Genomics of microRNA genes and their target mRNAs

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Genomics of microRNA genes and their target mRNAs

• Jan. 23, 2006

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Readings

• Bentwich (review for background)
• Smalheiser and Torvik (experimental, for
  detailed analysis)
• Hill et al. (points to the future)

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Where are miR genes?

• Intergenic tendency to cluster
• Primary transcripts are polycistronic
• Promoter, (splicing), 5-cap, poly A tail
• Intronic most are in sense orientation
• may also be in clusters
• At least some are pol II
• No promoter needed?
• Relation to genes they regulate? Chimeric
  transcripts, antisense orientation, origin from
  pseudogenes or inverted duplication of target
  mRNAs
• miRs in viral genomes too

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Rules for predicting hairpins as being miR
precursors

• Stem-loop, stem is over 23 bases
• LOTS of hairpins in the genome all over
• predicted loop often smaller than actual size
• cross species conservation and pattern of change
  over evolution (loop vs. arm vs. arm), drop of
  conservation just outside the hairpin
• nucleotide composition, melting temperature,
  symmetrical bubbles
• Clustering (better signal-to-noise ratio)
• validation using small RNA cloning

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Machine Learning

• Training Sets positive and negative examples
• Goal recognize patterns, summarize features
• Features to be encoded (may be redundant,
  nonlinear or non-independent)
• Algorithm for learning or deciding something
  about the testing set. How to weight each
  feature, how to combine features?
• Optimization criterion (when to stop analyzing??)
• Form clusters, set cut-off values, optimize
  multiple parameters into a single ranked value,
  predict groups
• Process is only as robust as the training sets!

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Where do miR genes come from originally?

• some come from inverted target duplications,
  genomic repeats, pseudogenes these give
  automatic targets too in some cases
• Very large pool of candidate hairpins!!
• could evolve by change in hairpin sequences that
  allow Drosha or Dicer to cut it
• or could acquire promoter to transcribe the
  hairpin

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Computational Prediction of miR targets (in
Animals)

• Machine learning with positive and negative
  examples
• Prediction confidence vs. biological reality
• Improve signal to noise ratio
• cross species conservation,
• Binding energy of miR to target sequence
• Multiple hits to same target
• Multiple hits from different miRs on same target
• Restrict attention to 3-UTR
• 5- seeds, 6-8 with or without GU matches
  allowed
• Mismatch between miR and target in central region
• Validation by cloning, expression, genetics

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What is the purpose of miR-target interaction?

• Turn OFF genes that should not be expressed at a
  given time or cell type?
• Lim paper and other recent papers showing
  OPPOSITE pattern of expression of miR and target
  mRNAs
• Or the opposite, modulate levels up and down
  dynamically?
• Examples in plants
• miR-214 hits all five synaptic targets.
• Mir-134 in brain, regulates a brain target LIMK1
• combinatorial miRs targets with
  CO-expression..

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miRs are constrained by targets and vice versa

• miRs often NOT co-expressed with target mRNAs (in
  time or tissue type)
• housekeeping genes have short 3-UTRs to avoid
  regulation by miRs (anti-targets)
• so, miRs constrain target sequences
• conversely, targets constrain miR seed sequences
  too!!!
• (many different miRs that are otherwise unrelated
  share the same seed sequence)

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Evolution of miR genes

• duplication and divergence
• keep one miR conserved (paired with target mRNA)
• but the others may drift in terms of the pattern
  of their expression
• or the sequence of their target mRNAs

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Our Study of Human microRNA targetsBMC
Bioinformatics 2004

• No training set, no heuristics required.
• Compare the characteristics of the ENTIRE SET of
  miRs vs. scrambled counterparts against ENTIRE
  human RefSeq mRNAs, look for statistical outliers
• Identify outlying tails for exact hit length,
  gapped-BLAST score, proximity of hits from
  distinct microRNAs on same target

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microRNA sequences (nonredundant set)
Scrambled
Number of hits of exact hit length x or greater
0.2
Exact hit length, x
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microRNA sequences (nonredundant set)
Scrambled
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Number of microRNA sequences
15
10
5
0
2
4
6
8
10
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Number of distinct targets hit
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Take-Home Lessons for Human Long Seed Targets

• Longer exact hit length, better overall match,
  closer multiple hits all predict true targets
  (unlikely to arise by chance)
• gt10 bases up to perfect and near-perfect
  complementarity even in mammals
• NO preference for 3-UTR on target
• NO preference for 5 end of miR to be a perfect
  6 base match

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perfect microRNA-target interactions

• A few examples of microRNAs exhibiting perfect
  complementarity have been described (miR-196,
  miR-127 and miR-136.
• During our analysis of long-seed interactions we
  were struck by the existence of targets that had
  perfect complementarity to microRNAs along their
  full length.
• Whereas long-seeds are defined as having exact
  Watson-Crick base pairing with no GU matches,
  recent data suggest that complementarity
  interactions that contain up to, say, 5-7 GU
  matches (but no frank mismatches) could still be
  deemed perfect in gene silencing.
• In particular, we noted that human miR-95 was
  perfectly complementary (including up to 4 GU
  matches) with scores of human mRNAs and ESTs
  (fig. 6). Similarly, miR-151 was perfectly
  complementary to 6 transcripts, which was
  significantly above the level expected by chance.

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SW perc perc perc query position in
query matching repeat position in
repeat score div. del. ins. sequence
begin end (left) repeat class/family
begin end (left) ID 203 10.0 0.0 0.0
hsa-mir-151 2 31 (59) C L2
LINE/L2 (94) 3178 3149 1 193
28.0 8.0 0.0 hsa-mir-151 4 78 (12)
L2 LINE/L2 3102 3182 (90)
2 202 17.6 0.0 0.0 hsa-mir-28
2 35 (51) C L2 LINE/L2 (93)
3179 3146 3 236 18.2 0.0 0.0
hsa-mir-28 43 86 (0) L2
LINE/L2 3137 3180 (92) 3 392
2.2 0.0 0.0 hsa-mir-321 2 47 (12)
tRNA-Arg-AGG tRNA 28 73 (3) 4
194 22.5 0.0 0.0 hsa-mir-325 3
42 (56) L2 LINE/L2 3220
3259 (13) 5 219 23.4 0.0 0.0
hsa-mir-325 52 98 (0) C L2
LINE/L2 (18) 3295 3249 6 252
10.5 0.0 0.0 hsa-mir-340 9 46 (49)
C MARNA DNA/Mariner (346) 240 203
7 206 15.2 0.0 0.0 hsa-mir-95
2 34 (47) L2 LINE/L2
3273 3305 (8) 8 201 17.1 0.0 0.0
hsa-mir-95 47 81 (0) C L2
LINE/L2 (7) 3306 3272 8 195
7.1 0.0 0.0 mmu-mir-151 1 28 (40) C
L2 LINE/L2 (96) 3176 3149 1
193 20.6 0.0 0.0 mmu-mir-28 2
35 (51) C L2 LINE/L2 (93)
3179 3146 2 214 20.4 0.0 0.0
mmu-mir-28 43 86 (0) L2
LINE/L2 3137 3180 (92) 2 304
21.9 2.7 0.0 mmu-mir-297-1 1 73 (3)
RMER12 LTR/ERVK 774 848 (474)
3 185 22.8 0.0 3.4 mmu-mir-297-2
3 61 (3) (TATATG)n Simple_repeat 2
58 (0) 4 392 2.2 0.0 0.0
mmu-mir-321 2 47 (12) tRNA-Arg-AGG
tRNA 28 73 (3) 5 202
20.0 0.0 0.0 mmu-mir-325 3 42 (56)
L2 LINE/L2 3220 3259 (13)
6 240 21.3 0.0 0.0 mmu-mir-325
52 98 (0) C L2 LINE/L2
(18) 3254 3208 6 252 10.5 0.0 0.0
mmu-mir-340 12 49 (49) C MARNA
DNA/Mariner (346) 240 203 7 402
15.1 0.0 0.0 mmu-mir-341 12 84 (12)
(CGGT)n Simple_repeat 3 75 (0)
8 203 10.0 0.0 0.0 rno-mir-151
7 36 (61) C L2 LINE/L2
(94) 3178 3149 1 194 10.3 2.4 4.9
rno-mir-151 38 78 (19) L2
LINE/L2 3180 3219 (94) 2 236
19.2 0.0 0.0 rno-mir-28 35 86
(0) L2 LINE/L2 3137 3221
(92) 3 486 8.8 0.0 0.0 rno-mir-297
1 68 (0) (TATG)n
Simple_repeat 1 68 (0) 4 392
2.2 0.0 0.0 rno-mir-321 2 47 (12)
tRNA-Arg-AGG tRNA 28 73 (3) 5
202 20.0 0.0 0.0 rno-mir-325 3
42 (56) L2 LINE/L2 3220
3259 (13) 6 240 21.3 0.0 0.0
rno-mir-325 52 98 (0) C L2
LINE/L2 (18) 3254 3208 6 451
23.1 2.2 0.0 rno-mir-327 4 94 (0)
C RodERV21 LTR/ERV1 (2811) 2180 2088
7 476 5.0 0.0 0.0 rno-mir-333
36 95 (0) B2_Rat1 SINE/B2
2 61 (127) 8 234 13.2 0.0 0.0
rno-mir-340 12 49 (49) C MARNA
DNA/Mariner (346) 240 203 9 402
15.1 0.0 0.0 rno-mir-341 12 84 (12)
(CGGT)n Simple_repeat 3
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lt--22 mir-151 lt--1
lt--22
mir-95 lt--1 atgatctgacactcgaggagct

acgagttatttatgggcaactt


lt--22 mir-28 lt--1

1--gt mir-325 21--gt gagttatctgacactcgag
gaa
cctagtaggtgtccagtaagt


tccactagaatgtaagctccatgagggcagggactttgtctgttttgt
tcactgctgtatccccagcgcctagmacagtgcctggcacatagtaggc
3188
--L2A consensus--gt
3305/3314


cccactggactgtgagctccgcgagggcagggac
tgtgtctgtcttgttcaccactgtatccccagcgcctagcacagtgcctg
gcacatagcaggcgctcagtaaatgtttgttgaa 294
--L2B
consensus--gt
411/419
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miR-151 perfect hits
NM_014400.1 - Homo sapiens GPI-anchored
metastasis-associated protein homolog (C4.4A),
mRNA mir-151 22 TACTAGACTGTGAGCTCCTCGA 1 hit
C4.4A 1584
TACTAGACTGTGAGCTCCTCGA 1605/1698
3'UTR Repeatmasker predicts a L2 element
within the mRNA at position 1575-1673. 2)
NM_005373.1 - Homo sapiens myeloproliferative
leukemia virus oncogene (MPL), mRNA mir-151
22 TACTAGACTGTGAGCTCCTCGA 1 hit
?? MPL 5' 3526
TACTAGATTGTGAGCTCCTTGA 3547/3646
3'UTR Repeatmasker predicts a L2 element
within the mRNA at position 3504-3646.
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miR-28 showed an excellent hit upon transcription
factor E2F6
miR 3' GAGT--T-ATCTGACACTCGAGGAA 5' w/GU
matches BLAST
Target 5' 2207 TTTACCATTAGACTGTGAGCTCCTT
2231/2342
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LINE-2 derived miRs

• The LINE-2 repeat derived microRNAs appear to
  recognize transcripts that share repeats in their
  3-UTR regions.
• when they bind with perfect or near-perfect
  complementarity, they may be expected to lead to
  degradation of the transcripts.
• This could serve as a mechanism for detecting and
  neutralizing aberrant transcripts (having
  readthrough transcription from retained introns
  or neighboring genomic regions)
• as well as serving to regulate specific mRNAs.

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Take-Home Lessons

• miRs may not all derive from the same genomics,
  may not all interact with their targets via the
  same rules.
• Transposable elements have contributed to both
  miR genes and targets.
• Helps explain why targets have generic tags, why
  many targets are in 3-UTR region, why miRs are
  not all conserved.
• Alu repeats as a putative miR target!

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CAGCACUUUGGGAG 14-mer from Alu consensus
position 32-45 a) sequence length
microRNA CAGCACUUU 9 93, 372, 106b
AGCACUUUG 9 17-5p, 20b, 520g,h
CAGCACUU 8 512-3p AGCACUUU
8 520a,b,c,d,e, 526b GCACUUUG 8 519d
AGCACUU 7 302a,b,c,d, 373
UUGGGAG 7 150 b)
AGCACUUU 8 106a, 20a AGCACUU 7 520f
GCACUUU 7 519a,b,c,e
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Hill et al. do introns have miR-like effects?

• Transfected isolated introns into HeLa cells
  (incorporated into genome)
• Lots of genes went up and down, some pattern to
  the effects
• Reminiscent of Lim et al. with miRs, but.
• Did not show if drosha/dicer is involved..
• Did not show if there is a sequence
  complementarity involved with targets.
• Did not show if introns are processed to small
  RNAs
• Much less whether 22 nt. in length.
• But, introns could be superset of small RNA
  world?!