1. Lecture 4. Topics in Gene Regulation and Epigenomics (Basics)
The Chinese University of Hong Kong
CSCI5050 Bioinformatics and Computational Biology

2. Lecture outline Introduction to gene regulation and epigenetics
Experimental methods for epigenomics
Relevant problems in computational biology and bioinformatics
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

3. Introduction to Gene Regulation and Epigenetics
Part 1
Introduction to Gene Regulation and Epigenetics

4. Gene regulation
Here defined as the control of the amount and gene products
Amount:
Number of transcripts
Number of proteins
Products:
RNAs
Total
A particular transcript isoform
With a particular modification
Proteins
With a particular form (e.g., activated)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

5. Gene “expression”
Gene expression is a general term used to indicate the production of gene products
More specific terms:
Transcription rate (number of new transcripts per time)
Transcript level (total number of transcripts in the cell)
Translation rate
Protein level
All these are correlated but not identical, sometimes with only weak correlations
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

6. Gene regulation Expression of genes needs to be tightly regulated
Differentiation into different cell types
Response to environmental conditions
How are genes regulated?
Transcriptional
Post-transcriptional
Translational
Post-translational
Analogy: lighting controlling
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

7. A simple illustration G3 G1 P7 P1 G2 P3 P5 P6 G4 Me Me Ac
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

8. A simple illustration G3 G1 P7 P1 G2 P3 P5 P6 G4 Me Me Ac
miRNA-mRNA interactions
Protein-RNA interactions P7
Transcription factor binding
DNA methylation P1 Me G2
Protein-protein interactions and DNA long-range interactions
Histone modifications P3 Me Ac P5 P6
Chromatin accessibility G4
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

9. More details and other mechanisms
Transcriptional regulation
Transcription factors
Binding to promoter vs. distal elements (e.g., enhancers)
Activators vs. repressors
Post-transcriptional regulation
Capping
Poly-adenylation
Splicing
RNA editing
mRNA degradation
Translation
Translational repression
Post-translational
Protein modifications (e.g., phosphorylation)
Image source:
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

10. Epigenetics
Wikipedia: “the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence”
Heritable: Can pass on to offspring (daughter cells)
Same DNA, different outcomes
But how can these signals be inherited? Still based on DNA sequence (in some complex way) or not?
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

11. Heritability (D1, E1)  (D2, E2) D2 = f(D1)  D1 E2 = f(D1, E1)
D: DNA
E: Epigenetic signals
1, 2: proliferation, differentiation, fertilization
D2 = f(D1)  D1
E2 = f(D1, E1)
E2 = f(E1)?
E0 = f(D0)?
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

12. Active and inactive epigenetic signals
DNA methylation
Chromatin remodeling
Histone modifications
RNA transcripts
...
(And actually not that simple!)
Image credit: Zhou et al., Nature Reviews Genetics 12(1):7-18, (2011)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

13. DNA methylation
Methyl group (-CH3) added to cytosine in eukaryotic DNA, usually next to a guanine (in a CpG dinucleotide)
Less common, but could also be in CpHpG or CpHpH contexts (H = A, C or T)
Could be converted to other forms during TET-mediated active de-methylation
Image credit: Song et al., Nature Biotechnology 30(11): , (2012)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

14. DNA methylation
Hyper-methylation at CpG islands/promoters is associated with gene repression
Regulatory function of DNA methylation at other regions is not as clear
Recent studies have suggested links between DNA methylation and
Protein binding
Transcriptional elongation
Splicing
Histone modifications
Gene imprinting: parent-specific expression
Implications in diseases
De novo vs. maintenance
Image source:
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

15. Trajectory of DNA global methylation changes
Image credit: Saitou et al., Development 139:15-31, (2012)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

16. Chromatin remodeling Chromatin: compact structure of DNA and proteins
DNA wraps around histone proteins to form nucleosomes (~146bp of DNA around each histone octamer)
Nuelceosome positioning can be changed dynamically, affecting DNA accessibility (e.g., to binding proteins)
Image credit: wikipedia
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

17. Histone modifications
Modification of specific residues on histone proteins
Acytelation, methylation, phosphorylation, ubiquination, etc.
Nomenclature: H3K4me3 (Histone protein H3, lysine 4, tri-methylation)
Image credit: Kato et al., IBMS BonKEy 7: , (2010)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

18. Histone modifications and regulation
Histone modifications give different types of signals in gene regulation (again, a simplified view):
Image credit: Zhou et al., Nature Reviews Genetics 12(1):7-18, (2011)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

19. Non-coding RNA
There are different types of functional RNA that do not translate into proteins
Type
Abbreviation
Function
Ribosomal RNA
rRNA
Translation
Transfer RNA
tRNA
Small nuclear RNA
snRNA
Splicing
Small nucleolar RNA
snoRNA
Nucleotide modifications
MicroRNA
miRNA
Gene regulation
Small interfering RNA
siRNA …
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

20. MicroRNA
Short (~22 nucleotides) RNAs that regulate gene expression by promoting mRNA degradation or repressing translation
Image credit: wikipedia, Sun et al., Annual Review of Biomedical Engineering 12:1-27, (2010)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

21. Gene regulation and epigenetics
Some mechanisms are known to regulate gene expression. For example:
Transcription factor binding can activate or repress transcription
miRNA-mRNA binding can promote mRNA cleavage or repress translation
Some signals are correlated with expression, but the causal direction is not certain (or not fixed). For example:
Promoter DNA methylation and transcriptional repression
Histone modifications and gene activation/repression
The different mechanisms are not independent.
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

22. Experimental Methods for Epigenomics
Part 2
Experimental Methods for Epigenomics

23. From epigenetics to epigenomics
Not focusing on a single gene, but the whole genome
Measuring signals genome-wide
Studying general (statistical) phenomena / (biological) mechanisms
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

24. High-throughput methods (recap)
Protein-DNA binding (ChIP-seq, ChIP-exo, ...)
DNA long-range interactions (ChIA-PET, Hi-C, TCC, ...)
DNA methylation (bisulfite sequencing, RRBS, MeDIP-seq, MBDCap-seq, ...)
Open chromatin (DNase-seq, FAIRE-seq, ATAC-seq, ...)
Histone modifications (ChIP-seq)
Gene expression (RNA-seq, CAGE, ...), isoforms
Protein-RNA binding (CLIP-Seq, HITS-CLIP, PAR-CLIP, RIP-seq, ...)
...
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

25. ChIP-seq Chromatin immunoprecipitation followed by sequencing
Use antibody to “pull down” target DNA, such as DNA bound by a certain protein or with a certain chemical modification
Image credit: Mardis, Nature Methods 4: , (2007)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

26. ChIP-exo Higher data resolution by adding a digestion step
Image credit: Wikipedia, Rhee et al., Cell 147(6): , (2011)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

27. Open chromatin
ATAC-seq winning out due to its lower amount of cells required
As low as 50,000 cells
Up to 1,000 folds lower than MNase-seq or DNase-seq
Image credit: Meyer and Liu., Nature Reviews Genetics 15(11): , (2014)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

28. DNA methylation BS and oxBS DNA-seq BS oxBS C T 5mC 5hmC/5fC
Image credit: Booth et al., Nature Protocols 8(10): , (2013)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

29. Problems in Computational Biology and Bioinformatics
Part 3
Problems in Computational Biology and Bioinformatics

30. Some related CBB Problems
Analysis of chromatin patterns
Identification of regulatory elements [lecture]
Reconstruction of transcription factor (TF) regulatory networks
Identification of non-coding RNAs
Prediction of miRNA targets
Construction of gene expression models
Inferring epigenetic signals
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

31. Analysis of chromatin patterns
Computational tasks:
Segmentation of the human genome
Single bases/fix-sized bins or based on annotation
Unsupervised clustering or supervised classification
Data aggregation and integration
Large-scale correlations
Learning of signal shapes
Visualization
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

32. Genome segmentation Using chromatin state to segment the genome
Hidden Markov model
Clustering
Annotate identified states using biological knowledge
Image credit: Ernst and Kellis, Nature Methods 9(3): , (2012)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

33. Global chromatin patterns
Many recent findings that relate chromatin patterns with other features
Global example: histone modifications, recombination rates and chromosome 1D and 3D structures in C. elegans
Image credit: Gerstein et al., Science 330(6012): , (2010)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

34. Local chromatin patterns
Histone modifications and protein binding at promoters and enhancers in human
Image credit: Heintzman et al., Nature Genetics 39(3): , (2007)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

35. Identifying regulatory elements
There are different types of protein-binding regions in the DNA
Promoters
Enhancers
Silencers
Insulators
...
How to locate them in the genome?
Image credit: Raab and Kamakaka, Nature Reviews Genetics 11(6): , (2010)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

36. Identifying regulatory elements
Some useful information:
Genomic location
E.g., promoters are around transcription start sites
Evolutionary conservation
Functional regions are more conserved
Protein binding signals and motifs
E.g., EP300 at enhancers, CTCF at insulators
Chromatin features
E.g., DNase I hypersensitivity, H3K4me1 and H3k27ac at active enhancers
Reporter assays
...
Difficulty: integrating different types of information
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

37. Reconstruction of TF network
Goals:
Identifying TF binding sites
Determining the target genes of each TF
In different cell types
In different conditions
Deducing how gene expression is regulated by TFs
Studying how TFs interact with each other
Methods:
From expression data
Sequence-based (motif analysis)
From binding experiments
Sign of regulation (activation vs. repression) usually not determined for #2 and #3
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

38. Expression-based methods
Input: gene expression levels of genes
Usually from microarrays
Often time series data
Output: a network (i.e., directed graph)
Each node is a gene (and its protein product)
An AB edge means A is a TF and it regulates B
Types:
Differential equations
Probabilistic networks
Boolean networks
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

39. Expression-based methods
Differential equations
Models (yj: expression level of gene j, aji: influence of TF i on gene j):
Linear
Sigmoidal
...
Methods:
Solve system of equations to get best-fit parameter values
Difficulties:
Many parameters when there are many TFs
Insufficient training data
L1 (LASSO) regularization to control the number of non-zero variables
Long running time
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

40. Boolean networks Considering each gene to be either on or off
Treat the gene regulatory network as a Boolean network (similar to a electric circuit)
Expression of a gene at time t+1 depends on the expression of genes that regulate it at time t
Goal: Find the logical relationships between genes
Image credit: Akutsu and Miyano, Pacific Symposium on Biocomputing 4:17-28, (1999)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

41. From binding data
Input: binding signals of transcription factors in the whole genome
Usually from ChIP-chip or ChIP-seq
Or from motifs
(Best to combine both)
Output: TF regulatory network
Difficulties:
Finding binding sites
Peak calling
Motif analysis
Associating binding sites with target genes
Promoters (e.g., 500bp upstream of transcription start site)
More difficult for distal binding sites
Expression patterns could help
Evaluating functional effects of binding (strong vs. weak, transient binding)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

42. Combining both types of data
Use expression data to infer initial network
Identify potential regulators
Search for binding motifs of these regulators
Incorporate global occurrence of these motifs at gene promoters to refine the network
Image credit: Tamada et al., Bioinformatics 19(Suppl.2):ii227-ii236, (2003)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

43. Identification of non-coding RNAs
It has recently been shown that a vast amount of DNA is transcribed into RNA by high-throughput experiments
What are they?
Experimental artifacts?
Unannotated protein-coding genes?
Non-functional transcripts?
Functional non-coding RNAs?
Pseudogenes?
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

44. Prediction of miRNA targets
Table credit: Peterson et al., Frontiers in Genetics 5:23, (2014)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

45. Seed match Table source: TargetScan Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

46. Construction of expression models
Given the many different mechanisms involved in gene regulation, how are they related to each other?
Are they redundant?
Do they simply add to each other, or have synergistic effects?
Which have more impacts to final expression levels?
What are their time scales?
When is each mechanism used?
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

47. Construction of expression models
Modeling and prediction
An indirect way to estimate how well a model is: evaluating the accuracy of predicted expression
Prediction of:
Expression level
Regression: yi  f(xi)
Classification: (yi > t)  f(xi)
Differential expression
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

48. Construction of expression models
Chromatin features and expression
Image credit: Cheng et al., Genome Biology 12(2):R15, (2011)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

49. Construction of expression models
Model construction and accuracy
Image credit: Cheng et al., Genome Biology 12(2):R15, (2011)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

50. “Histone code” hypothesis
The statistical models are good, but too complex for humans to interpret
Is there a simple set of rules (i.e., a “code”) that can easily tell the expression level of a gene?
Image credit: Cheng et al., Genome Biology 12(2):R15, (2011)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

51. Inferring epigenetic signals
Can we infer epigenetic signals (e.g., open chromatin, DNA methylation or histone modifications) from DNA sequence alone?
For a specific cell type
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

52. Deep learning of functional activity
Image credit: Kelley et al., Genome Research 26(7): , (2016)
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018

53. Summary
“Gene expression” is a general term with several possible meanings
Gene expression is regulated by many mechanisms, including (but not limited to)
Transcription factor binding
DNA long-range interactions
DNA methylation
Chromatin structure
Histone modifications
MicroRNA-mRNA binding
A lot of new genome-wide data
Many emerging research topics in CBB
Last update: 30-Jan-2018
CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Spring 2018