Synonymous mutations - from

bacterial evolution to somatic changes in

human cancer

Fran Supek

1) Lehner group, CRG/EMBL Systems Biology Unit, Barcelona2) Division of Electronics, RBI, Zagreb, Croatia

XXI Jornades de Biologia MolecularBarcelona, 11.6.2014

Part 1: Inferring microbial gene function from evolution of codon biases.

synonymous mutations =changes in the gene sequencethat don’t alter the protein sequence

Synonymous mutations

• (some) synonymous mutations are subject to evolutionary pressures• clearly shown for many bacteria and yeasts• likely also higher Eukarya (but weaker signal)

• how does selection for/against synonymous changes relate to gene function in (a) evolution of bacteria and (b) in carcinogenesis?

evolutionary trace across ~1000 bacterial genomes somatic mutations in ~4000 human cancersmalignant transformationadaptation to diverse environments

( plush microbes in photos are from http://www.giantmicrobes.com/ )

• In what way can evolution of synoymous codon preference be used to systematically infer gene function in bacteria?

• There are other simpler (known) ways to determine gene function from the genome sequences:

• commonly/systematically applied: transfer of annotation via sequence similarity (BLAST, COG, Pfam...)

• >30% of genes end up with no known function annotated. They may not have known homologs, or their homologs may have no experimentally determined function.

• known but less common: genomic context methods, such as phyletic profiling

evolutionary trace across ~1000 bacterial genomesadaptation to diverse environments

( plush microbes in photos are from http://www.giantmicrobes.com/ )

Phyletic (or phylogenetic) profiling

Pellegrini, Marcotte et al., PNAS (1999)

one genomic context method:

examines presence/absence patterns of homologous genes across species.

Kensche et al. (2008) J Royal Soc Interface. ~30 examples of success of phyletic profiling

• by 2008 -> n~=30

• by 2014 -> n~=300 (estimate)

• aim for: N > 3000

Enriching phyletic profileswith information on orthology and paralogy

orthologs in cliquesorth. outside cliquesparalogs

groups of orthologs from OMA database:Schneider, Dessimoz and Gonnet (2007) Bioinformatics

Skunca et al. PLoS Comp Biology 2013doi:10.1371/journal.pcbi.1002852

Accuracy of predicting GO categories strongly increases when adding paralogs

+ paralogs + orthologs(outside clique)

+ para + orthoclique only

(bubbles are Gene Ontology categories)

Supervised machine learning is superior to common approaches based on pairwise distances

Based on correlationof profiles

AUC

(are

a un

der

ROC

curv

e)

Decision trees

Schietgat et al. 2010. BMC Bioinfo

Experimental validation of predictions made with phyletic profiling

• knockout mutants of E. coli in predicted genes• three selected GO categories targeted by particular antibiotics:

• ‘response to DNA damage’• ‘translation’• ‘peptidoglycan-based cell wall biogenesis’

• predictions: 38 genes with expected precision > 60%

inhibitstranslationinitiation

inhibits cell wall synthesis

DNA damaging

agent

Does this gene participate in ‘peptidoglycan-based cell wall biogenesis’ ?

Does this gene participate in ‘peptidoglycan-based cell wall biogenesis’ ?

25/38 validated predictions (experimental precision = 66%; theoretically expected = 60%)Þ our method is useful for prioritizing genes for experimentally

determining gene function

http://gorbi.irb.hr/

“We predict Gene Ontology annotations ... for about 1.3 million poorly annotated genes in 998 prokaryotes at a stringent threshold of 90% Precision...”

“...about 19000 of those are highly specific functions.”

published in:Skunca et al. PLoS Comp Biology 2013doi:10.1371/journal.pcbi.1002852

• Codon usage biases are another useful source of evolutionary information

• ... complementary to gene presence/absence• ... available from just the genome sequence• ... with an established biological rationale

tRNA levels and codon usage biases

E. coli K-12, tRNA gene counts (proxy for tRNA levels)

codon

anticodon

Commonly used codons typically correspond to abundant tRNAs, particularly in highly expressed genes.

Codon biases correlate to gene expression

0.5

1

1.5

2

2.5

0.5 1 1.5 2 2.5 3 3.5

MIL

C (n

on-R

P ge

nes)

MILC (ribosomal protein genes)

ribosomal protein genes other highly expressed genes rest of genome

B

Figure fromSupek and Vlahoviček (2005) BMC Bioinformaticsdoi:10.1186/1471-2105-6-182

E. coli genome

• organisms adapt to the environment through changes in translation efficiency?

• Carbone A (2005) J Mol Evol – codon adaptation in metabolic pathways:

Photosynthesis genes in Synechocystis

Methanogenesis genes in Methanosarcina

Archaea

Bacteria

An example phenotype: oxygen requirement

• Man & Pilpel (2007) Nat Genet: 9 yeasts

TCA cycle glycolysis

aerobic anaerobic (low) codon adaptation (high)

• Based on these examples, we aimed to systematically link:• Many environments/phenotypes, with

• evolutionary change in translation efficiency across many gene families

Measuring translation efficiency

Method fromSupek et al. (2010)PLoS Geneticsdoi:10.1371/journal.pgen.1001004

non-HE HE

4-20% of genome

Expression levels: microarrayson 19 diverse bacteria

01234

log 2

expr

essio

n ra

tio

OCU/non-OCU, from ref. [7] HE/non-HE ribosomal proteins/all genes

gene 1

intergenicDNA

codonusage

all otherproteingenes

highly expressed

genes *

increasein

probability after adding

codon usage?

classifier predicts probability:

expr.

A

gene1

gene2

gene3

* ribosome, translationelongation factors, chaperones

vs.

B

C

3.9x6.0x

Correlation vs. causality?

a randomization test to control for confounding phenotypes and phylogeny

This passes the randomization test:

This fails (association not unique):

associations between phenotypes, and also with phylogeny:

• 514 aerotolerant vs. 214 aerointolerant:

295 COGs are significantly enrichedwith HE genes

• obligate vs. facultative aerobes:

• thermophiles

• halophiles

+ 20 other phenotypes tested

control for confounders 23 COGs

11 COGs

16 COGs

6 COGs

Gene families linked to aerotolerance

all experiments: Anita Kriško lab (Mediterranean Institute for Life Science, Split, Croatia)published as Kriško et al, Genome Biology 2014. doi:10.1186/gb-2014-15-3-r44

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** ** **

* known antioxidant proteins in E. coli (or homologs in other organisms)

* known to be regulated in response to air or oxidative stress

positive control

2 nonspeci-fic hits

ROS levels in the mutantsca

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wild-type

ROS are typically not increased (except cysD, yaaU, rseC, and the positive control sodA)

Predicted functional interactions from STRING v9

Gene families whose codon biases are associated to aerobicity/aerotolerance:

Putative mechanisms of oxidative stress resistance

NAD(P)Hrelated

iron-related

unknown

all experiments: Anita Kriško lab (Mediterranean Institute for Life Science, Split, Croatia)published as Kriško et al, Genome Biology 2014. doi:10.1186/gb-2014-15-3-r44

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2.5 mM H2O2 5 mM NAC pretreatment heat shock osmotic shock

A

Other phenotypes: thermophilicity, halophilicity

Knockout of candidate genes affects heat shock resistance and osmotic shock resistance.

Validation using synthetic genes with introduced suboptimal codons

0%

5%

10%

15%

20%

25%

30%

w.t. ΔclpS ΔclpS + clpS_w.t.

ΔclpS + clpS_15

ΔclpS + clpS_20

ΔclpS + clpS_25

% s

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codon distance (MILC) to ribosomal protein genes

ribosomal protein genesall other E. coli genes

w.t.

1520 25

w.t.

21 28 35

yjjB

clpS

0%

5%

10%

15%

20%

25%

30%

w.t. ΔyjjB ΔyjjB + yjjB_w.t.

ΔyjjB + yjjB_21

ΔyjjB + yjjB_28

ΔyjjB + yjjB_35

% s

urv

ival

osmotic shock

heat shockC

DB

A

all experiments: Anita Kriško lab (Mediterranean Institute for Life Science, Split, Croatia)published as Kriško et al, Genome Biology 2014. doi:10.1186/gb-2014-15-3-r44

Overall

• 200 links between 187 different COG gene families

- and -

24 diverse phenotypic traits, including• spore-forming ability• motility• pathogenicity to plants or mammals

• affecting certain tissues/organs

• (1000s more predictions at less stringent thresholds)

Anita Kriško lab – MediterraneanInstitute for Life Sciences (MedILS)Split, Croatia.

all experimentalwork shown

Nives Škunca ETH Zurich.

Phyletic profiling,

GORBI

Thank you!

Fran Supek

1) Lehner group, CRG/EMBL Systems Biology Unit, Barcelona2) Division of Electronics, RBI, Zagreb, Croatia

XXI Jornades de Biologia MolecularBarcelona, 11.6.2014

End of Part 1. Part 2 deals with causal synonymous mutations inhuman cancer genomes, and is available separately.