research into thousands of diseases with a genetic basis. Automated DNA sequencing still uses Sanger's chain termination method but is now more accurate and faster. Emerging methods include sequencing by hybridization, mass spectrophotometry, and single molecule techniques. Future applications include individual genome sequencing using nanotechnology to study gene expression and interactions on a large scale.
This document provides an overview of the field of bioinformatics. It discusses that bioinformatics is the analysis of biological information using computers and statistical techniques, and involves organizing, storing, analyzing and visualizing genomic data. It also discusses various databases used in bioinformatics, including nucleotide sequence databases like GenBank, protein sequence databases like Swiss-Prot, structure databases like PDB, and species-oriented databases. Examples of analyzing genomic sequences, predicting protein structures, and correlating gene expression and disease are also provided.
Whole genome sequencing of arabidopsis thalianaBhavya Sree
This document summarizes the genome sequencing of Arabidopsis thaliana. It discusses that genome sequencing approaches began being discussed in 1984 and the Human Genome Project officially began in 1990. The Arabidopsis genome project was initiated in 1990 and was completed in 2000, sequencing approximately 115.4 Mb and predicting 25,498 genes. The outcomes of the sequencing project included characterization of coding regions, comparative analysis between accessions and other plant genera, and integration of the three plant genomes.
Genetic toxicology involves assessing the effects of physical and chemical agents on DNA and genetic processes in living cells. It examines the health impacts of genetic alterations in somatic and germ cells, mechanisms that induce alterations like DNA damage and repair, and formation of gene mutations. A variety of assays are used to detect genetic alterations, with goals of identifying mutagenic chemicals and repair mechanisms. These assays examine DNA damage, mutations in nonmammalian and mammalian models, and chromosomal aberrations. Germ cell mutagenesis is also evaluated through assays measuring gene mutations and chromosomal alterations.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome or cell. It involves the large-scale study of proteins, including their structures and functions. The document discusses key aspects of proteomics, including what constitutes a proteome, why studying the proteome is important, and how proteomics compares and contrasts with genomics. It also describes various techniques used in proteomics, such as sample preparation, two-dimensional gel electrophoresis, detection technologies like mass spectrometry, and bioinformatics tools for protein identification and analysis of expression profiles.
Applications of Genomic and Proteomic ToolsRaju Paudel
This document provides an overview of genomic and proteomic tools. It discusses topics like genomics, which is the study of genomes including structural and functional genomics. Proteomics is defined as the large-scale study of proteins, their structures and functions. Several techniques are described briefly, including DNA gel electrophoresis, polymerase chain reaction (PCR), real-time PCR, DNA sequencing, microarray technology, enzyme-linked immunosorbent assay (ELISA), and blotting techniques like Southern blotting, Northern blotting and Western blotting. Applications of these various tools are also mentioned.
This document provides an overview of genetic toxicity testing guidelines. It discusses the history and aims of toxicity studies. Various in vitro and in vivo genetic toxicology tests are described, including tests for gene mutation, chromosomal abnormalities, and primary DNA damage. Key tests covered include the mammalian erythrocyte micronucleus test, mammalian bone marrow chromosomal aberration test, rodent dominant lethal assay, and mouse heritable translocation assay. The principles, procedures, and parameters of these tests are summarized. References on genetic toxicology guidance documents and studies are also provided.
This document provides an overview of genomics and related fields. It discusses the historical discoveries that laid the foundations of genomics. It then defines key genomics terms and describes different areas of genomics research like comparative genomics, metagenomics, structural genomics, functional genomics, transcriptomics, proteomics and metabolomics. The document also discusses genome sequencing techniques, genome organization of different organisms like bacteria, plants and humans. It concludes with an overview of genome mapping methods.
This document provides an introduction to genomics and proteomics. It outlines key topics including the tree of life, genes, and genomics definitions. The tree of life section distinguishes between prokaryotic and eukaryotic genomes, noting that prokaryotes like bacteria contain single circular DNA molecules while eukaryotes have more complex genomes. The document also compares genome sizes across various species and describes genes and exons and introns in eukaryotes. It discusses identifying genes in genomes through similarity to known genes or ab initio methods examining DNA sequence properties.
The document summarizes the sequencing of the yeast Saccharomyces cerevisiae genome. Key points:
1) The yeast genome was sequenced between 1989-1996 by over 35 European laboratories in a collaborative effort. By 1996, the entire 12 megabase genome across 16 chromosomes had been sequenced.
2) The genome contains approximately 6,000 open reading frames that were annotated after sequencing. About 30% of yeast genes have homologs in human genes.
3) Sequencing involved creating ordered cosmid libraries, shotgun sequencing, and assembling overlapping sequences into contigs. Genes were identified and analyzed after full genome assembly.
Genomics refers to the study of the entire genome of an organism. It deals with mapping genes on chromosomes and sequencing entire genomes. While work on genomics began with prokaryotes like bacteria, research has now been conducted on crop plants like rice and Arabidopsis thaliana. Genomics is an interdisciplinary field that uses tools from molecular biology, robotics, and computing to study genomes. It provides information on genome size, gene number, gene function, and evolution. Genomics has applications in crop improvement through gene mapping, marker-assisted selection, and transgenic breeding. However, genomic research also faces limitations due to high costs, technical challenges, and complexity of traits.
This document summarizes a seminar on genomics presented by Komal Rajgire. It defines genomics as the study of all genes in an organism, including their mapping, sequencing, and functional analysis. The key differences between genetics and genomics are outlined. The document discusses approaches in functional genomics like homology searching and expression analysis. It also covers related fields like structural genomics, epigenomics, metagenomics, pharmacogenomics, and the applications and future impact of genomics on medicine, drug discovery, and personalized treatment.
Comparative genomics involves comparing genomes to discover similarities and differences. It can provide insights into evolutionary relationships, help predict gene function, and aid in drug discovery. The first step is often aligning genome sequences using tools like BLAST or MUMmer. Genomes can then be compared at various levels, such as overall nucleotide statistics, genome structure, and coding/non-coding regions. Comparing gene and protein content across genomes helps predict functions. Conserved genomic features across species also aid prediction. Insights into genome evolution come from studying molecular events like inversions and duplications. Comparative genomics has impacted phylogenetics and drug target identification.
The document discusses a lab for bioinformatics and computational genomics at Ghent University. The lab has 10 "genome hackers" who are mostly engineers and 42 scientists, technicians, geneticists and clinicians. The lab focuses on bioinformatics, epigenetics, personal genomics and 3D printing. Bioinformatics is defined as the application of information technology to biological information, facilitated by computers. The document then discusses various topics related to genetics, genomics and personalized medicine.
This document provides information on genomics, proteomics, and metabolomics. It discusses that genomics is the study of genomes through sequencing and analysis. It involves various types of genomics like structural, functional, and comparative genomics. Proteomics is the large-scale study of the structure and function of proteins in organisms. Key proteomics methods include antibody detection and mass spectrometry. Metabolomics is the study of small molecule metabolites within cells and biofluids, which make up the metabolome. These "omics" fields provide insights into cellular processes and are applied in areas like disease diagnosis and drug development.
The document provides an overview of the field of biotechnology, including its history, key areas and applications. It discusses topics like genetic engineering, recombinant DNA technology, transgenic plants and animals, DNA microarrays, bioinformatics, and careers in biotechnology. The future prospects of biotechnology in addressing global challenges like food security and healthcare are also highlighted.
Molecular techniques for pathology research - MDX .pdfsabyabby
This document discusses molecular techniques used in pathology research such as PCR, microarrays, next generation sequencing, immunohistochemistry, ELISA, and Western blotting. It provides details on each technique including the basic principles, applications in research, and examples of uses in studies of gene expression, cancer, bone disease, and growth retardation. The learning outcomes are to understand these techniques and their uses in basic and clinical research.
The document describes a lab for bioinformatics and computational genomics at Ghent University. It has over 100 people including engineers, mathematicians, and molecular biologists. The lab uses bioinformatics approaches like sequence analysis, datamining, and computational biology to analyze large genomic datasets. One goal is developing an app for personal genomic analysis and interpretation.
This document provides an introduction and overview of a refresher course in molecular biology and bioinformatics. The course aims to provide a solid introduction to molecular biology techniques and bioinformatics tools currently used to investigate molecular mechanisms and their application to disease diagnosis and treatment. It will introduce concepts like genomics, transcriptomics, proteomics, epigenetics, genome editing with CRISPR, molecular cloning, and genome-wide association studies. The course also discusses various molecular biology techniques like DNA sequencing, PCR, immunohistochemistry and their role in furthering our understanding of human disease at the molecular level to improve patient care through more accurate diagnosis and targeted therapies.
The document discusses various topics related to molecular profiling and personalized medicine. It describes first generation molecular profiling techniques like gene sequencing, microarrays, and PCR. It then covers next generation sequencing technologies like Roche 454, Illumina, and ABI SOLID. It also discusses second generation techniques for DNA and RNA profiling including exome sequencing, ChIP-seq, and RNA-seq. Finally, it briefly mentions third generation sequencing and epigenetic profiling.
Experimental methods and the big data sets improvemed
1. Experiments in systems biology use quantitative data from multiple omics techniques like microarrays, sequencing, proteomics, lipidomics, and metabolomics to study biological systems.
2. Computational models are used to simulate dynamic changes in molecules over time based on precise quantification from experiments.
3. Both hypothesis-generating and hypothesis-driven studies are important in systems biology, with the latter focusing on targeted subsets of molecules or organelles.
A comparative study using different measure of filterationpurkaitjayati29
This document presents a study comparing different scoring functions used in filter-based feature selection methods for microarray gene expression data. Chapter 1 introduces gene expression, DNA microarrays, and the goals of classification and feature selection. Chapter 2 provides background on bioinformatics, molecular biology, and the central dogma. Chapter 3 describes DNA microarray technology and gene expression data. Chapter 4 reviews literature on feature selection techniques applied to microarray data, discussing filter, wrapper, embedded, hybrid, and ensemble methods. Chapter 5 proposes using a scoring function-based filter method to select relevant genes, focusing on mutual information, symmetric uncertainty, information gain, and Chi-square scoring functions.
Developing tools & Methodologies for the NExt Generation of Genomics & Bio In...Intel IT Center
This document discusses computational challenges in analyzing next generation DNA sequencing data and implications for diagnostics and therapeutics. It notes that sequencing one genome now takes just a few days but analysis is the bottleneck. The author's organization, Genomeon, has developed a high performance computing system called SHADOWFAX to analyze over 7,700 genomes. Genomeon is focusing on analyzing repetitive DNA sequences called microsatellites that were previously understudied. They have identified patterns of informative microsatellites that differentiate cancer types like breast cancer from healthy genomes with high sensitivity and specificity. These findings could enable new cancer risk assessment, companion diagnostics, clinical trial stratification, drug targets, and non-cancer applications.
Genomics and proteomics in drug discovery and developmentSuchittaU
This document discusses the role of genomics and proteomics in drug discovery and development. It explains that genomics and proteomics technologies can help identify new drug targets by comparing gene and protein expression between healthy and diseased cells. Proteomics in particular analyzes changes in protein levels and can quantify individual proteins using techniques like 2D gel electrophoresis and mass spectrometry. The integration of genomics and proteomics provides a more comprehensive understanding of biological systems and is improving the drug discovery process.
What is bioinformatics?
About human genome
Human genome project
Aim of human genome project
History
Sequencing Strategy
Benefits of Human Genome Project research
Disadvantages of human genome project
Conclusion
References
This document provides an overview of bioinformatics. It defines bioinformatics as the science of collecting, analyzing and conceptualizing biological data through computational techniques. It discusses that bioinformatics involves managing, organizing and processing biological information from databases, as well as analyzing, visualizing and sharing biological data over the internet. It also outlines some of the goals of bioinformatics like organizing the human and mouse genomes, as well as some applications like genomic and protein sequence analysis, protein structure prediction, and characterizing genomes.
Clinical Assessment In Incorporating a Personal GenomeDiego Herrera
This presentation goes in-depth in the growing field of personal genome sequencing. The advances in high-throughput DNA sequencing has made the process of mapping structural deviations in an individual's genetic totality more economical. The advantages in health care makes this technology more like to be fully integrated in medicine within the next ten years.
The document discusses the Human Genome Project, which had goals of identifying all 30,000 human genes, determining the sequence of the 3 billion base pairs that make up human DNA, storing this information in databases, and improving data analysis tools. By sequencing factories generating 1000 nucleotides per second, the project was completed ahead of schedule. The project revealed that humans have fewer genes than expected, 99.9% of bases are identical between humans, and 50% or more of the genome consists of "junk DNA" with unknown functions.
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble and analyze the function and structure of genomes
The document summarizes a presentation on bioinformatics case studies focusing on epigenetics and personal genomics. It discusses DNA methylation and its role in cancer development. It also describes how next-generation sequencing can be used to identify epigenetic biomarkers for clinical use. Finally, it addresses issues around personal and recreational genomics, including increasing access, educating users, and protecting individual privacy and rights.
This document provides an overview of bioinformatics and highlights several key points:
- Bioinformatics has emerged as a field to help analyze the vast amounts of biological data being generated through high-throughput technologies. It integrates biology, computer science, and information technology.
- The size of the human genome and rate of data generation has grown exponentially, necessitating computational approaches. International efforts like the Human Genome Project helped sequence the entire human genome.
- Bioinformatics tools and databases are used to study genomics, transcriptomics, proteomics and more to better understand living systems at the molecular level and enable applications in medicine, agriculture, forensics and more. This work also raises ethical, legal and social considerations.
Conferencia de la Dra. Ana María Roa, Bióloga Molecular, sobre Epigenética, impartida en la Universidad Popular Carmen de Michelena de Tres Cantos el 1 de marzo de 2013.
Más información en:
http://www.universidadpopularc3c.es/index.php/actividades/conferencias/event/448-conferencia-una-revision-de-los-conocimientos-fundamentales-de-la-biologia-de-la-celula-la-epigenetica
Introduction
Overview
Reductionist approach
Holistic approach
What is systems biology?
○ Advantages of Systems Biology
Tools of holistic approach
○ Proteomics, Transcriptomics and Metabolomics
Conclusion
References
Similar to 2013 10 23_dna_for_dummies_v_presented (20)
This document provides an overview of bioinformatics and biological databases. It discusses how bioinformatics draws from fields like biology, computer science, statistics, and machine learning. Biological databases are important resources for bioinformatics that can be searched and analyzed to answer questions, find similar sequences, locate patterns, and make predictions. The document also outlines common uses of biological databases, such as annotation searches, homology searches, pattern searches, and predictive analyses.
The document discusses the Rh blood group system and its clinical significance. It describes the key observations in 1939 that linked adverse reactions in mothers to stillborn fetuses and blood transfusions from fathers, indicating a relationship. This syndrome is now called hemolytic disease of the fetus and newborn. The Rh system was identified in 1940 through experiments immunizing animals with Rhesus macaque monkey red blood cells. The D antigen is the most important RBC antigen in transfusion practice, as those lacking it do not produce anti-D antibody unless exposed to D antigen through transfusion or pregnancy. Testing for D is routinely performed to ensure D-negative patients receive D-negative blood.
The document discusses views and materialized views in data warehousing and decision support systems. It covers three main points:
1) OLAP queries typically involve aggregate queries, so precomputation is essential for fast response times. Materialized views allow precomputing aggregates across multiple dimensions.
2) Warehouses can be thought of as collections of asynchronously replicated tables and periodically maintained views, renewing interest in efficient view maintenance.
3) Materialized views store the results of views in the database for fast access like a cache, but they require maintenance as underlying tables change. Incremental maintenance algorithms are ideal to efficiently update materialized views.
The document discusses various database concepts including normalization, which is used to design optimal relation schemas by removing redundant data. It also covers transaction processing, which involves executing logical database operations as transactions to maintain data integrity. Database systems use techniques like logging and concurrency control to prevent transaction anomalies and ensure failures can be recovered from.
This document contains a list of names, emails, and study programs of students. It includes their official student code, last name, first name, email, and educational program. There are 20 students listed with their details.
This document discusses the Biological Databases project being conducted by a group of students. The project involves using the video game Minecraft to visualize protein structures retrieved from the Protein Data Bank (PDB). Python scripts are used to import PDB data files and place blocks in Minecraft to represent atoms, with different block colors used to distinguish atom types. SPARQL queries are also employed to search the RDF version of the PDB for protein entries. The goal is to build 3D protein models inside Minecraft for educational and visualization purposes.
The document discusses various bioinformatics tools and algorithms for analyzing protein sequences, including Biopython for working with biological sequence data, the Kyte-Doolittle algorithm for predicting transmembrane regions, and the Chou-Fasman algorithm for predicting secondary structure from amino acid preferences for alpha helices, beta sheets, and random coils. It also provides examples of analyzing Swiss-Prot data to find properties of human proteins and applying these tools and libraries to extract insights from protein sequences.
The document discusses various topics related to analyzing protein sequences using Python and Biopython. It provides examples of using Biopython to parse sequence data from UniProt, calculate lengths and translations of sequences. It also discusses analyzing properties of sequences like molecular weight, isoelectric point, transmembrane regions, and comparing sequences to find conserved motifs. Finally, it introduces hydropathy indices and tools for predicting properties like transmembrane helices from primary sequences.
This document discusses Python functions. It explains that there are built-in functions provided as part of Python and user-defined functions. User-defined functions are created using the def keyword and can take parameters and return values. The body of a function is indented and runs when the function is called. Functions allow code to be reused and organized in a modular way. Examples are provided to demonstrate defining and calling functions with different parameters and return values.
The document provides a recap of Python programming concepts like conditions and statements, while loops, for loops, break and continue statements, and working with strings. It also introduces regular expressions as a way to match patterns in strings using a formal language that can be interpreted by a regular expression processor.
[SUMMARY
This document discusses next generation DNA sequencing technologies. It begins by describing some of the limitations of traditional Sanger sequencing, such as read lengths of 500-1000 bases and throughput of 57,000 bases per run. It then introduces some key next generation sequencing technologies, such as 454 sequencing which uses emulsion PCR and pyrosequencing to achieve read lengths of 20-100 bases but higher throughput of 20-100 Mb per run. Illumina/Solexa sequencing is also discussed, which uses sequencing by synthesis with reversible terminators and laser-based detection. Finally, third generation sequencing technologies are mentioned, such as Pacific Biosciences' single molecule real time sequencing and nanopore sequencing. In summary, the document provides a high-level
The document provides an overview of the history and evolution of various programming languages. It discusses early languages like FORTRAN, LISP, PASCAL, C, and Java. It also covers scripting languages and their uses. The document explains what Python is as a programming language - that it is interpreted, object-oriented, and high-level. It was named after Monty Python and was created by Guido van Rossum. The document then gives examples of using Python to program Minecraft by importing protein data from PDB files and using coordinates to place blocks to visualize proteins in the game.
This document provides an introduction to bio-ontologies and the semantic web. It discusses what ontologies are and how they are used in the bio domain through initiatives like the OBO Foundry. It introduces key semantic web technologies like RDF, URIs, Turtle syntax, and SPARQL query language. It provides examples of ontologies like the Gene Ontology and how ontologies can be represented and queried using these semantic web standards.
This document provides an overview of NoSQL databases, including:
- Key-value stores store data as maps or hashmaps and are efficient for data access but limited in query capabilities.
- Column-oriented stores group attributes into column families and store data efficiently but are operationally challenging.
- Document databases store loosely structured data like JSON and allow retrieving documents by keys or contents.
- Graph databases are suited for interaction networks and path finding but are less suited for tabular data.
The document discusses creating a multicore database project. It recommends taking the following steps:
1. Define what the project is about, what it aims to achieve, and who it is for.
2. Identify information resources and develop a basic data model.
3. Design a user interface mockup without technical constraints, thinking creatively.
This document discusses biological databases and PHP. It begins with an overview of biological databases and examples using BIOSQL to load genetic data from GenBank into a MySQL database. It then provides examples of building a basic 3-tier model with Apache, PHP, and a MySQL backend database. The document also includes a brief introduction to PHP, covering its history, why it is commonly used, and basic syntax like conditional statements.
This document discusses biological databases and SQL. It provides an overview of primary and derived data in biological research, as well as different data levels. It then discusses direct querying of selected bioinformatics databases using SQL and provides examples of 3-tier database models. The document proceeds to discuss rationale for learning SQL to query biological databases and provides definitions and explanations of key SQL concepts like tables, records, queries, data types, keys, integrity rules and constraints.
This document discusses biological databases and bioinformatics. It begins with an overview of bioinformatics as an interdisciplinary field combining biology, computer science, and information technology. It then discusses different types of biological databases, including those focused on sequences, pathways, protein structures, and gene expression. The document outlines some common uses of biological databases, including searching for annotations, identifying similar sequences through homology, searching for patterns, and making predictions. It also briefly discusses comparing data across databases. The summary provides a high-level overview of the key topics and uses of biological databases covered in the document.
The document discusses several topics related to protein structure prediction using Python:
1. It introduces the Chou-Fasman algorithm for predicting protein secondary structure from amino acid sequence. The algorithm calculates preference parameters for each amino acid to be in alpha helices, beta sheets, or other structures.
2. It provides an example of calculating helical propensity.
3. It lists the preference parameters output by the Chou-Fasman algorithm for each amino acid.
4. It outlines the steps of applying the Chou-Fasman algorithm to predict secondary structure elements in a protein sequence.
The document provides information on various Python programming concepts including control structures, lists, dictionaries, regular expressions, exceptions, and biological applications using Biopython. It discusses if/else statements, while and for loops, list operations, dictionary usage, regex patterns, exception handling roles, and gives examples analyzing protein sequences and structures using Biopython.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
2. Lab for Bioinformatics and computational genomics
Overview
^[now][transl⎮comput]ational[epi]genomic$
•
•
•
•
Who ? Where ?
Bioinformatics
(Epi)genetics
Technology: Next Gen
Sequencing
• Personal Genomics
5. Lab for Bioinformatics and computational genomics
Cell Theory
• All organisms are
composed of one or
more cells.
• Cells are the smallest
living units of all living
organisms.
• Cells arise only by
division of a previously
existing cell.
6. Each human cell contains 46 chromosomes (except sperm or egg cells)
9. Lab for Bioinformatics and computational genomics
DNA: Structure and Function
The human genome comprises the information contained in one
set of human chromosomes which themselves contain about 3
billion base pairs (bp) of DNA in 46 chromosomes (22
autosome pairs + 2 sex chromosomes). The total length of DNA
present in one adult human is calculated by the multiplication of
(length of 1 bp)(number of bp per cell)(number of cells in the body)
10. Lab for Bioinformatics and computational genomics
DNA: Structure and Function
The human genome comprises the information contained in one
set of human chromosomes which themselves contain about 3
billion base pairs (bp) of DNA in 46 chromosomes (22
autosome pairs + 2 sex chromosomes). The total length of DNA
present in one adult human is calculated by the multiplication of
(length of 1 bp)(number of bp per cell)(number of cells in the body)
(0.34 × 10-9 m)(6 × 109)(1013)
2.0 × 1013 meters
11. Lab for Bioinformatics and computational genomics
DNA: Structure and Function
The human genome comprises the information contained in one
set of human chromosomes which themselves contain about 3
billion base pairs (bp) of DNA in 46 chromosomes (22
autosome pairs + 2 sex chromosomes). The total length of DNA
present in one adult human is calculated by the multiplication of
(length of 1 bp)(number of bp per cell)(number of cells in the body)
(0.34 × 10-9 m)(6 × 109)(1013)
2.0 × 1013 meters
That is the equivalent of nearly 70 trips from the earth to
the sun and back.
18. Lab for Bioinformatics and computational genomics
Overview
^[now][transl⎮comput]ational[epi]genomic$
•
•
•
•
Who ? Where ?
Bioinformatics
(Epi)genetics
Technology: Next Gen
Sequencing
• Personal Genomics
19. Microbes are all over us
There are millions of microbes per
square inch on your body
Thousands of different species on the skin alone
Some thrive on dry patches of the elbow, others
thrive in moist environment of armpit
It is estimated that there are more microbes in
your intestine than there are human cells in your
body!
http://commons.wikimedia.org/wiki/File:Man_sha
dow_-_upper.png
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
20. Lab for Bioinformatics and computational genomics
Defining Epigenetics
Genome
DNA
• Reversible changes in gene
expression/function
• Without changes in DNA
sequence
Chromatin
Epigenome
Gene Expression
Phenotype
• Can be inherited from
precursor cells
• Epigenetic information is
included in the epigenome
• Allows to integrate intrinsic
with environmental signals
(including diet)
21. Lab for Bioinformatics and computational genomics
Chromatin is a Key Component of Epigenetic Mechanisms
22. Lab for Bioinformatics and computational genomics
Chromatin is a Key Component of Epigenetic Mechanisms
Cellular DNA is packaged into a structure called
chromatin
The unit of chromatin is the nucleosome, a complex
of a histone tetramer with approx. 125 bp of DNA
wound around it
nucleosome
histone
DNA
chromatin
•
Chromatin organizes genes to be accessible for transcription, replication, and
repair
23. Lab for Bioinformatics and computational genomics
Basic Epigenetic Mechanisms:
Post Translational Modifications to Histones and Base Changes in DNA
•
Epigenetic modifications of histones and DNA include:
– Histone acetylation and methylation, and DNA methylation
Histone
Acetylation
Ac
Histone
Methylation
Me Me
Me
DNA Methylation
24. Lab for Bioinformatics and computational genomics
Epigenetic Changes can Alter Chromatin Structure and Regulate Gene Expression
TF
TF
Ac
Ac
Ac
Ac
Ac
Ac
Ac
Ac
Ac
Gene
expression
•
•
Gene
expression
Gene expression (transcription) requires DNA to be physically accessible to
transcription factors (TF)
Epigenetic changes alter the structure of the chromatin, which determines
whether DNA is accessible
– Open chromatin allows gene expression
– Closed chromatin prevents gene expression
30. Lab for Bioinformatics and computational genomics
Epigenetics
• Epigenetics is essentially the
study of how our environment
impacts traits acquired within
our lifetimes, altering certain
gene expressions which may
then be passed on to future
generations
• That is, what we do to our own
bodies may affect our children
& grandchildren more than we
thought.
30
31. Lab for Bioinformatics and computational genomics
Epigenetic (meta)information = stem cells
32. Lab for Bioinformatics and computational genomics
Translational Research towards Personalised Medicine
•
•
DNA diagnostic tests can be
used to identify in advance
which patients are likely to
respond well to a therapy
The benefits of this approach
are to:
– avoid adverse drug
reactions
– improve efficacy
– adjust the dose to suit the
patient
– differentiate a product in a
competitive market
– meet future legal or
regulatory requirements
33. Lab for Bioinformatics and computational genomics
Historically, Cancer Was Considered
to be Driven Mostly by Genetic Changes
GENETIC
•
•
•
•
Example:
Replication errors
X X
Mutations in p53
Activating mutations in RAS
Mutations or amplifications of the HER-2 gene
Chromosomal translocations in myeloid cells and the
generation of the BCR-ABL fusion protein
Altered
DNA sequence
Altered
DNA/mRNA/proteins
Oncogenesis
Tumor
34. Lab for Bioinformatics and computational genomics
Recent Evidence Shows that Epigenetic Changes are Also Important in Causing Cancer
GENETIC
EPIGENETIC
Example:
Chromatin modification errors
Example:
Replication errors
X X
Altered
chromatin structure
Altered
DNA sequence
Altered
DNA/mRNA/proteins
Oncogenesis
Tumor
Altered levels of
mRNA/proteins
35. Lab for Bioinformatics and computational genomics
Biology uses methylation extensively
as a “regulatory checkpoint” in (cancer) development
Schuebel et al 2007
36. Methylation of MGMT in GBM
Kaplan-Meier Estimates of Overall Survival in GBM,
According to MGMT Promoter Methylation Status
Hegi et al. NEJM 2005, 352(10):997-1003
3
37. Lab for Bioinformatics and computational genomics
Cancer Stem Cell Theory: the ‘Root’ of Cancer Growth
Tumor
Tumor
Development
and
Growth
Epigenetically
altered, selfrenewing cancer
stem cells
38. Lab for Bioinformatics and computational genomics
Gene-specific
Epigenetic
reprogramming
39.
40.
41.
42.
43.
44. Personalized Medicine
•
•
•
The use of diagnostic tests (aka biomarkers) to identify in advance
which patients are likely to respond well to a therapy
The benefits of this approach are to
– avoid adverse drug reactions
– improve efficacy
– adjust the dose to suit the patient
– differentiate a product in a competitive market
– meet future legal or regulatory requirements
Potential uses of biomarkers
– Risk assessment
– Initial/early detection
– Prognosis
– Prediction/therapy selection
– Response assessment
– Monitoring for recurrence
45. Biomarker
First used in 1971 … An objective and
« predictive » measure … at the molecular
level … of normal and pathogenic processes
and responses to therapeutic interventions
Characteristic that is objectively measured and
evaluated as an indicator of normal biologic
or pathogenic processes or pharmacologic
response to a drug
A biomarker is valid if:
– It can be measured in a test system with well
established performance characteristics
– Evidence for its clinical significance has been
established
46. Rationale 1:
Why now ? Regulatory path becoming more clear
There is more at stake than
efficient drug
development. FDA
« critical path initiative »
Pharmacogenomics
guideline
Biomarkers are the
foundation of « evidence
based medicine » - who
should be treated, how
and with what.
Without Biomarkers
advances in targeted
therapy will be limited and
treatment remain largely
emperical. It is imperative
that Biomarker
development be
accelarated along with
therapeutics
47. Why now ?
First and maturing second generation molecular
profiling methodologies allow to stratify clinical
trial participants to include those most likely to
benefit from the drug candidate—and exclude
those who likely will not—pharmacogenomicsbased
Clinical trials should attain more specific results
with smaller numbers of patients. Smaller
numbers mean fewer costs (factor 2-10)
An additional benefit for trial participants and
internal review boards (IRBs) is that
stratification, given the correct biomarker, may
reduce or eliminate adverse events.
48. Molecular Profiling
The study of specific patterns (fingerprints) of proteins,
DNA, and/or mRNA and how these patterns correlate
with an individual's physical characteristics or
symptoms of disease.
49. Generic Health advice
•Exercise (Hypertrophic Cardiomyopathy)
•Drink your milk (MCM6 Lactose intolarance)
•Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)
•& your grains (HLA-DQ2 – Celiac disease)
•& your iron (HFE - Hemochromatosis)
•Get more rest (HLA-DR2 - Narcolepsy)
50. Generic Health advice (UNLESS)
•Exercise (Hypertrophic Cardiomyopathy)
•Drink your milk (MCM6 Lactose intolarance)
•Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)
•& your grains (HLA-DQ2 – Celiac disease)
•& your iron (HFE - Hemochromatosis)
•Get more rest (HLA-DR2 - Narcolepsy)
51. Generic Health advice (UNLESS)
•Exercise (Hypertrophic Cardiomyopathy)
•Drink your milk (MCM6 Lactose intolerance)
•Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)
•& your grains (HLA-DQ2 – Celiac disease)
•& your iron (HFE - Hemochromatosis)
•Get more rest (HLA-DR2 - Narcolepsy)
52. Generic Health advice (UNLESS)
•Exercise (Hypertrophic Cardiomyopathy)
•Drink your milk (MCM6 Lactose intolerance)
•Eat your green beans (glucose-6-phosphate
dehydrogenase Deficiency)
•& your grains (HLA-DQ2 – Celiac disease)
•& your iron (HFE - Hemochromatosis)
•Get more rest (HLA-DR2 - Narcolepsy)
53. Lab for Bioinformatics and computational genomics
Overview
^[now][transl⎮comput]ational[epi]genomic$
•
•
•
•
Who ? Where ?
Bioinformatics
(Epi)genetics
Technology: Next Gen
Sequencing
• Personal Genomics
55. Lab for Bioinformatics and computational genomics
Overview
^[now][transl⎮comput]ational[epi]genomic$
•
•
•
•
Who ? Where ?
Bioinformatics
(Epi)genetics
Technology: Next Gen
Sequencing
• Personal Genomics
56. Wobblebase Mission
provide tools to both specialists (researchers,
bioinformaticians, health care providers) and
individual consumers that unlock the power of
genomic data to the USER
enable personalized genomics today by simplifying
the way we organize, visualize and manage
genomic data.
57. PGM: Personal Genomics Manifesto
Everybody who wants to get his genome sequenced has the human right to do so.
No third party can own your genetic data, your genetic data is exclusively yours.
Nobody can be forced to get his genome analyzed or to reveal his genome to a
third party.
Your genome should allways be treated as confidential, private information.
People should be advised not to share their identity AND their entire genome on a
public forum.
People should be advised to use secure technologies that allow to maximally
protect phenotypic and/or genotype data.
People should be able to actively explore, manage and get updated interpretation
on their genomic data.
59. Choosing the Red Pill
The Technical Feasibility Argument
The Quality Argument
The Price Argument
The Logistics around the sample on howto
manage the data Argument
The Ethical debate
The Privacy/Security concern
64. The Human Microbiome
Christine Rodriguez, Ph.D.
Harvard Outreach 2012
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
65. Microbes are all over us
There are millions of microbes per
square inch on your body
Thousands of different species on the skin alone
Some thrive on dry patches of the elbow, others
thrive in moist environment of armpit
It is estimated that there are more microbes in
your intestine than there are human cells in your
body!
http://commons.wikimedia.org/wiki/File:Man_sha
dow_-_upper.png
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
66. What is the Human Microbiome?
Microbe: tiny living organism, such as bacterium,
fungus, protozoan, or virus
Microbiome: collectively all the microbes in the
human body; a community of microbes
Biofilm: a community of microbes that live together
on a surface
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
67. Microbes in the Human Microbiome include species
from each major domain
“Extremophile”
Archaebacteria
Bacteria
Fungi
http://en.wikipedia.org/wiki/File:Aspergillus_niger_01.jpg
http://en.wikipedia.org/wiki/File:SalmonellaNIAID.jpg
http://en.wikipedia.org/wiki/File:Grand_prismatic_spring.jpg
http://commons.wikimedia.org/wiki/File:Tree_of_life.svg
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
68. What features distinguish the
microbial domains?
Bacteria
•Have no nucleus or membrane bound organelles
•Often sphere (cocci) or rod (bacillus) shape, but others as well
Generalized
bacteria and
archaebacteria
cell
Archeabacteria
•Have no nucleus or membrane bound organelles
•Can look similar to bacteria or drastically different shapes, such
as flat and square
•Have some metabolic similarities to eukaryotes
Eukaryotes
•Have a true nucleus and membrane bound organelles
•Wide variety of shapes. For this presentation, we will focus on fungi
•Fungi are unique since they have a cell wall and form spores during
reproduction
eneralized eukaryotic cell
http://biodidac.bio.uottawa.ca/thumbnails/filedet.htm?File_name=CELL006B&File_type=GIF
http://biodidac.bio.uottawa.ca/thumbnails/filedet.htm?File_name=BACT003B&File_type=GIF
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
69. Microbes are normally found in and
on the human body
The following sites are “hotspots” for microbial life
Some microbes are native,
normally found in the body
Let’s explore
these five
regions
Some microbes are
introduced, suddenly
arriving at a new residence
in the body
http://nihroadmap.nih.gov/hmp/
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
70. What’s Happening
in the Nose?
Cilia and mucous
lining trap inhaled
microbes
The nose is a
primary defender
against inhaled
pathogens
Inflammation
from viral
infection and
allergic reactions
Inhaled medicines
and oral antibiotics
There is a delicate balance of microbes that are maintained to keep that environment
healthy. Weakened immune systems can throw off that balance and allow the wrong
microbes to grow out of control.
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
http://commons.wikimedia.org/wiki/File:Human-nose.jpg
71. Nose
The interior lining of the nose contains mucous secreting glands. A wide variety of
microbes are normally found there. Here’s a few:
• Staphylococcus epidermidis bacteria forms a biofilm that
coats the mucosal lining
• Staphylococcus aureus bacteria is fine when kept under
control by a protease found in S. epidermidis, but if left to
grow out of control, S. aureus can become pathogenic and
cause infection
Summer 2012 Workshop in Biology and
Multimedia for High School Teachers
http://commons.wikimedia.org/wiki/File:Human-nose.jpg
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72. Nose
• Aspergillus fungal spores are often
inhaled through the nose. If the immune
system fails to clear these, mold can grow
in the lungs
•Corneybacterium accolens bacteria is rarely a pathogen,
but if it enters the bloodstream due to a torn blood vessel,
it can cause serious infections
Summer 2012 Workshop in Biology and
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http://commons.wikimedia.org/wiki/File:Human-nose.jpg
http://en.wikipedia.org/wiki/File:Corynebacterium_ulcerans_01.jpg
http://en.wikipedia.org/wiki/File:Aspergillus.jpg
http://en.wikipedia.org/wiki/File:Aspergillus_fumigatus_Invasive_Disease_Mechanism_Diagram.jpg
73. What’s Happening in
the Oral Cavity?
A wide variety
of microbes
regularly enter
the oral cavity
Brushing and flossing teeth
clears some built up biofilm
saliva, pH,
temperature, immune
system prevent many
species from surviving
Oral antibiotics
inhibit growth
Symbiosis of the oral microbes that are able to survive these conditions form an elaborate
scaffold that lives on the tooth enamel and at the interface with the gums. It forms a
barrier for incoming bacteria.
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http://en.wikipedia.org/wiki/File:Teeth_by_David_Shankbone.jpg
74. Oral Cavity
The oral cavity has a wide variety of microbes normally found there. Here’s a few:
Fusobacterium sp.
bacteria is a larger
bacteria that helps
form a scaffold for
many other bacteria
in the oral biofilm
http://en.wikipedia.org/wiki/File:Teeth_by_David_Shankbone.jpg
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Streptococcus mitis
bacteria typically forms a
biofilm on the hard
enamel surfaces of the
teeth. If gums get
inflamed, it can enter the
bloodstream and cause
infection
75. Oral Cavity
•Prevotella sp. bacteria have natural antibiotic resistance
genes. They can attach to epithelial cells or other bacteria
and cause larger infections in inflamed areas.
• Candida albicans fungus can cause oral infection known as
thrush
http://microbewiki.kenyon.edu/index.php/File:P_ruminicola.jpg
http://en.wikipedia.org/wiki/File:Teeth_by_David_Shankbone.jpg
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http://en.wikipedia.org/wiki/File:Thrush.JPG
http://en.wikipedia.org/wiki/File:Candida_albicans_2.jpg
76. What’s Happening
on the Skin?
There are several skin
environments: oily, dry,
moist. Some microbes
prefer one over another.
The skin has natural
defenses including
slightly acidic sweat and
antimicrobial peptides.
Microbes hide in crevices
to recolonize skin after
washing with soap
Antibiotic washes and
oral antibiotics disturb
normal balance of
microbes on the skin
There is a normal balance of microbes on the skin that protect introduced microbes from
harming us. Damaged skin gives opportunities for microbes to invade the bloodstream and
cause serious illness.
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http://commons.wikimedia.org/wiki/File:Anterior_view_of_male_upper_body,_retouched.jpg
77. Skin
• Propionibacterium acnes bacteria colonizes healthy pores, but if pores
become clogged, it grows out of control
• Staphylococcus epidermidis bacteria normally colonizes on the skin. But when
P. acnes clogs pores, S. epidermidis also grows out of control in the infected
pores
• Staphylococcus aureus bacteria can also infect clogged pores like Staph
epidermidis. Even worse, many antibiotic resistant strains of Staph aureus
make it difficult to treat the infection.
http://microbewiki.kenyon.edu/index.php/File:Lesionsmicro.jpg
http://microbewiki.kenyon.edu/index.php/File:Lesionsclosed.jpg
http://commons.wikimedia.org/wiki/File:Anterior_view_of_male_upper_body,_retouched.jpg
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78. Skin
Trichophyton and Microsporum fungi feast on keratin in the skin
and cause ringworm fungal infections
http://en.wikipedia.org/wiki/File:Yeartinfection.JPG
http://commons.wikimedia.org/wiki/File:Anterior_view_of_male_upper_body,_retouched.jpg
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79. What’s Happening
in the Gut?
Major barriers for microbes entering the gut:
•low pH
•Saliva and Bile
•Immune system
•Finding a place to attach to intestinal wall
•Surviving a widely varied diet
For those microbes that manage to colonize the gut:
•gut flora perform regular tasks of digestion, vitamin production, many others
• Gene transfer between the myriad of species in the gut can generate new
combinations of drug resistant “superbugs”
http://commons.wikimedia.org/wiki/File:Intestine_and_stomach_-_transparent_-_cut.png
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80. Gut
Bacteroides thetaiotaomicron
bacteria ferments simple
carbohydrates in the gut,
releasing hydrogen and CO2.
+ carbohydrates
CO2 and H2
Methanobrevibacter smithii
archeabacteria consumes
hydrogen gas from Bacteroides
and produces methane, which is
lost from gut as “gas”
http://commons.wikimedia.org/wiki/File:Intestine_and_stomach_-_transparent_-_cut.png
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CH4 Methane
Gas
81. Gut
Ruminococcus sp. bacteria can be found in significantly
high numbers in the gut flora. They break down cellulose
in the gut, helping with digestion.
Helicobacter pylori bacteria has a helical shape and colonizes the
stomach and upper G.I. tract. It is known to be a major cause of
stomach ulcers, although many with H. pylori do not get ulcers.
http://microbewiki.kenyon.edu/index.php/File:G_reaction1.jpg
http://commons.wikimedia.org/wiki/File:Intestine_and_stomach_-_transparent_-_cut.png
Summer 2012 Workshop in Biology and
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http://commons.wikimedia.org/wiki/File:Helicobacter_pylori_diagram.png
82. What’s Happening in the
Urogenital Tract?
Urinary system almost
sterile due to urea and
other chemicals
Introducing a catheter into
the urethra can introduce
microbes directly into the
bladder, where a biofilm
can grow and cause bladder
infection
Urine often flushes
out microbes that
find their way in
The vagina has a low pH due to Lactobacillus secreting lactic acid and hydrogen peroxide.
Let’s explore the microbiome of this region further.
Summer 2012 Workshop in Biology and
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http://commons.wikimedia.org/wiki/File:Female_Genital_Organs.svg
83. Urogenital
Lactobacillus
normally maintain
low pH while other
species are kept in
small numbers in
the vagina
Candida albicans
can take over and
cause a yeast
infection
If Lactobacillus
decreases from
antibiotics…
Lactobacillus and vaginal epithelial cell
G. vaginalis and vaginal epithelial cell
Gardnerella vaginalis
can grow too much
and cause bacterial
vaginosis.
http://commons.wikimedia.org/wiki/File:Lactobacillus_sp_01.png
http://commons.wikimedia.org/wiki/File:Female_Genital_Organs.svg
Summer 2012 Workshop in Biology and
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http://en.wikipedia.org/wiki/File:Candida_albicans_2.jpg
84. Urogenital
The urinary tract is normally sterile due to urine flushing out the tract.
Urine sample infected with E. coli
Urine sample infected with E. coli
But, Escherichia coli from GI tract can infect urinary tract due to poor hygiene
and contamination from nearby GI tract opening.
http://commons.wikimedia.org/wiki/File:Female_Genital_Organs.svg
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http://commons.wikimedia.org/wiki/File:E_choli_Gram.JPG
http://commons.wikimedia.org/wiki/File:Pyuria2011.JPG
85. Interplay Between
Medicine and Microbes
Antibiotics
Chemotherapy drugs
Kills infectious bacteria but also disrupts
natural flora. Can result in yeast
infections, digestive problems, etc.
http://commons.wikimedia.org/wiki/File:Chemotherapy_bottles_NCI.jpg
http://commons.wikimedia.org/wiki/File:NOVAMOXIN_antibiotic.jpg
Gut flora has been shown to modify
some drugs during metabolism. This
causes many side effects, including upset
stomach.
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86. Use of Antimicrobial Products
How many do we really need?
But do we need some
natural exposure to
germs to keep our
normal flora around?
Products kill germs
to reduce infection
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Will this allow “superbugs”
that can barely survive
these treatments to grow
and become more
prevalent…causing
problems for the future?
87. Is My Gut Microbiome the
Same as Yours?
The number and amount
of the many different
microbes can vary greatly
from person to person.
Summer 2012 Workshop in Biology and
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88. Relative amounts of species
Research in the Human Microbiome
Project is starting to identify the relative
amount of each microbe present at
different locations in the body.
The Microbiome of one person
can be different than others in
species and relative amounts
Summer 2012 Workshop in Biology and
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http://en.wikipedia.org/wiki/File:Skin_Microbiome20169-300.jpg
89. So many new questions to answer
about the Human Microbiome…
How does the gut
flora modify drugs,
and how can we
minimize side effects?
Are we making germs more
resistant to anitmicrobials?
What happens when the
germs are resistant to all of
the drugs in our arsenal?
Why does my gut flora look
different than yours? How
does that affect obesity,
food allergies, and ability to
fight disease?
What do you want to know?
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http://commons.wikimedia.org/wiki/File:Chemotherapy_bottles_NCI.jpg
http://commons.wikimedia.org/wiki/File:Intestine_and_stomach_-_transparent_-_cut.png
Summer 2012 Workshop in Biology and
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Here, we define epigenetics and depict the relationship between the genome and the epigenome
The genome is hereditary information encoded in the DNA and the epigenome is the way cells express the encoded information1
The epigenome is a ‘bridge’ between genotype and phenotype (epigenetics governs genotype and phenotype)
Epigenetic information is included in the genome of a cell but is not encoded by the DNA1,2
Epigenetic information may be inherited from precursor cells1
Epigenetic changes affect chromosome structure to alter gene expression1,2
References
Goldberg AD et al. Cell 2007;128:635–8.
Bernstein BE et al. Cell 2007;128:669–81.
This slide shows the histone and DNA components and the nucleosome subunits of chromatin, located in the cell nucleus1
Chromatin consists of repeating units of nucleosomes: DNA wrapped around histone octamers, which consist of two copies each of histones H2A, H2B, H3, and H41
The function of chromatin is to package DNA and to organize and arrange genes to be accessible to factors involved in transcription, replication, and repair1
Epigenetic mechanisms involve modifications of DNA and histone proteins which affect chromatin structure1
References
Wang GG et al. Trends Mol Med 2007;13:363–72.
This slide illustrates the epigenetic modifications of histones and DNA that are discussed in this slide set
Epigenetic modifications change the chemical interactions of histones and DNA, which leads to changes in chromatin structure1
Of all of the known types of histone modifications, acetylation and methylation are among the best characterized2
Histones can also be modified by phosphorylation, isoprenylation, ubiquitination, sumoylation and poly(ADP) ribosylation2
Epigenetic modification of DNA involves methylation of cytosine residues3
References
Jones PA, Baylin SB. Cell 2007;128:683–92.
Kouzarides T. Cell 2007;128:693–705.
Esteller M. Nat Rev Genet 2007;8:286–98.
Here we illustrate one way in which changes in chromatin structure affect gene expression
Gene expression (transcription) requires DNA to be physically accessible to transcription factors (TF)1,2
The compactness of the chromatin structure – or degree of wrapping of DNA – determines whether DNA is accessible
Epigenetic modifications to histones and DNA can change chromatin structure, which regulates gene expression1,2
The panel on the left of the slide depicts an ‘open’ chromatin structure, which may allow transcription factor binding and gene expression
The panel on the right depicts a ‘closed’ chromatin structure, which may block transcription factor binding and inhibit gene expression
References
Kouzarides T. Cell 2007;128:693–705.
Drummond DC. Annu Rev Pharmacol Toxicol 2005;45:495–528.
This slide outlines the role of genetic changes in the development of cancer
Traditionally, cancer has been considered a disease of genetic defects, such as gene mutations, deletions, and chromosomal abnormalities1
On the left of this slide, we see that mutations in DNA sequence result in production of mRNA and proteins with altered function
Mutation of proteins involved in cell growth and death can lead to deregulated cell proliferation and survival, resulting in cancer2
Examples:
Mutations in p532
Activating mutations in RAS2
Mutations or amplifications of the HER-2 gene3
Chromosomal translocations in myeloid cells and the generation of the BCR-ABL fusion protein3
References
Bolden JE et al. Nat Rev Drug Discov 2006;5:769–84.
Rieger PT Semin Oncol Nurs 2004;20:145–54.
Croce CM N Engl J Med 2008;358:502–11.
There is growing evidence that epigenetic modifications are also crucial to the onset and progression of cancer1
On the right of the slide, we see that changes in gene expression due to chromatin modifications (e.g. histone acetylation, DNA methylation) lead to altered levels of mRNA and proteins
Altered levels of proteins involved in cell growth and death can lead to deregulated cell proliferation and survival, resulting in cancer 2
Examples:
Silencing of p15 tumor suppressor gene expression3
Aberrant expression of IGF24
Silencing of ER-α gene expression3
References
Bolden JE et al. Nat Rev Drug Discov 2006;5:769–84.
Miranda E et al. Br J Cancer 2006;95:1101–7.
Esteller M. N Engl J Med 2008;358:1148–59.
Feinberg AP. Nature 2007;447:433–40.
The involvement of epigenetics in cancer development has led researchers to propose the cancer stem cell, or malignant progenitor cancer cell, theory1,2:
Self renewal and differentiation of stem cells are largely governed by epigenetic programs
Occasionally, some stem cells acquire heritable epigenetic and genetic changes that result in the malignant phenotype
Malignant stem cells “seed” highly proliferative and transformed cells that cause cancer
Evidence in support of an epigenetic cancer stem cell model for anti-tumor therapy1:
Studies of tumor cells show that epigenetic deregulation is reversible
Global epigenetic changes are often found to precede genetic mutations in cancer and are found in benign neoplasia as well as tumors
Most of the properties of tumor cells are epigenetically controlled and can be reprogrammed to follow normal development
Since the viability and differentiation of malignant stem cells is highly governed by epigenetic mechanisms, targeting epigenetic regulators may offer a more complete approach for the treatment of cancer1
The importance of epigenetics in cancer can be conceptualized as a tree, where epigenetic therapy may affect both ‘branches’ (full blown tumor cells) and ‘roots’ (malignant stem cells), as shown in the following slides
References
Jones PA, Baylin SB. Cell 2007;128:683–92.
Feinberg AP et al. Nat Rev Genet 2006;7:21–33.