biosci d145 lecture #1 -...

BioSci D145 lecture 1 page 1 ©copyright Bruce Blumberg 2020. All rights reserved

BioSci D145 Lecture #1

• Bruce Blumberg ([email protected]) – 4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment) – phone 824-8573

• TA – Angela Kuo ([email protected])

– 4311 Nat Sci 2– office hours TBA – Phone 824-6873

• check e-mail regularly for announcements, etc..

• Lectures will be posted in advance (without answers)

• Updated lectures (with answers) will be posted after lecture

– http://blumberg-lab.bio.uci.edu/biod145-w20120


Introductions and Goals

• Let’s introduce each other - – Name – Major – Favorite thing about UCI – Least favorite thing about UCI


Class requirements

• Grading Midterm 35% Final exam 35% Presentation 10% Term paper 10% Participation 10% (attendance, class discussion)

• How are grades determined?

• 20 minute presentation and discussion of a journal article is required • These will be randomly assigned – Angela will schedule yours • Presentations will be done as teams for most papers (depending on class size)

• Volunteers for 1/16 ? See Angela.

• Attendance and participation is important • Please come to class having read assigned material

• Final examination will not be cumulative, however, understanding of concepts and

techniques from first part of course is required.


General comments

• Overall philosophy – This class is about understanding genomic and proteomic (i.e. whole

genome) approaches to problems of biological interest • Focus will be on research problems

– Intended to be informative and cutting edge but also interesting and relevant, even fun.

– Office hours are after class but I am always around – Questions are welcome

• Please stop me and ask questions if something is unclear – I am going to ask you questions

• Answers get participation creditcredit

• Memorizing vs. understanding – I am not concerned with your memory – This course is about problem solving – how to address interesting

biological problems using modern, whole-genome approaches


General comments

• Letters of recommendation – If you want a letter from me, I need to know you as more than a student

number and grade • come to office hours • participate in class discussions • make your interest in the subject apparent


About the texts

• Bookstore vs. online?

• Neither text book is absolutely required – Brown has lots of introductory material that will help to fill in

background between BioSci 99 and this class

• Reading noted in text books are intended to supplement lecture material

• Source of material for this class will be lectures and assigned papers.


Requirements for the term paper • Goals

– Analytical thinking – Improved writing

• Select a topic of interest to you and then propose a whole genome approach to address the problem (not necessarily your 199 research!) – Talk with me about your topic (so that I can help you focus it on

something do-able and rewarding to you)

• Write a short paper (5 pages) in the style of a pre-doctoral fellowship proposal describing how you will attack this problem (examples posted). – Specific aims (~1/2 page)

• Hypotheses to be tested • How will you test hypotheses?

– Background and significance (1-2 pages) • What is known, what remains to be learned • why should someone give you money to study this problem?

– Research plan (~3 pages) • specific experiments to answer the questions posed in specific aims • How will you handle expected vs. unexpected results


Requirements for the term paper (contd)

• Outline (due Friday February 7 (24:00) – Title and topic – Introductory paragraph telling why the problem is important – What is the hypothesis that your proposed research will address? – Enumerate 1-3 specific aims in the form of questions that will test

aspects of your hypothesis • Topic can be changed later, if necessary

• What is a hypothesis?

• What is a theory ?

– A supposition or conjecture put forth to account for known facts; esp. in the sciences, a provisional supposition from which to draw conclusions that shall be in accordance with known facts, and which serves as a starting-point for further investigation by which it may be proved or disproved and the true theory arrived at.

– An analytical framework that explains a set of observations – A comprehensive explanation of an important feature of nature that is

supported by facts that have been repeatedly confirmed through observation and experiment


Requirements for the oral presentation

• Goal – again to get you to think more analytically – Exposure to literature (classic and current) – Learn critical reading – Discuss practical applications of what we are learning

• Powerpoint (“journal club”) presentation – as a presenter

– 15-20 minutes with time allowed for discussion (max of 15 – 20 slides) – Frame the problem – what are the big picture questions?

• What was known before they started? What was unknown? • Present background (not more than 5 slides)

– What are specific questions asked or hypotheses tested • Discuss figures

– What is the question being asked in each figure or panel? – What experiments did the authors do to answer questions? – Do the data support the conclusions drawn?

• What did they conclude overall? • What could have been improved?

– Point out a few papers for further reading (reviews, follow-ups, etc) – Summarize main points and key techniques used in the last slide


Requirements for the oral presentation (contd)

• Powerpoint presentation – as a listener – READ THE PAPERS – you are responsible for the material covered – Study the figures

• What points don’t you understand? – Make notations, ask the speaker to clarify these

– Listen to the speaker • If presentation is unclear, ask the speaker to elaborate • Always feel free to ask questions – we want an open discussion

• Papers are posted on the web sites listed

• Logistics

– Prepare presentation in Powerpoint or PDF (not Keynote) and either e-mail to me or bring it on a USB stick.


Presentation schedule

• Week 1 papers – Dear and Cook, 1993, Jiang et al, 2011 (Angela) • Week 2 papers – (1) Geisler et al., 1999 (2) Redon et al., 2006 (3) Venter et al., 2004 • Week 3 papers – (4) Bentley et al., 2008 (5) Lindblad-Toh et al., 2011 (6) Sessions et al., 2016 • Week 4 papers – (7) Kapranov et al., 2007 (8) Morrison et al., 2017 (9) Owens et al., 2016 • Week 5 papers - (10) Cheng et al., 2019 (11) Chen et al., 2012 (12) Silvert et al., 2019 • Week 6 papers –Midterm, no presentations • Week 7 papers – (13) Flyamer et al., 2017 (14) Buenrostro et al., 2012 (15) Argelaguet et al., 2019 • Week 8 papers – (16) Gilbert et al., 2014 (17) Anzalone et al., 2019 (18) Luo et al., 2009 • Week 9 papers – (19) Ito et al., 2001 (20) Dejardin and Kingston, 2009 (21) Gavin et al., 2002 • Week 10 papers - (22) David et al., 2014 (23) Rampelli et al., 2015 (24) Tang et al., 2019


Lecture Outline – Organization and Structure of Genomes

• Today’s topics – Genome complexity – Implications of split genes for protein diversity – Repetitive elements and gene evolution

• The big picture for the next 2 lectures

– How are genomes similar and different? – How do we find out this information? – Why do we care?

• What is genomics? Proteomics?

– ‘omics is the study of a property using “whole genome” approach – Genomics – study of genes and gene function – Proteomics – study of all the proteins

BioSci D145 lecture 1 page 13 ©copyright Bruce Blumberg 2020 All rights reserved

The rise of -omics

• The -omics revolution of science – http://www.genomicglossaries.com/content/omes.asp

• What does it all mean?

– Transcriptomics – – Proteomics – – Functional genomics – – Structural genomics – – Pharmacogenomics – – Toxicogenomics –

– Metabolomics –

– Interactomics –

– Bibliomics –

large scale profiling of gene expression study of complement of expressed proteins

vague term, typically encompasses many others prediction of structure and interactions from sequence (Rick Lathrop, Pierre Baldi)

transcriptional profiling of response to drug treatment – often looking for genetic basis of differences

transcriptional profiling of response to toxicants (often includes pharmacogenomics

• Seeks mechanistic understanding of toxic response analysis of total metabolite pool ("metabolome") to

reveal novel aspects of cellular metabolism and global regulation genome wide study of macromolecular interactions, physical and genetic are included

identifying words that occur together in papers Sadly, usually just abstracts

http://www.genomicglossaries.com/content/omes.asp


Organization and Structure of Genomes (contd)

• Genome size – i.e. total number of DNA bp – Varies widely - WHY?

– i.e., what is the source of the differences?

• Do the number of genes required vary so much?

C- paradox

— (how many “phyla” are represented at the right?)

unlikely

Phylum Chordata

Phylum Arthropoda

Mixed bag



• How to measure genome complexity? – Hybridization kinetics – Shear and melt DNA – Allow to hybridize and measure double-

stranded vs. single-stranded by spectrophotometry

• Cot½ - measures genome size and complexity – What does a large value (longer to

hybridize) mean? • k is smaller (rate constant slower) • Longer to hybridize – more unique

sequences, larger genome – Much of what we knew about genome

size and complexity (until advent of genome sequencing) comes from these studies



• Assumptions – Cot½ measures rate of association

of sequences – Simple curves at right suggest

simple composition • No repetitive sequences

• What would a more complex genome

look like? – Would it be just shifted further

to the right?

– Or ?



• Measure eukaryotic DNA – Multiple components – Can calculate more than

1 Cot½ value – Either means starting

material is not pure (i.e., multiple types of DNA)

– Or means different frequency classes of DNA

• Highly repetitive • Moderately repetitive • Unique

– Very big surprise



• What can we conclude from great variation in genome size ?

Genetic complexity is not directly proportional to genome size!

• Increase in C is not always accompanied by proportional increase in number of genes — Gene number is controversial — Depends on what is a “gene” — Are we no more complicated than a weed (Arabidopsis) ?



• What can we learn by hybridizing RNA back to the genomic DNA? – Label RNA and hybridize with

excess DNA – measure formation of hybrids over time

– Rot½ analysis shows that RNA does not hybridize with highly repetitive DNA

– What does this mean?

• Most of mRNA is transcribed from non-repetitive DNA

• Moderately repetitive DNA is transcribed

• Much of highly repetitive DNA is probably not transcribed into mRNA – Key argument why genome

sequencers do not bother with “difficult” regions of repetitive DNA


Organization and Structure of Genomes (contd) stopped here

• Gene content is proportional to single copy DNA – Amount of non-repetitive DNA has a

maximum,total genome size does not – What is all the extra DNA, i.e., what is it

good for?

• Repetitive DNA • Telomeres • Centromeres • Transposons • Junk of all sorts

• DNA replication is very accurate • Selective advantage?

• OR

– Where did all this junk come from and why is it still around?



• What is this highly repetitive DNA? • Selfish DNA?

– Parasitic sequences that exist solely to replicate themselves?

• Or evolutionary relics? – Produced by recombination,

duplication, unequal crossing over

• Probably both – Transposons exemplify “selfish DNA”

• Akin to viruses? – Crossing over and other recombination lead

to large scale duplications

• ENCODE (encyclopedia of DNA elements) considers > 80% of genome to be functional.

− But see Grauer et al, 2013. Genome Biol Evol 5:578-590. On the immortality of television sets: “function” in the human genome according to the evolution-free gospel of ENCODE


Transcription of Prokaryotic vs Eukaryotic genomes (stopped here)

• Prokaryotic genes are expressed in linear order on chromosome – mRNA corresponds directly

to gDNA

• Most eukaryotic genes are interrupted by non-coding sequences – Introns (Gilbert 1978) – These are spliced out after

transcription and prior to transport out of nucleus

– Post-transcriptional processing in an important feature of eukaryotic gene regulation

• Why do eukaryotes have introns, i.e., what are they good for? • Main function may be to generate protein diversity • Harbor regulatory sequences


Introns and splicing

• Alternative splicing can generate protein diversity – Many forms of alternative splicing seen – Some genes have numerous alternatively spliced forms

• Dozens are not uncommon, e.g., cytochrome P450s


Introns and splicing

• Alternative splicing can generate protein diversity (contd) – Others show sexual dimorphisms

• Sex-determining genes • Classic chicken/egg paradox

– how do you determine sex if sex determines which splicing occurs and spliced form determines sex?


Origins of intron/exon organization

• Introns and exons tend to be short but can vary considerably – “Higher” organisms tend to have longer lengths in both – First introns tend to be much larger

than others – WHY?

• Often contain regulatory elements – Enhancers – Alternative promoters – etc



• Exon number tends to increase with increasing organismal complexity – Possible reasons?

• Longer time to accumulate introns? • Genomes are more recombinogenic due to repeated sequences? • Selection for increased protein complexity

– Gene number does not correlate with complexity – therefore, it must come from somewhere



• When did introns arise – Introns early – Walter Gilbert

• There from the beginning, lost in bacteria and many simpler organisms

– Introns late – Cavalier-Smith, Ford Doolittle, Russell Doolittle • Introns acquired over time as a result of transposable elements,

aberrant splicing, etc • If introns benefit protein evolution – why would they be lost?

– Which is it?

• Introns “late” (at the moment)

• But late = ~580 million years ago

• What is common factor among animals that share intron locations?

All deuterostomes (echinoderms, chordates, hemichordates, xenoturbellids – diverged about 580 x 106 years ago

Actin


Evolution of gene clusters • Many genes occur as multigene families (e.g., actin, tubulin, globins, Hox)

– Inference is that they evolved from a common ancestor – Families can be

• clustered - nearby on chromosomes (α-globins, HoxA) • Dispersed – on various chromosomes (actin, tubulin) • Both – related clusters on different chromosomes (α,β-globins,

HoxA,B,C,D) – Members of clusters may show stage or

tissue-specific expression • Implies means for coregulation as well

as individual regulation • Much recent evidence showing that gene

clusters occur on loops of chromatin that are bounded by particular structural protein (cohesins, CTCF)


Evolution of gene clusters (contd)

• multigene families (contd) – Gene number tends to increase with

evolutionary complexity • Globin genes increase in number from

primitive fish to humans – Clusters evolve by duplication and divergence


Evolution of gene clusters (contd)

• History of gene families can be traced by comparing sequences – Molecular clock model holds that rate of change within a group is

relatively constant • Not totally accurate – check rat genome sequence paper

– Distance between related sequences combined with clock leads to inference about when duplication took place


Types and origin of repetitive elements

• DNA sequences are not random – genes, restriction sites, methylation sites

• Repeated sequences are not random either – Some occur as tandemly repeated sequences – Usually generated by unequal crossing

over during meiosis – These resolve in ultracentrifuge into

satellite bands because GC content differs from majority of DNA

– This “satellite” DNA is highly variable • Between species • And among individuals within a

population • Can be useful for mapping

genotyping, etc – Much highly repetitive DNA is in

heterochromatin (highly condensed regions) • Centromeres are one such place


Types and origin of repetitive elements (contd)

• Dispersed tandem repeats are “minisatellites” 14-500 bp in length – First forensic DNA typing used satellite DNA – Sir Alec Jeffreys – Minisatellite DNA is highly variable and perfect for fingerprinting


Types and origin of repetitive elements – dispersed repeated sequences


Types and origin of repetitive elements – dispersed repeated sequences

• Main point is to understand how such elements can affect evolution of genes and genomes – Gene transduction has long been known in bacteria (transposons, P1, etc) – LINE (long interspersed nuclear elements)

can mediate movement of exons between genes

• Pick up exons due to weak poly- adenylation signals

• The new exon becomes part of LINE by reverse transcription and is inserted into a new gene along with LINE

– Voila – gene has a new exon – Experiments in cell culture proved this

model and suggested it is unexpectedly efficient

– Likely to be a very important mechanism for generating new genes

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