G+C content. 1 Introduction. 2 Chromosomes Topology & Counts. 3 Genome size. 4 Replichores and gene orientation. 5 Chirochores.

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1 1 Introduction 2 Chromosomes Topology & Counts 3 Genome size 4 Replichores and gene orientation 5 Chirochores 6 7 Codon usage 121 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

2 Introduction [G+C] Is calculated in percentage of G+C : 100[A+T+C+G] First nucleic acid technology applied to bacterial systematics One of the genomic characteritics recommended for the description of species and genera 5% and 10% are the common range found within a species and a genera, respectively Modulates the aminoacid content of proteins 122 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

3 Introduction DNA double helix Watson and Crick, Nature, Bacterial genome structures

4 Introduction The original figure 124 Bacterial genome structures

5 Introduction is the same for both strands Let ACGT the primary formula on one strand. Then, its complementary strand composition is given by : A t C g G c T a The is not affected : g+c a+c+g+t = c+g t+g+c+a 125 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

6 Introduction DNA denaturation Quizz: How do you know if a sample of DNA is double-stranded or single-stranded? 126 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

7 Introduction Single strand or double strand? 127 Bacterial genome structures

8 Introduction T m is the temperature at midpoint of transition 128 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

9 Introduction T m increases with DNA AT pairs: 2 hydrogen bonds GC pairs: 3 hydrogen bonds 129 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

10 Between species variability What is the distribution of in bacteria? Study this yourself: 1 Extract G+C data from GOLD ( use the goldtable.txt dataset as previously) and study its distribution. 2 Study data from mbailly/amig/data/gctopt.rdata. What is the relationship with optimum growth temperature T opt? With the class of organism? 130 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

11 Between species variability from GOLD 131 Bacterial genome structures

12 Between species variability and T opt 132 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

13 Between species variability and T opt (n = 739) 133 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

14 Between species variability and T opt (n = 739) > shapiro.test(gctopt$topt) Shapiro-Wilk normality test data: gctopt$topt W = , p-value < 2.2e-16 > shapiro.test(gctopt$gc) Shapiro-Wilk normality test data: gctopt$gc W = 0.957, p-value = 6.941e marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

15 Between species variability and T opt (n = 739) 135 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

16 Between species variability and T opt 136 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

17 Between species variability and aerobiosis Study this yourself: Study data from What is the relationship with (an)aerobiosis? How does it compare with the relation to T opt? 137 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

18 Between species variability and aerobiosis 138 Bacterial genome structures

19 Between species variability The distribution of in bacteria Results from your study: 139 Bacterial genome structures

20 Between species variability Underlying mechanism: why no 100% G+C genomes? Symmetric Directional Mutation Pressure : Mutation rate v Noboru Sueoka AT pair GC pair u Sueoka, N. (1962) On the genetic basis of variation and heterogeneity of DNA base composition. Proc. Natl. Acad. Sci. USA, 48: marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

21 Between species variability Underlying mechanism : theory θ(t) = dθ dt ( θ 0 at equilibrium : = v(1 θ) uθ v ) e (u+v)t + v u +v u +v θ = θ(+ ) = v u +v 141 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

22 Between species variability Underlying mechanism : predictions 60 Mutation pressure Negative selection 50 Number of species % u = 3v u = v 3u = v 142 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

23 Between species variability Underlying mechanism : experiment Direct experimental evidence : Cox, E.C., Yanofsky, C. (1967) Altered base ratios in the DNA of an Escherichia coli mutator strain. Proc. Natl. Acad. Sci. USA, 58: Accelerated evolution experiment with a mutator strain: G+C content variation visible at a lab time scale. 143 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

24 and amino-acid content in proteins GC and the genetic code 144 Bacterial genome structures

25 and amino-acid content in proteins and aa content The impact of on the amino-acid composition of proteins was known even before the deciphering of the genetic code : Sueoka, N. (1961) Correlation between base composition of deoxyribonucleic acid and amino acid composition of protein. Proc. Natl. Acad. Sci. USA, 48: Was used as a clue to crack the genetic code (e.g. here Ala codons are expected to be G+C rich, and yes indeed GCN codons are G+C rich!). First evidence that the genetic code is (almost) universal. 145 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

26 and amino-acid content in proteins A null hypothesis for the aa content of proteins CDS are built by random sampling from an urn with a given θ. We assume for the sake of simplicity that C = G = θ 2 and A = T = 1 θ 2. What would be the amino-acid composition of proteins under this simplistic model? 146 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

27 and amino-acid content in proteins A null hypothesis for the aa content of proteins Let X i {A,C,G,T} a random variable for the result of outcome number i. Note P A = P(X i = A), P C = P(X i = C), P G = P(X i = G), P T = P(X i = T) the probabilities for the four bases. We have assumed that P C = P G = θ 2 and P A = P T = 1 θ 2. The probability for codon GAA is for instance : P(GAA) = P(X 1 = G X 2 = A X 3 = A) = P G P A P A In coding sequences there are no stop codons (TAA, TAG or TGA) so that: P(GAA not stop) = P(GAA) P(not stop) = P G P A P A 1 (P T P A P A +P T P A P G +P T P G P A ) 147 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

28 and amino-acid content in proteins A null hypothesis for the aa content of proteins At the amino-acid level, Glu is encoded by GAA or GAG, so that: P(Glu) = P(GAA GAG not stop) = P(GAA not stop)+p(gag not stop) In a similar way, we can deduce of the expected frequencies for all amino-acids under the model. 148 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

29 and amino-acid content in proteins A null hypothesis for the aa content of proteins f(θ) = f(θ) P(θ,aa) = 8 (1 θ) 2 (1+θ) (1 θ) 2 (2 θ) if aa {Ile} (1 θ) 2 if aa {Phe, Lys, Tyr, Asn} 1 θ 2 if aa {Leu} (1 θ) 2 θ if aa {Met} (1 θ)θ if aa {Asp, Glu, His, Gln, Cys} 2(1 θ)θ if aa {Val, Thr} 3(1 θ)θ if aa {Ser} (1 θ)θ 2 if aa {Trp} θ(θ+1) if aa {Arg} 2θ 2 if aa {Gly, Pro, Ala} 149 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

30 and amino-acid content in proteins What is the impact of G+C on the aa content? Study this yourself: > load(" > dim(uco739) [1] > uco739[1:5,1:5] aaa aac aag aat aca ACHROMOBACTER DENITRIFICANS ACHROMOBACTER XYLOSOXIDANS ACIDIANUS AMBIVALENS ACIDITHIOBACILLUS FERROOXIDANS ACINETOBACTER BAUMANNII This is a dataset of codon counts in 739 bacterial species. 150 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

31 and amino-acid content in proteins What is the impact of G+C on the aa content? Compute the from codon counts. From colnames(uco739), make a vector of in each codon: aaa aac aag aat aca acc acg act aga agc agg agt ata atc atg att caa cac cag cat cca ccc ccg cct cga cgc cgg cgt cta ctc ctg ctt gaa gac gag gat gca gcc gcg gct gga ggc ggg ggt gta gtc gtg gtt tac tat tca tcc tcg tct tgc tgg tgt tta ttc ttg ttt There are multiple ways to do that: using strsplit or s2c in library seqinr, using %in% or looping ==, using explicit loops or sapply. 151 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

32 and amino-acid content in proteins What is the impact of G+C on the aa content? Now, thanks to matrix multiplication (%*%), compute the G+C content in percent: [,1] ACHROMOBACTER DENITRIFICANS ACHROMOBACTER XYLOSOXIDANS ACIDIANUS AMBIVALENS ACIDITHIOBACILLUS FERROOXIDANS ACINETOBACTER BAUMANNII ACINETOBACTER CALCOACETICUS Bacterial genome structures

33 and amino-acid content in proteins What is the impact of G+C on the aa content? Compute the amino-acid content from codon counts. From colnames(uco739), make a vector of the corresponding amino-acid: aaa aac aag aat aca acc acg act aga agc "Lys" "Asn" "Lys" "Asn" "Thr" "Thr" "Thr" "Thr" "Arg" "Ser" agg agt ata atc atg att caa cac cag cat "Arg" "Ser" "Ile" "Ile" "Met" "Ile" "Gln" "His" "Gln" "His" cca ccc ccg cct cga cgc cgg cgt cta ctc "Pro" "Pro" "Pro" "Pro" "Arg" "Arg" "Arg" "Arg" "Leu" "Leu" ctg ctt gaa gac gag gat gca gcc gcg gct "Leu" "Leu" "Glu" "Asp" "Glu" "Asp" "Ala" "Ala" "Ala" "Ala" gga ggc ggg ggt gta gtc gtg gtt tac tat "Gly" "Gly" "Gly" "Gly" "Val" "Val" "Val" "Val" "Tyr" "Tyr" tca tcc tcg tct tgc tgg tgt tta ttc ttg "Ser" "Ser" "Ser" "Ser" "Cys" "Trp" "Cys" "Leu" "Phe" "Leu" ttt "Phe" Useful functions are s2c(), translate() and aaa() in the seqinr package. 153 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

34 and amino-acid content in proteins What is the impact of G+C on the aa content? Now, in one line 2, thanks to apply() and tapply(), compute the amino-acid content in each proteome: Ala Arg Asn Asp Cys ACHROMOBACTER DENITRIFICANS ACHROMOBACTER XYLOSOXIDANS ACIDIANUS AMBIVALENS ACIDITHIOBACILLUS FERROOXIDANS ACINETOBACTER BAUMANNII or more if you do not feel that geeky 154 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

35 and amino-acid content in proteins What is the impact of G+C on the aa content? Plot the results : Show the influence of for Ala, Lys, and Glu (at least). Add the linear fit Add the neutral model as a line. 155 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

36 and amino-acid content in proteins What should be obtained for Ala: Ala frequency evolution with G+C Ala content [%] Linear fit Neutral model [%] 156 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

37 and amino-acid content in proteins What should be obtained for Lys: Lys frequency evolution with G+C [%] Lys content [%] Linear fit Neutral model 157 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

38 and amino-acid content in proteins What should be obtained for Glu: Glu frequency evolution with G+C Glu content [%] Linear fit Neutral model [%] 158 marc.bailly-bechet@univ-lyon1.fr Bacterial genome structures

39 and amino-acid content in proteins How to do these graphs? 159 Bacterial genome structures

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