Chapter 22— Genomics II Functional Genomics—studying genes in groups, with respect to the cell, tissue, signaling pathway or organism Proteomics—to understand the interplay among many different proteins (cellular processes and organismal level [traits]) Bioinformatics—using computers, math, and statistics to understand the genome and proteome information (record, store, analyze, predict)

Chapter 22— Genomics II Functional Genomics—studying genes in groups, with respect to the cell, tissue, signaling pathway or organism Proteomics—to understand the interplay among many different proteins (cellular processes and organismal level [traits]) Bioinformatics—using computers, math, and statistics to understand the genome and proteome information (record, store, analyze, predict)

Add reverse transcriptase, poly-dT primers that anneal to the mRNAs, and fluorescent nucleotides. Note: Only 1 complementary cDNA strand is made. View with a laser scanner. Hybridize cDNAs to the microarray. A mixture of 3 different types of mRNA A portion of a DNA microarray Fluorescently labeled cDNA that is complementary to the mRNA A A A AB CD EF AB CD EF D F F F D D A A A D F F F D D Figure 22.1 Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Microarrays for studying gene expression or re- sequencing

Modern day “Southerns” and “Northerns”—microarray analysis

Two distinct forms of large B-cell lymphoma are shown by the expression pattern: GC B-like DLBCL (orange) and Activated B-like DLBCL (blue) ASH ALIZADEH et al Nature 403, (3 February 2000) significantly better overall survival

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Observation/problem Diffuse large B-cell lymphoma (DLBCL) = most common subtype of non-Hodgkin's lymphoma is clinically heterogeneous: 40% of patients respond well to current therapy and have prolonged survival, whereas the remainder succumb to the disease Hypothesis  variability in natural history reflects unrecognized molecular heterogeneity in the tumours. Experiment DNA microarrays used for a systematic characterization of gene expression in B-cell malignancies. Results Diversity in gene expression among the tumours of DLBCL patients (reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour). Identified two molecularly distinct forms of DLBCL which had gene expression patterns indicative of different stages of B-cell differentiation. –One type expressed genes characteristic of germinal centre B cells ('germinal centre B-like DLBCL'); –the second type expressed genes normally induced during in vitro activation of peripheral blood B cells ('activated B-like DLBCL'). Patients with germinal centre B-like DLBCL had a significantly better overall survival than those with activated B-like DLBCL. Conclusion Molecular classification of tumours on the basis of gene expression can thus identify previously undetected and clinically significant subtypes of cancer. ASH ALIZADEH et al Nature 403, (3 February 2000)

Add formaldehyde to crosslink protein to DNA. Lyse the cells. Sonicate DNA into small pieces. Add antibodies that recognize the protein of interest. The antibodies are bound to heavy beads. After the antibodies bind to the protein of interest, the sample is subjected to centrifugation. Collect complexes in pellet. Add chemical that breaks the crosslinks to remove the protein. Unknown Candidates: Ligate DNA linkers to the ends of the DNA. Known Candidates: Conduct PCR using primers to a known DNA region. If PCR amplifies the DNA, the protein was bound to the DNA region recognized by the primers. Conduct PCR using primers that are complementary to the linkers. Incorporate fluorescently labeled nucleotides during PCR. Denature DNA and hybridize to a microarray. Antibody against protein of interest Protein of interest Bead Protein of interest Linker or Pellet See Figure 22.1 Figure 22.2 Which DNA sequences bind to my protein of interest? Chromatin Immunoprecipitation Assay (ChIP)

Chapter 22—Genomics II Functional Genomics—studying genes in groups, with respect to the cell, tissue, signaling pathway or organism Proteomics—to understand the interplay among many different proteins (cellular processes and organismal level [traits]) Bioinformatics—using computers, math, and statistics to understand the genome and proteome information (record, store, analyze, predict)

Exon 1Exon 2Exon 3Exon 4Exon 5Exon 6 Exon 1 Exon 2 Exon 4 Exon 5 Alternative splicing Translation Exon 6 Exon 1 (a) Alternative splicing Exon 3 Exon 4 or Exon 5 Exon 6 pre-mRNA Exon 1 Exon 2 Exon 4 Exon 6 Why is the proteome so large? Alternative splicing

Proteolytic processing Attachment of prosthetic groups, sugars, or lipids Sugar Heme group Phospholipid Disulfide bond formation SSSH Irreversible modifications (b) Posttranslational covalent modification Phosphorylation Methylation Phosphate group Acetyl group Methyl group PO 4 2- C CH 3 O Reversible modifications Acetylation Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Why is the proteome so large? Post translational modification

SDS-polyacrylamide gel Proteins migrate until they reach the pH where their net charge is 0. At this point, a single band could contain 2 or more different proteins. Lyse a sample of cells and load the resulting mixture of proteins onto an isoelectric focusing gel. pH 10.0pH 4.0 pH 10.0 pH kDa 10 kDa Lay the tube gel onto an SDS-polyacrylamide gel and separate proteins according to their molecular mass. Techniques to study the proteome: 2D Gel analysis Brooker, Fig 22.4

Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Digest protein into small fragments using a protease. Determine the mass of these fragments with a first spectrometer. C N C N Purified protein Mass/charge Abundance daltons Techniques to study the proteome: Mass spectrometry Brooker, Fig 22.5

Brooker, Fig 22.5, cont. Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Analyze this fragment with a second spectrometer. The peptide is fragmented from one end. Mass/charge Abundance daltons Mass/charge Abundance 900 –Asn–Ser–Asn–Leu–His–Ser– Tandem mass spectrometry to sequence peptides

Chapter 22—Genomics II Functional Genomics—studying genes in groups, with respect to the cell, tissue, signaling pathway or organism Proteomics—to understand the interplay among many different proteins (cellular processes and organismal level [traits]) Bioinformatics—using computers, math, and statistics to understand the genome and proteome information (record, store, analyze, predict)

Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Numbers represent the base number in the sequence file Example of DNA Sequence as stored in Genetic Database

A bioinformatics program may ask: Does the sequence contain a gene? Which nt’s are the functional sites (e.g. promoters, exons, introns, termination sequence)? Does the sequence encode a protein? (have an open reading frame [ORF] What is the secondary structure of its RNA or associated amino acid sequence? Is the sequence homologous to any other known sequences? What is the evolutionary relationship between two or more sequences?

3′ end 5′ end Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Brooker, Fig 22.7 A secondary structural model for E. coli 16S rRNA

Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display DNA sequences of the lacY gene – ~ 78% of the bases are a perfect match In this case, the two sequences are similar because the genes are homologous to each other – They have been derived from the same ancestral gene – Refer to Figure 22.6 Sequence matches between E. coli and K. pneumoniae

Human Pa Ca Mouse Lu Ca Human LHON, Human Thy Ca Mouse Lu Ca Example output from a computer alignment program (and comparison to real world data) Interesting cancer mutation pattern in mitochondrial ND6 protein

Sequence homology used to “hang” human cancer mutations on the bovine crystal structure of Cytochrome B Chen and Uberto 2014

Federal Genetic Databases National Center for Biotechnology Information U.S. government-funded national resource for molecular biology information.

BLAST programs identify sequences with homology or similarity Table 22.5

Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Figure 22.6 Accumulation of random mutations in the 2 genes Mutation Ancestral lacY gene Ancestral organism Evolutionary separation of 2 (or more) distinct species lacY gene E. coli lacY gene K. pneumoniae Mutation Origin of orthologous genes

Myoglobin  chains  chains Hemoglobins Millions of years ago 1, Mb ζψζψζ ψα2ψα2 ψα1ψα1 α2α2 α1α1  ε GG AA ψβψβ δ β Copyright ©The McGraw-Hill Companies, Inc. Permission required for reproduction or display Brooker, Fig 8.7 Duplication Better at binding and storing oxygen in muscle cells Better at binding and transporting oxygen via red blood cells Ancestral globin

Orthologs, paralogs, homologs From Thompson and Thompson, Genetics in Medicine, 6 th ed. Like Brooker fig 8-7 All the globin genes have homology to each other  -like genes are paralogs of each other;  -like genes are paralogs of each other;  -1 in mice and  -1 in humans are orthologs