Inferring microbial gene function from evolution of synonymous codon usage biases
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Introduction: Thousands of microbial genomes are available, yet even for the model organisms, a sizable portion of the genes have unknown function. Phyletic profiling is a technique that can predict their function by comparing the presence/absence profiles of their homologs across genomes. In addition, prokaryotic genomes contain an evolutionary signature of gene expression levels in the codon usage biases, where highly expressed genes prefer the codons better adapted to the cellular tRNA pools. Objectives: We aimed to augment the existing phyletic profiling approaches by incorporating more detailed knowledge of gene evolutionary history, and create a very large database of predicted gene functions direcly usable for microbiologists. Materials & methods: We used the OMA groups of orthologs and the paralogy relationships inferred through OMA's „witness of non-orthology“ rule. Genes were assigned to Gene Ontology categories and the phyletic profiles compared using the CLUS classifier that performs a hierarchical multilabel classification using decision trees. We quantified significant codon biases using a Random Forest randomization test that compares against the composition of intergenic DNA. Codon biases in COG gene families were contrasted between microbes inhabiting different enviroments, while controlling for phylogenetic inertia. Results: The genomic co-occurence patterns of both the orthologs and the paralogs (the homologs separated by a speciation and by a duplication event, respectively) were informative and synergistic in a phylogenetic profiling setup, even though paralogy relationships are thought to conserve function less well. The resulting ~400,000 gene function predictions for 998 prokaryotes (at FDR<10%)> method to systematically link codon adaptation within COG gene families to microbial phenotypes and environments (thus functionally characterizing the COGs) and experimentally validated the predictions for novel E. coli genes relevant for surviving oxidative, thermal or osmotic stress. Conclusion: Our work towards ehnancing phylogenetic profiling, as well as developing complementary genomic context approaches, will contribute to prioritizing experimental investigation of microbial gene function, cutting time and cost needed for discovery.
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Inferring microbial gene function from evolution of synonymous codon usage biases
- 1. Synonymous mutations - from bacterial evolution to somatic changes in human cancer Fran Supek 1) Lehner group, CRG/EMBL Systems Biology Unit, Barcelona 2) Division of Electronics, RBI, Zagreb, Croatia XXI Jornades de Biologia Molecular Barcelona, 11.6.2014 Part 1: Inferring microbial gene function from evolution of codon biases.
- 2. synonymous mutations = changes in the gene sequence that don’t alter the protein sequence
- 3. 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 cancers malignant transformationadaptation to diverse environments ( plush microbes in photos are from http://www.giantmicrobes.com/ )
- 4. • 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 genomes adaptation to diverse environments ( plush microbes in photos are from http://www.giantmicrobes.com/ )
- 5. Phyletic (or phylogenetic) profiling Pellegrini, Marcotte et al., PNAS (1999) one genomic context method: examines presence/absence patterns of homologous genes across species.
- 6. 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
- 7. Enriching phyletic profiles with information on orthology and paralogy Species 1 Species 2 … Species 997 Species 998 Function OMA 1 … 0 GO:001, GO:007 OMA 2 0 … ? … … … … … … … OMA 64051 0 … 0 0 GO:042 OMA 64052 0 … GO:003, GO:160 orthologs in cliques orth. outside cliques paralogs groups of orthologs from OMA database: Schneider, Dessimoz and Gonnet (2007) Bioinformatics Skunca et al. PLoS Comp Biology 2013 doi:10.1371/journal.pcbi.1002852
- 8. Accuracy of predicting GO categories strongly increases when adding paralogs + paralogs + orthologs (outside clique) + para + orthoclique only (bubbles are Gene Ontology categories)
- 9. Supervised machine learning is superior to common approaches based on pairwise distances Based on correlation of profiles AUC(areaunder ROCcurve) Decision trees Schietgat et al. 2010. BMC Bioinfo
- 10. 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%
- 11. 0% 20% 40% 60% 80% 100% 120% 140% 160% w .t. dbpA rhlB yhbJ pm bA rhlE tldD yidD ynbB envC m urE nalidixic acidampicillinkasugamycin Survivalcomparedtothewildtype inhibits translation initiation inhibits cell wall synthesis DNA damaging agent
- 12. 0% 20% 40% 60% 80% 100% 120% 140% 160% w .t. dbpA rhlB yhbJ pm bA rhlE tldD yidD ynbB envC m urE nalidixic acidampicillinkasugamycin Survivalcomparedtothewildtype Does this gene participate in ‘peptidoglycan-based cell wall biogenesis’ ?
- 13. 0% 20% 40% 60% 80% 100% 120% 140% 160% w .t. dbpA rhlB yhbJ pm bA rhlE tldD yidD ynbB envC m urE nalidixic acidampicillinkasugamycin Survivalcomparedtothewildtype Does this gene participate in ‘peptidoglycan-based cell wall biogenesis’ ? 25/38 validated predictions (experimental precision = 66%; theoretically expected = 60%) our method is useful for prioritizing genes for experimentally determining gene function
- 15. “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 2013 doi:10.1371/journal.pcbi.1002852
- 16. • 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
- 17. 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.
- 18. 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 MILC(non-RPgenes) MILC (ribosomal protein genes) ribosomal protein genes other highly expressed genes rest of genome B Figure from Supek and Vlahoviček (2005) BMC Bioinformatics doi:10.1186/1471-2105-6-182 E. coli genome
- 19. •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
- 20. 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
- 21. Measuring translation efficiency Method from Supek et al. (2010) PLoS Genetics doi:10.1371/journal.pgen.1001004 non-HE HE 4-20% of genome Expression levels: microarrays on 19 diverse bacteria 0 1 2 3 4 log2expressionratio OCU/non-OCU, from ref. [7] HE/non-HE ribosomal proteins/all genes gene 1 intergenic DNA codon usage increase in expr. A gene1 B C 3.9x 6.0x
- 22. 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:
- 23. • 514 aerotolerant vs. 214 aerointolerant: 295 COGs are significantly enriched with HE genes • obligate vs. facultative aerobes: • thermophiles • halophiles + 20 other phenotypes tested control for confounders 23 COGs 11 COGs 16 COGs 6 COGs
- 24. 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 60% 80% 100% 120%C 60% 80% 100% 120% malizedtow.t. B 0x 1x 2x 3x 4x 5x 6x 0% 20% 40% 60% 80% 100% 120% NAC/noNACsurvivalratio survival,normalizedtow.t. 2.5mM H2O2 5mM NAC pretreatment heat shock osmotic shock A ** ** ** * 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
- 25. ROS levels in the mutantscarbonylation increase DHR-123 increase CellROX increase total Fe increase dipyridyl rescue NADPH level increase NADPH rescue fre sufD rseC sodA w.t. clpA recA napF lon ybeQ yaaU cysD ybhJ gpmM icd lpd yidH 0.8 positive control wild-type ROS are typically not increased (except cysD, yaaU, rseC, and the positive control sodA)
- 26. Predicted functional interactions from STRING v9 Gene families whose codon biases are associated to aerobicity/aerotolerance:
- 27. Putative mechanisms of oxidative stress resistance NAD(P)H related 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 carbonylation increase DHR-123 increase CellROX increase totalFe increase dipyridyl rescue NADPHlevel decrease exogenous NADPHrescue
- 28. 0% 20% 40% 60% 80% 100% 120% w.t. yjjB flgH cysG mnmA nlpE proX osmotic oxidative heat C 0% 20% 40% 60% 80% 100% 120% w.t. clpS oppA tig ssuD nudF pnp typA mngR lsrR yebS rhlE yajL pykF dtd eutD gloB yfcA marR yccX pncB ttdB moaA dsbB survival,normalizedtow.t. heat oxidative osmotic B 0x 1x 2x 3x 0% 20% 40% 60% NAC/noNAC survival,normali 2.5mM H2O2 5mM NAC pretreatment heat shock osmotic shock Other phenotypes: thermophilicity, halophilicity Knockout of candidate genes affects heat shock resistance and osmotic shock resistance.
- 29. 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 %survival 0 0.1 0.2 0.3 0.4 0.5 0.6 0 0.5 1 1.5 2 2.5 relativefrequency codon distance(MILC)to ribosomalprotein genes ribosomalproteingenes all otherE. coli genes w.t. 15 20 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 %survival osmoticshock heatshock C D B 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
- 30. 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 – Mediterranean Institute for Life Sciences (MedILS) Split, Croatia. all experimental work shown Nives Škunca ETH Zurich. Phyletic profiling, GORBI
- 32. Thank you! Fran Supek 1) Lehner group, CRG/EMBL Systems Biology Unit, Barcelona 2) Division of Electronics, RBI, Zagreb, Croatia XXI Jornades de Biologia Molecular Barcelona, 11.6.2014 End of Part 1. Part 2 deals with causal synonymous mutations in human cancer genomes, and is available separately.
Editor's Notes
- For envC, we predict ‘peptidoglycan-based cell wall biogenesis’ with Pr of 0.71