GENOMICS AND PROTEOMICS ANALYSES

Transcription

1 GENOMICS AND PROTEOMICS ANALYSES Dr Joshua Boateng 21 /11 / 2011

2 Biotech and pharmaceutical companies spent $10 billion on hardware, software, and services in Source: Gartner The biotechnology/it market will increase at a compound annual growth rate (CAGR) of 24% to nearly $38 billion by Source: IDC Research Reference: Prof. A.S. Kolaskar Vice Chancellor, University of Pune

3 GENOMICS Genetics: the science of genes, heredity, and the variation of organisms. In modern research, genetics provides tools in the investigation of the function of a particular gene, e.g. analysis of genetic interactions. Genomics: the study of large-scale genetic patterns across the genome for a given species. It deals with the systematic use of genome information to provide answers in biology, medicine, and industry.

4 The study of sequences, gene organization & mutations at the DNA level i.e. the study of information flow within a cell Genomics has the potential of offering new therapeutic methods for the treatment of some diseases, as well as new diagnostic methods. Major tools and methods related to genomics are bioinformatics, genetic analysis, measurement of gene expression, and determination of gene function.

5 GENOME COMPARISONS Species Chrom. Genes Base pairs Humans , billion Mouse billion Puffer fish million Malaria Mosquito million Fruit Fly million Roundworm million E. Coli million

6 GENOMIC ANALYSIS Many diverse studies require the determination of the abundance of large numbers of specific DNA or RNA molecules in complex mixtures, including, for example, the determination of the changes in mrna levels of many genes Genome analysis entails the prediction of genes in uncharacterized genomic sequences. The 21st century has seen the announcement of the draft version of the human genome sequence. Model organisms have been sequenced in both the plant and animal kingdoms.

7 GENOMIC ANALSIS However, the pace of genome annotation is not matching the pace of genome sequencing. Experimental genome annotation is slow and time consuming. The demand is to be able to develop computational tools for gene prediction. Computational gene prediction is relatively simple for the prokaryotes where all the genes are converted into the corresponding mrna and then into proteins. The process is more complex for eukaryotic cells where the coding DNA sequence is interrupted by random sequences called introns.

8 BIOLOGICAL QUESTIONS Some of the questions biologists want to answer today are: What part of and DNA sequence codes for a protein and what part of it is junk DNA? Classify the junk DNA as intron, untranslated region, transposons, dead genes, regulatory elements. Divide a newly sequenced genome into the genes (coding) and the non-coding regions.

9 Biological Research in 21st Century The new paradigm, now emerging is that all the 'genes' will be known (in the sense of being resident in databases available electronically), and that the starting "point of a biological investigation will be theoretical. - Walter Gilbert

10 IMPORTANCE OF GENOME ANALYSIS The importance of genome analysis can be understood by comparing the human and chimpanzee genomes. The chimp and human genomes vary by an average of just 2% i.e. just about 160 enzymes. A complete genome analysis of the two genomes would give a strong insight into the various mechanisms responsible for the differences.

11 COMPLEXITY IS AN UNDERSTATEMENT?

12 GENOMIC ANALYSIS_ basics Techniques used to estimate the relative abundance of two or more sets of mrna differential screening of cdna libraries, subtractive hybridization, differential display, However, more advanced methods have been recently developed.

13 GENOMICS ANALYSIS_Advances Advanced methods are particularly amenable to organisms whose entire genome sequences are known, such as S. cerevisiae. It is now practicable to investigate changes of mrna levels of all yeast open reading frames (ORFs) in one experiment.

14 Advanced genomic analysis techniques DNA sequencing DNA microarray technology analysis of gene expression profiles at the mrna level Bioinformatic tools to organize and analyze such data Chip-based analysis of samples Models of gene networks

15 Microarray Technology

16 Series of omics Post-genomic Era Comparative genomics Structural and functional genomics Transriptomics Proteomics Metabolomics

17 tools needed for analysis of data from these omics

18 Data Mining Development of new tools for data mining Sequence alignment Genome sequencing Genome comparison Micro array data analysis Proteomics data analysis Small molecular array analysis To derive information and gain knowledge from the data

19 COMPARATIVE GENOMICS Analyzing & comparing genetic material from different species to study evolution, gene function, and inherited disease Understand the uniqueness between different species Comparative genomics involves the use of computer programs that can line up multiple genomes and look for regions of similarity among them.

20 When we BLAST a sequence is that comparative genomics? Difference is in Scale and Direction Other omics Comparative One or several genes compared against all other known genes. Use genome to inform us about the entire organism. Entire Genome compared to other entire genomes. Use information from many genomes to learn more about the individual genes.

21 Background on Comparative Genomic Analysis Sequencing the genomes of the human, the mouse and a wide variety of other organisms - from yeast to chimpanzees Driving force for the development of new field of biological research called - comparative genomics.

22 BACKGROUND Comparing the human genome with the genomes of different organisms helps to better understand gene structure and function and thereby develop new strategies in the battle against human disease. Comparative genomics also provides a powerful new tool for studying evolutionary changes among organisms.

23 This helps to identify the genes that are conserved among species along with the genes that give each organism its own unique characteristics. Using computer-based analysis to zero in on the genomic features that have been preserved in multiple organisms over millions of years, researchers will be able to pinpoint the signals that control gene function. This should in turn translate into innovative approaches for treating human disease and improving human health.

24 BACKGROUND The evolutionary perspective may prove extremely helpful in understanding disease susceptibility. For example, chimpanzees do not suffer from some of the diseases that strike humans, such as malaria and AIDS. A comparison of the sequence of genes involved in disease susceptibility may reveal the reasons for this species barrier, thereby suggesting new pathways for prevention of human disease.

25 BACKGROUND Although living creatures look and behave in many different ways, all of their genomes consist of DNA, the chemical chain that makes up the genes that code for thousands of different kinds of proteins. Precisely which protein is produced by a given gene is determined by the sequence in which four chemical building blocks - adenine (A), thymine (T), cytosine (C) and guanine (G) - are laid out along DNA's double-helix structure.

26 BACKGROUND In order for researchers to most efficiently use an organism's genome in comparative studies, data about its DNA must be in large, contiguous segments, anchored to chromosomes and, ideally, fully sequenced. Furthermore, the data needs to be organized for easy access and high-speed analysis by sophisticated computer software. Organisms that have been completely sequenced include: mouse (Mus musculus), human (Homo sapiens), fruit fly (Drosophila melanogaster); and...

27 BACKGROUND The fledgling field of comparative genomics has already yielded some dramatic results. For example, a March 2000 study comparing the fruit fly genome with the human genome discovered that about 60 percent of genes are conserved between fly and human. Simply put, the two organisms appear to share a core set of genes. Researchers have found that two-thirds of human cancer genes have counterparts in the fruit fly.

28 BACKGROUND More surprisingly, when scientists inserted a human gene associated with early-onset Parkinson's disease into fruit flies, they displayed symptoms similar to those seen in humans with the disorder. This raises the possibility that the tiny insects could serve as a new model for testing therapies aimed at Parkinson's.

29 Comparative Genomics What one should look for? Human P. falciparum Mosquito Proteins that are shared by All genomes Exclusively by Human & P.f. Exclusively by Human & Mosquito Exclusively by P.f. & Mosquito Unique proteins in Human P.f. Targets for anti-malarial drugs Mosquito

30 Comparative Gene Prediction GenScan : ab initio gene prediction. GeneWise, Procrustes : homology guided. Rosseta, SGP1 (Syntetic Gene Prediction), CEM (Conserved Exon Method) : gene prediction and sequence alignment are clearly separated. GenomeScan : Ab Initio modified by BLAST homologies. SGP-2, TwinScan, SLAM, DoubleScan : modification of GenScan scoring schema to incorporate similarity to known proteins.

31 Proteome by the dictionary The term proteome, coined in A linguistic equivalent to the concept of genome Proteome - complete set of proteins that is expressed, and modified by the entire genome in the lifetime of a cell. Practical: the complement of proteins expressed by a cell at any one time.

32 Proteomics by the dictionary Proteomics (Practical) - the study of the proteome using technologies of large-scale protein separation and identification. Large scale separation : 2DE Liquid Chromatography Identification : MALDI MS Tandem MS/MS FT-MS..

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34 Proteomics according to Medline Development of Proteomics From 220 publications in the previous millennium ( 94-99) To 21,350 (!!!) publications in this millennium ( 00-05) Papers Reviews

35 Proteomics by Google THE REALISTIC TRUTH. Proteomics 886,000 hits (2004) 4,700,000 hits (2005) Genomics 2,070,000 hits (2004) 16,000,000 hits (2005)

36 Comparing Proteomics & Genomics Genome Genomics proteome Proteome analysis analysis DNA Nc-RNA linear Completion mrna Coding DNA Proteins Dynamic Up/down Archived (EST, cdna, GEO 3D No notion of completion Peptides Glyco, other modifications Dynamic Up/ down variants Poorly archived

37 Proteomics Genomics More differences Handle Gene/ RNA dynamic Stable molecules Protein dynamic Fragile molecules Tech Handling cheap/ easy Minimal modification Works in isolation Sequencing (established) Handling dependent Labile modification Protein-interaction Localization dependent MS related (not yet) HTP DNA array / genotyping/ expression / CGH/ Protein Chip (not yet) Antibodies array (not yet)

38 Proteomics: Original definition: study of the proteins encoded by the genome of a biological sample Current definition: study of the whole protein complement of a biological sample (cell, tissue, animal, biological fluid [urine, serum]) Usually involves high resolution separation of polypeptides at front-end, followed by mass spectrometry identification and analysis

39 Challenges facing Proteomic Technologies Limited/variable sample material Sample degradation (occurs rapidly, even during sample preparation) Vast dynamic range required Post-translational modifications (often skew results) Specificity among tissue, developmental and temporal stages Perturbations by environmental (disease/drugs) conditions Researchers have deemed sequencing the genome easy, as PCR was able to assist in overcoming many of these issues in genomics.

40 The Proteomics Tool Kit technologies for separating and visualizing proteins and peptides technologies for assessing protein-protein interactions technologies for identifying proteins* technologies for quantifying protein expression* bioinformatic tools for assessment and communication

41 Proteomic Technologies Amino Acid Composition Array-based Proteomics 2D PAGE Mass Spectrometry Structural Proteomics Informatics (and the challenges facing the Human Proteome Project)

42 Amino Acid Composition (Edmund) Pioneering method of obtaining information from proteins. Cumbersome and tedious by today s standards. Requires the use of terrible smelling ß- mercaptoethanol. Not high-throughput by today s standards, hence, comp is no longer the most widely used technique.

43 Protein Sequencing step 1, fragmenting into peptides

44 Protein Sequencing step 2, sequencing the peptides by Edmund degradation. Separation by HPLC and detect by absorbance at 269nm.

45 Array-based Proteomics Employ two-hybrid assays Use GFP, FRET, and GST GFP = green florescent protein FRET = florescence resonance energy transfer GST = glutathione S-transferase, a well characterized protein used as a marker protein.

46 Array-based Proteomics

47 Array-based Proteomics Offer a high-throughput technique for proteome analysis. These small plates are able to hold many different samples at a time. Current research is ongoing in an attempt to interface array methodologies with Mass Spectrometry at ORNL.

48 2D PAGE 2-D gel electrophoresis is a multi-step procedure that can be used to separate hundreds to thousands of proteins with extremely high resolution. It works by separation of proteins by their pi's in one dimension using an immobilized ph gradient (first dimension: isoelectric focusing) and then by their MW's in the second dimension. The core technology of proteomics is 2-DE At present, there is no other technique that is capable of simultaneously resolving thousands of proteins in one separation procedure. (sited in 2000)

49 Evolution of 2-DE methodology Traditional IEF procedure: Iso electric focusing (IEF) in run in thin polyacrylamide gel rods in glass or plastic tubes. Gel rods containing: 1. urea, 2. detergent, 3. reductant, and 4. carrier ampholytes (form ph gradient). Problem: 1. tedious. 2. not reproducible. In the past

50 Evolution of 2-DE methodology SDS-PAGE Gel size: This O Farrell techniques has been used for 20 years without major modification. 20 x 20 cm have become a standard for 2-DE. Assumption: 100 bands can be resolved by 20 cm long 1-DE. Therefore, 20 x 20 cm gel can resolved 100 x 100 = 10,000 proteins, in theory

51 Evolution of 2-DE methodology Problems with traditional 1 st dimension IEF Works well for native protein, not good for denaturing proteins, because: 1. Takes longer time to run. 2. Techniques are cumbersome. (the soft, thin, long gel rods needs excellent experiment technique) 3. Batch to batch variation of carrier ampholytes. 4. Patterns are not reproducible enough. OPERATOR DEPENDENT 5. Lost of most basic proteins and some acidic protein.

52 2D PAGE 2-D gel electrophoresis process consists of these steps: Sample preparation First dimension: isoelectric focusing Second dimension: gel electrophoresis Staining Imaging analysis via software

53 1. Spot number: Challenges for 2-DE 10, ,000 gene products in a cell. PTM makes it difficult to predict real number. Sensitivity and dynamic range of 2-DE must be adequate. It s impossible to display all proteins in one single gels.

54 Challenges for 2-DE 2. Isoelectric point spectrum: pi of proteins: range from ph (by in vitro translated ORF) PTM would not alter the pi outside this range. ph gradient from 3-13 dose not exist. For proteins which pi > 11.5, they need to be handed separately.

55 Challenges for 2-DE 3. molecular weights: Small proteins or peptides can be analysed by modifying the gel and buffer condition of SDS-PAGE. Protein > 250 kda do not enter 2 nd SDS-PAGE properly. 1-DE (SDS-PAGE) can be run in a lane at the side of 2-DE.

56 Challenges for 2-DE 4. hydrophobic proteins: Some very hydrophobic proteins do not go in solution. Some hydrophobic proteins are lost during sample preparation and iso electric focusing (IEF). More chemical developments are required.

57 Challenges for 2-DE 5. Sensitivity of detection: Low copy number proteins are very difficult to detect, even employing most sensitive staining methods. Sensitivity of staining methods: 1. Silver staining 2. Fluorescent staining 3. Dye binding staining (CBR)

58 Challenges for 2-DE 6. Loading capacity: For detection of low abundant proteins, more sample needs to be loaded. A wide dynamic range of the SDS-PAGE is required to prevent merging of highly abundant protein. Loading capacity: IEF > SDS-PAGE.

59 Challenges for 2-DE 7. Quantitation: The detection method must give reliable quantitative information. Silver staining does not give reliable quantitative data.

60 Challenges for 2-DE 8. Reproducibility: Highest importance in 2-DE experiment. Immobilized ph gradient strip have improved a lot for 1st dimension consistency Variation most comes from sample preparation.

61 A good-looking spot pattern streak and smear free is not a guarantee for best 2-DE protocol

62 Technologies for identifying Western blotting proteins Chemical (Edman) sequencing of proteins mass spectrometry peptide mass fingerprint mass spec decay databases and search engines

63 Mass Spectrometry Mass Spectrometry is another tool to analyze the proteome. In general a Mass Spectrometer consists of: Ion Source Mass Analyzer Detector Mass Spectrometers are used to quantify the mass-to-charge (m/z) ratios of substances. From this quantification, a mass is determined, proteins are identified, and further analysis is performed.

64 MASS SPECTROMETRY MORE DETAILED MASS SPECTROMETRY APPLICATIONS IN MORNING LECTURE ON 28TH NOVEMBER 2011

65 application of bioinformatics in the fields of genomics and proteomics

66 What is? Conceptualizing biology in terms of molecules and then applying informatics techniques from math, computer science, and statistics to understand and organize the information associated with these molecules on a large scale

67 How do we use? Store/retrieve biological information (databases) Retrieve/compare gene sequences Predict function of unknown genes/proteins Search for previously known functions of a gene Compare data with other researchers Compile/distribute data for other researchers

68 Sequence retrieval: National Center for Biotechnology Information GenBank and other genome databases Sequence comparison programs: BLAST GCG MacVector Protein Structure: 3D modeling programs RasMol, Protein Explorer

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70 Similarity Search: BLAST A tool for searching gene or protein sequence databases for related genes of interest Alignments between the query sequence and any given database sequence, allowing for mismatches and gaps, indicate their degree of similarity The structure, function, and evolution of a gene may be determined by such comparisons

71 % identity CATTATGATA GTTTATGATT 70% MRCKTETGAR MRCGTETGAR 90%

72 Strengths: Accessibility Growing rapidly User friendly Weaknesses: Sometimes not up-to-date Limited possibilities Limited comparisons and information Not accurate

73 Need for improved Genomics: Human Genome Project Gene array technology Comparative genomics Functional genomics Proteomics: Global view of protein function/interactions Protein motifs Structural databases

74 Data Mining Handling enormous amounts of data Sort through what is important and what is not Manipulate and analyze data to find patterns and variations that correlate with biological function

75 Proteomics Uses information determined by biochemical/crystal structure methods Visualization of protein structure Make protein-protein comparisons Used to determine: - conformation/folding - antibody binding sites - protein-protein interactions - computer aided drug design

76 students educators bioinformatics researchers institutions